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MathWorks Fellows are pioneering solutions to some of today’s most urgent challenges—both global and national.

From advancing models of cardiac failure to accelerating the path to sustainable fusion energy, from developing responsible applications of generative AI to designing next-generation semiconductor materials for faster, more energy-efficient computing, they are shaping a healthier, more resilient, and more intelligent future.

Explore their biographies:

Frederick Ajisafe
Aeronautics and Astronautics

Frederick Ajisafe is a PhD student in aeronautics and astronautics whose research aims to apply space technologies and systems engineering techniques to address challenges on Earth. As a MathWorks Fellow, he will conduct a project evaluating the benefits of satellite-based Earth observation tools in local methane monitoring, as part of policies to address climate and air quality concerns. The project, centered on the Seropédica landfill in Rio de Janeiro, employs an Environment-Vulnerability-Decision-Technology Framework (EVDT) to draw system-level connections among the natural environment, its consequences for human and socioeconomic vulnerability, the role of human decision-making, and the ability of technology to resolve gaps in a decision-maker’s knowledge of the environment. Frederick will create MATLAB-based models to create simulations and analyses guided by EVDT. Frederick’s work has the potential to offer a novel system engineering framework for holistically analyzing the benefits of sensing technologies applied to methane generation in urban landfills, as well as a valuable example of how interdisciplinary modeling packages can be used to inform real-world policymaking.

Ethan Almquist
Mechanical Engineering

Ethan Almquist is a graduate student in mechanical engineering whose research aims to solve critical challenges in naval ship design and power system diagnostics. Specifically, he is working to create energy-efficient systems that minimize electrical consumption and maximize ship performance. As a MathWorks Fellow, Ethan will develop a behavioral simulation method to permit much faster than real-time emulation of detailed power streams for shipboard power plants. His approach utilizes data from nonintrusive power monitoring systems to model both steady-state and transient events at the power level without the computational demands of traditional numerical simulations. His project will enable rapid, highly detailed studies of shipboard power flow, fuel consumption, and operational capability based on actual or hypothetical mission profiles at low computational cost. Ethan’s work relies significantly on MATLAB and Simulink and demonstrates the power of these tools to solve critical problems in modern ship design. In addition to delivering valuable tools and methods for ship design, Ethan’s fast-solver approach has the potential for broad applications in other domains, including renewable energy and microgrid operation.

Jasmine Jerry Aloor
Aeronautics and Astronautics

Jasmine Jerry Aloor is a PhD candidate in aeronautics and astronautics whose research bridges aerospace engineering, robotics, AI, and control. Supported by her second MathWorks Fellowship, she will pursue studies of system- and fleet-level objectives, including fairness and efficiency for highly decentralized execution settings, such as those enabled by multi-agent reinforcement learning (MARL) methods. Her previous projects include studies examining whether agents can learn to be fair, especially in scenarios where agents must safely navigate to perform a coverage task or get into formation. Jasmine demonstrated that by training agents using min-max fair assignments, agents can learn to balance the tradeoff between efficiency and fairness. The next steps of her research involve determining how to effectively combine the safety guarantees provided by control-theoretic techniques with MARL-based planning methods for aircraft trajectory waypoint generation. MathWorks products, including the Aerospace Blockset Toolbox and Simulink, are essential to her work. Jasmine’s research has the strong potential to advance the field of aerial mobility and to help ensure the efficient, safe, and equitable integration of autonomous aircraft into the National Airspace System.

Bastien F. G. Aymon
Mechanical Engineering

Bastien F. G. Aymon is a PhD candidate in mechanical engineering whose research is located at the intersection of mechanics, soft active materials, and bioadhesives. Specifically, Bastien is working to develop super-biocompatible implants by studying and engineering their mechanics and biology. As a MathWorks Fellow, Bastien will address the challenge of the foreign-body response (FBR), an immune reaction leading to the development of a fibrotic encapsulation around the implant, with the goals of producing guidelines for durable, fibrosis-free implantation and enhancing implant longevity. Bastien’s project leverages mechanical modifications of a previously developed anti-fibrotic adhesive film to study the critical parameters influencing the FBR. His preliminary experiments have shown how changes in immune cell populations correlate with the level of mechanical stresses. Next, he will use single-cell technologies to elucidate the biological pathways associated with the fibrotic state. His work relies heavily on, and has offered valuable enhancements to, MathWorks products. Bastien’s research has strong potential to advance the design of next-generation biocompatible implants and contribute to the nascent field of mechanoimmunology.

Akash Ball
Chemical Engineering

Akash Ball is a PhD candidate in chemical engineering whose research aims to use machine learning and computational chemistry to discover novel materials for applications in water desalination, industrial water treatment, and rare earth element recovery. Specifically, he is working to identify materials with unprecedented water permeation and ion-separation performance and creating models to explain how to optimize the transport properties of porous metal-organic frameworks (MOFs). MOFs are comprised of linkers and inorganic metal nodes; by combining them in novel ways or functionalizing linkers, it is possible to design vast numbers of novel materials. Akash’s current objectives are to uncover the chemical and geometric features governing water and ion transport in MOFs and to create machine-learning models that advance MOF design. MATLAB is a critical tool for his model design and results analysis. Ultimately, Akash hopes to develop predictive structure-property relationships for ion selectivity, enabling the design of MOFs for rare earth ion recovery and desalination/water purification. His research has the potential to help address the global shortage of freshwater reserves, impacting some four billion people.

Arittaya (Tiya) Boonkird
Nuclear Science and Engineering

Arittaya (Tiya) Boonkird is a PhD student in nuclear science and engineering who focuses on the exploration of quantum materials for applications in information and electrical devices. Her prior achievements include developing methodologies for evaluating topological materials, a large class of quantum materials, based on economic and environmental criteria, and identifying the top-200 candidates based on these sustainability considerations, as well as experimental work on the physical properties of two topological materials using X-ray scattering techniques. As a MathWorks Fellow, Tiya will explore promising material candidates for future terahertz devices and back-end-of-line interconnects in transistors. This involves employing first-principal calculations from physical properties, including Berry curvature dipole, and evaluating the electrical conductivity of nanowires. MATLAB facilitates many aspects of her work, including numerical simulations, data processing, and machine-learning implementation. Tiya has made seminal contributions to materials science, machine learning, and sustainability, and her ongoing work has the potential to advance quantum materials and next-generation energy and information applications.

Thomas Butruille
Mechanical Engineering

Thomas Butrille is a PhD candidate in mechanical engineering whose research interests are focused on architected materials and machine learning. Specifically, Thomas studies fabrication processes for complex micro-fabricated structures and how these structures interact with their environments and then develops machine-learning models to predict the behavior of these structures. As a MathWorks Fellowship, Thomas will pursue several research threads, including investigations of ultralight truss-based architected materials fabricated at the microscale using two-photon lithography. A second area of his work is computational and analytical modeling and scanning electron microscopy to study how different truss-based suspended lattices behave under ultra-high strain rate particle impact. Third, Thomas will help explore how Bayesian optimization can improve the training of a convolutional machine-learning model of the stress- strain behavior of hyperelastic triply periodic minimal surface lattices. Thomas’s research, which relies significantly on MATLAB, is offering valuable new insights and machine-learning methods in microengineering and has the potential to advance next-generation micro-fabricated structures in a wide variety of applications.

Myrella Vieira Cabral
Aeronautics and Astronautics

Myrella Vieira Cabral is a PhD candidate in aeronautics and astronautics whose research focuses on aerothermoelastic analysis for hypersonic flows. The external skin of hypersonic vehicles, primarily composed of panels and reinforcement structures, may experience complex flow interactions due to nonlinearities arising from aerodynamics, structure, and high-temperature gradients. Therefore, it is crucial to understand and predict the aeroelastic response to optimize the structural design and to ensure fatigue-life estimation to suppress undesirable aeroelasticity phenomena, such as panel flutter. As a MathWorks Fellow, Myrella will continue to develop a framework in MATLAB capable of predicting the Fluid-Structure Interaction response of panels under hypersonic flow while analyzing the impact of critical physical parameters such as freestream pressure and temperature, angle of attack, and convective heat. Additionally, other flow conditions and nonlinear post-flutter behavior will be analyzed, informing future experiments. Her work holds significant potential to advance the understanding of aerothermoelastic behavior in hypersonic flows and contribute to the design of next-generation hypersonic vehicles.

Honghao Cao
Electrical Engineering and Computer Science

Honghao Cao is a Ph.D. candidate in electrical engineering and computer science whose research focuses on the development and optimization of multiphoton microscopy techniques. His work addresses the challenges of deep-tissue imaging in living organisms for both biological research and clinical diagnostics. As a MathWorks Fellow, Honghao will expand his efforts in designing next-generation biomedical microscopy platforms that overcome current limitations in imaging depth and broaden the scope of applications across academic and industrial domains. Currently, his research focuses on developing ultrafast broadband light sources by precisely controlling nonlinear pulse propagation in multimode optical fibers. This work aims to enhance the flexibility and imaging depth of label-free multiphoton microscopy systems. By integrating advanced external modulation strategies, such as wavefront shaping and controlled perturbation, and leveraging the intrinsic self-organizing properties of nonlinear optical dynamics, he has demonstrated enhancements in multiphoton excitation efficiency and volumetric imaging speed. Honghao’s research has strong potential for non-invasive disease diagnostics by enabling in vivo imaging of human tissues with improved cellular contrast and deeper imaging capabilities.

Rodrigo Cavalcanti Alvarez
Nuclear Science and Engineering

Rodrigo Cavalcanti Alvarez is a PhD student in nuclear science and engineering whose research is focused on thermal sciences and the scientific development of the energy field to advance low-carbon electricity generation. Specifically, Rodrigo investigates the process of boiling heat transfer and critical heat flux (CHF), a critical parameter for design and safety considerations. The broad aim of his work is to characterize the physics of boiling with enhanced efficiency and accuracy through advanced optical diagnostics, for which he has utilized and created numerous MATLAB tools. Supported by a MathWorks Fellowship, Rodrigo is conducting experiments in the fundamental physics of boiling, utilizing two high-speed video cameras and one high-speed infrared camera, alongside sensors and hardware components. This setup records the boiling process occurring on a transparent, nano-engineered heating surface with exceptional detail, offering insights into boiling at an extremely high spatial resolution and frame rate. Rodrigo’s research has the potential to mitigate the uncertainties in CHF predictions for nuclear reactors, potentially enhancing energy production efficiency and addressing the global need for low-carbon electricity production.

Simran Chowdry
Nuclear Science and Engineering

Simran Chowdry is a PhD candidate in nuclear science and engineering who studies magnetic reconnection in astrophysical plasmas. Specifically, she is exploring the impact of radiative cooling processes and instabilities on the energy partition and dynamics of plasmoid instability in magnetic reconnection. Simran’s achievements to date include uncovering new phenomena taking place because of the coupling between two distinct plasma instabilities and the development of a novel algorithm to track plasmoids as they grow and then collapse due to strong radiative cooling. This tracking algorithm revealed that larger plasmoids collapse more slowly than smaller plasmoids, an unexpected result that has expanded our understanding of radiative collapse. Simran has made extensive use of, and built valuable additions to, MathWorks tools, including the Plasmoid Tracker Code and the Fundamental Plasma Toolbox. Her work is yielding valuable insights into the study of magnetic reconnection, a key underlying mechanism in a diverse range of highly dynamic events in our solar system and beyond, from the aurora in near-Earth space to space-weather events like solar flares to jets from supermassive black holes.

Jianqiao Cui
Chemical Engineering

Jianqiao Cui is a PhD candidate in chemical engineering whose research is focused on the field of plant nanobionics, in which nanomaterials are integrated with living plants to impart nonnative functions or enhance nonnative functions. Supported by a MathWorks Fellowship, Jianqiao will pursue her broad interests in nanoparticle-plant interactions, with the goal of furthering the development of nanobionic plants that could replace conventional electronic and plastic devices for environmental sensing and energy storage. Her current projects include investigations of the effects of nanoparticle properties on their distribution in plant cells and plastids through hyperspectral imaging; developing plant-based optical sensors for detecting phytoormones and environmental pollutants; and developing kinetic models in MATLAB to describe the luciferase- luciferin bioluminescent and releasing reactions in a recently developed, light-emitting nanobionic plant. Jianqiao’s research has the potential to harness the valuable functions of plants, such as constant fluidic exchange with the environment and photosynthesis, and advance the creation of zero-carbon, self-powered, and self-regulating nanobiotic plants for a wide variety of applications.

Mary Dahl
Aeronautics and Astronautics

Mary Dahl is a PhD candidate in aeronautics and astronautics whose research is focused on the use of small satellites to further our knowledge of the Earth by using onboard AI to improve data collection. Supported by her second MathWorks Fellowship, Mary seeks to improve the way we measure clouds and aerosol interactions to make more accurate climate models by prioritizing and targeting clouds of particular types. The objectives of Mary’s current project are: to design a look-ahead instrument that can autonomously identify clouds of interest, to develop an algorithm that chooses which clouds to prioritize and designs a path to measure them, and to combine this in a simulation environment to test and verify performance. Her efforts could not only benefit climate research but also help optimize satellite tasking to obtain cloud-free images for security needs when the presence of clouds may be detrimental. Outside of this work, she is contributing to a book on MATLAB applications for small satellites. Her research aims to expand the capabilities of small satellites to support climate modeling and research.

Mohua Das
Mechanical Engineering

Mohua Das is a PhD candidate in mechanical engineering whose research is in the field of non- Newtonian fluid dynamics. As a MathWorks Fellow, she will pursue the following project: a study of strain-controlled, optimally windowed chirp rheometry. The goal of the project is to establish guidelines for selecting appropriate chirp parameters, enabling accurate and efficient capture of the rapidly evolving properties of transient materials. Understanding the microstructural changes that soft materials undergo during synthesis or assembly is crucial for linking these changes to macroscopic behavior and advancing material design. Her other recent work, which extensively utilizes MATLAB, developed a framework to enhance the accuracy of predicting the first normal stress difference using Laun’s empirical formula based on analyses utilizing the time-strain separable Wagner constitutive formulation and the fractional Maxwell liquid model. The knowledge derived from this study could be valuable for technological processes involving viscoelastic fluids. Mohua’s research could offer valuable new insights to advance materials design in many spheres.

Louis DeRidder
Institute for Medical Engineering and Science

Louis DeRidder is a PhD candidate in medical engineering and medical physics whose research aims to develop a closed-loop drug delivery system for chemotherapy drugs. With the support of his second MathWorks Fellowship, Louis will continue an ambitious research program to bring the novel drug-delivery system, Closed-Loop Automated Drug Infusion (CLAUDIA) regulator, to the clinic. CLAUDIA measures the concentration of a drug and then inputs that value into a control algorithm that adjusts the infusion rate of the drug to bring its concentration to the desired level. This system has been shown to control the concentration of 5-fluorouracil in vivo, and Louis is now leading a team to validate CLAUDIA for additional drugs. He is also heading efforts to develop a fully automated version of CLAUDIA for clinical translation and working with oncologist collaborators to design the first-in-human clinical trials. MathWorks tools, including the Control Systems Toolbox and Simulink, have been critical in developing this first-of-its-kind system. Louis’s research holds great promise for reducing toxicity, enhancing the efficacy of many chemotherapy drugs, and improving outcomes for cancer patients.

Sebastian (Sebo) Diaz
Institute for Medical Engineering and Science

Sebastian (Sebo) Diaz is a PhD candidate in medical engineering and medical physics whose research interests are focused on developing innovative techniques for fetal imaging and addressing the challenges of imaging in pregnancy. Specifically, Sebo is working on new motion-compensation techniques and a fetal motion analysis framework, both of which draw extensively on MATLAB. With the support of a MathWorks Fellowship, he will pursue several objectives, including the development of a data-driven deep-learning framework that will ultimately provide motion-compensated, high-quality metabolic spectra for clinical use. He will also utilize sophisticated MRI pulse sequences and deep learning to create labels for various parts of the fetal body, enabling tracking and analysis of fetal movement over time. Finally, he is developing a new technique for examining neurological progression using time-series key point estimates to compare fetal motion at a particular gestational age to motion parameters known to align with healthy progression. Sebo’s research could enable more accurate fetal imaging and more detailed characterizations of neurodevelopmental complications, providing clinicians with important tools and information to treat and care for the growing baby.

Ana Dodik
Electrical Engineering and Computer Science

Ana Dodik is a PhD candidate in electrical engineering and computer science whose research is focused on geometry processing. Supported by a MathWorks Fellowship, Ana will continue her successful work to develop new graphics algorithms for human-centric 3D computing with high levels of interactive control. MATLAB gptoolbox is a cornerstone of Ana’s work. Her achievements include a novel algorithm for the computation of generalized barycentric coordinates, an element in geometry processing widely used in computer animation, and a custom-built neural architecture to approach the computation of skinning weights, a task that determines how high-resolution 3D surfaces move based on motions of a few isolated handles. Her future goals include designing a spatial computing system without cages or skeletons, letting users directly interact with 3D shapes, and utilizing geometric fields for other geometric processing tasks such as texturing or vector field design. Ana’s work offers innovative new tools with the potential to advance work in a broad range of fields and sectors, from computer animation to medical image analysis, and from autonomous driving to manufacturing.

Axel Feldmann
Electrical Engineering and Computer Science

Axel Feldmann is a PhD candidate in electrical engineering and computer science whose research aims to address hardware-level performance bottlenecks in many commonly used numeric algorithms. As a MathWorks Fellow, Axel will expand his successful work creating hardware accelerators specifically designed for sparse linear algebra. Axel has already designed two accelerators—Spatula and Azul—that are over 200× faster than CPUs and GPUs on linear solvers. The efficiency and scalability of these systems enable transformative speedups; a single computer system using Axel’s accelerators and consuming under 2 kilowatts achieves comparable performance to supercomputers with over 100,000 cores consuming megawatts of power. Axel is now generalizing these designs to produce a more flexible and programmable architecture capable of speeding up more than just linear solvers. By deploying this hardware in the MathWorks cloud, millions of MATLAB and Simulink users could have access to the same compute power that today is only available to institutions with supercomputers. Thus, Axel’s work has the potential to democratize high-performance scientific computing and enable revolutionary gains in performance through hardware acceleration.

Marie Floryan
Mechanical Engineering

Marie Floryan is a PhD candidate in mechanical engineering whose research is focused on designing microfluidic platforms to model human organ function. As a MathWorks Fellow, Marie will study the effect of fluid shear stress on tumor cell survival through the various stages of metastasis. She has designed a microphysiological system (MPS) that recapitulates the physical environment of tumor cells in vivo. In her previous work, Marie made significant contributions to the development of a novel microfluidic pump that provides recirculating flow through engineered tissues, which she used to study the role of luminal flow in the long-term culture of self-assembled microvascular networks. She is now using her vascularized system to study the cellular dynamics of metastasis; her experiments are coupled with an in vivo collaborator, creating the opportunity to compare the two and advance MPS as a companion to, and potential replacement for, preclinical animal models. MathWorks products are an essential resource for these projects. Marie’s work could ultimately help to explain why cancers tend to metastasize to specific organs, identify novel therapeutic targets, and advance microfluidics research.

Andres Garcia Coleto
Electrical Engineering and Computer Science

Andres Garcia Coleto is a PhD candidate in electrical engineering and computer science whose research in the field of integrated silicon photonics aims to deliver new applications in virtual reality, 3D printing, and medical tools. Specifically, Andres is working on novel integrated visible- light modulators based on liquid-crystal material. As a MathWorks Fellow, Andres will pursue several aligned projects, including the development of a novel integrated-photonics-based augmented-reality display consisting of a single transparent chip that displays 3D holographic images that only the user can see. Andres is also at work on the first mechanically flexible wafer- scale integrated-photonics platform, which would enable wearable healthcare monitors that conform to the body. Finally, he is developing a silicon-photonics-enabled chip-based 3D printer consisting of a single millimeter-scale photonic chip. MATLAB has played a key role in Andres’s work, from device simulation and instrument control to experimental data acquisition and analysis. His research has the potential to yield significant contributions in integrated silicon photonics and drive new solutions in augmented reality, 3D printers, healthcare applications, and other cutting- edge technologies.

Andres Garcia Jimenez
Aeronautics and Astronautics

Andres Garcia Jimenez is a PhD candidate in aeronautics and astronautics whose research interests lie at the intersection of materials science, mechanics, and aerospace engineering. Specifically, Andres is working to elucidate the mechanisms that drive frictional ignition of engineering alloys in high-pressure oxygen environments, such as those encountered in next- generation staged combustion rocket engines. These engines offer propellant efficiency without sacrificing thrust capability but pose challenges such as frictional heating due to the rubbing of rotating components. Andres’s work examines the causes of frictional ignition through experiments, contact mechanics theory, and metallurgical thermochemistry. MATLAB has been an essential tool for Andres in building multi-physics models and analyzing experimental data. Recently, Andres identified a specific material, Inconel MA754, as uniquely ignition-resistant, and is working to refine the processing and performance of this material. Engine designers at Blue Origin and SpaceX are now using Andres’s work to guide their materials selection and engine design, and his research is paving the way toward advanced fail-safe rocket engines and new alloy compositions resistant to frictional ignition under extreme operating conditions.

Aaron Garrison
Chemical Engineering

Aaron Garrison is a PhD student in chemical engineering whose research seeks to develop the fundamental theory enabling predictive simulations for high-throughput discovery of materials. Specifically, Aaron’s research employs data science and machine learning-assisted computational chemistry to more efficiently explore vast numbers of chemical candidates. His work is focused on density functional theory, a powerful technique in computational chemistry for the prediction of material properties, which are invaluable for the discovery of novel catalysts and materials. Supported by a MathWorks Fellowship, Aaron will attempt to refine an existing density functional approximation recommender to generalize across a wider chemical space through the generation of more data and explore a variety of machine learning architectures and representations to capture the relevant features that influence the properties of various materials. MATLAB enables him to analyze and visualize data in customizable, easy-to-use formats that can be shared with other researchers and used collaboratively. Aaron’s work to develop new frameworks integrating first-principles modeling with cutting-edge machine-learning techniques could lead to new discovery processes enabling the exploration of millions or even billions of material candidates.

Samuel (Sam) Dutra Gollob
Mechanical Engineering

Sam Dutra Gollob is a PhD candidate in mechanical engineering whose research centers on soft robotics and actuation system design. He has made important contributions to the model-informed design of novel, vacuum-powered soft actuators for medical applications, as well as the design of fuel-based power systems for pneumatics. With the support of a second MathWorks Fellowship, he aims to develop a generalized model of the multi-physics relationships between pneumatic actuators, pneumatic control, fuel chemical reactions, and heat transfer. This framework could help in the design of compact and powerful pneumatically actuated systems powered by fuels. The technology has broad applications, from an artificial ventricle for patients with single ventricle physiology and other medical devices to untethered soft robots and exoskeletons. Sam’s work addresses a critical gap where traditional machine design frameworks cannot be applied due to the nonlinearities in soft materials and highly coupled design parameters in soft machine design. The innovative MATLAB-based models and tools that Sam is creating have the potential to shape and accelerate soft machine design and the fields of soft robotics and medical robotics.

Chenghao Guo
Electrical Engineering and Computer Science

Chenghao Guo is a PhD candidate in electrical engineering and computer science whose research is in combinatorial statistics. As a MathWorks Fellow, Chenghao will expand his work to address challenges at the interface of computation and statistics on networks. His current work includes an exploration of graphs and hypergraphs, which are fundamental structures in numerous fields. Hypergraphs generalize graphs, with hyperedges consisting of subsets of the vertices; Chenghao is working to determine the conditions under which the projection of a hypergraph will result in information loss and, conversely, when a hypergraph can be recovered from its projection. His result identifies the precise density of a random 3-regular hypergraph for which sparser hypergraphs can be recovered from their projections and for which denser hypergraphs cannot, thus demonstrating that algorithms with provable guarantees for the simplest independent noise case can be used in much broader contexts via algorithmic pre-processing. Like many of Chenghao’s past and current projects, this research pushes the boundaries of combinatorial statistics and utilizes MATLAB’s power in theoretical mathematics to expand this new domain.

Dat Quok Ha
Civil and Environmental Engineering

Dat Quok Ha is a PhD candidate in civil and environmental engineering whose research focuses on topology optimization and increasing access to generative design tools through frameworks that actively involve humans. Dat uses a combination of computational mechanics, optimization, and AI tools in his work and has exclusively relied on MATLAB. Supported by his second MathWorks Fellowship, Dat will continue his research with extensions to his Human-Informed Topology Optimization (HiTop) framework. Users reveal their preferences on a final structural design as they interact with HiTop. This information can be surveyed, collected, and studied via machine learning to recommend the most optimal design strategy. Topology optimization offers an exciting opportunity to design structures with complex geometries that can be created with rapidly advancing digital manufacturing technologies. In addition, topology optimization could contribute significantly to sustainability by reducing waste in construction materials while achieving superior mechanical performance and aiding the discovery and manufacturing of new materials. Dat’s research has strong potential to leverage topology optimization in novel ways to create powerful design tools for diverse users and applications.

Juyeop Han
Mechanical Engineering

Juyeop Han is a PhD candidate in mechanical engineering whose research interests lie at the intersection of uncertainty quantification and machine learning. Specifically, he is interested in how quantifying uncertainty in learning models can aid in the development of safer, more efficient robotics systems. As a MathWorks Fellow, Juyeop will work on methods for uncertainty quantification (UQ) in neural networks and their integration into robotics applications. To that end, he has developed a novel visual-inertial odometry framework that incorporates a convolutional neural network for pose estimation, resulting in greater efficiency and accuracy compared to cases without UQ. His next steps include theoretically developing evidential learning methods for joint tasks of regression and classification, along with applying these methods to perception- aware control and planning. MathWorks has significantly facilitated his research, and he has contributed toolboxes for deep learning with UQ and for perception, planning, and control with uncertainty. Juyeop’s research has the potential to advance the field of robotics by leveraging evidential learning to enhance robot perception, control, and planning, thereby creating a safe and efficient software structure for robotics.

Zhiping He
Electrical Engineering and Computer Science

Zhiping He is a PhD candidate in electrical engineering and computer science. With the support of a MathWorks Fellowship, Zhiping will address the challenges in spintronics with numerical and theoretical modeling methods. His recent and current projects include: establishing the theoretical foundation for magnetic dynamics of a noncollinear antiferromagnet, a highly promising material in computer memory and logic applications; experimental investigations of the transport property of magnetic tunnel junctions made from a topological semimetal, the basic building block for high- performance spintronic devices; and work toward a hybrid system composed of quantum spin, microwave cavity, and ferromagnetic spin wave, to reach coherent dynamic control. In this third project, Zhiping is implementing the innovative idea of using a microwave cavity as a mediator to enhance the coupling strength between quantum spins and ferromagnetic magnons. MathWorks tools are a central component of his research, and he has contributed numerous open-source resources to the microelectronics community. Zhiping’s work has strong potential to help realize the potential of spintronics and open new avenues for enhanced performance and efficiency in memory and computing devices.

Erik Helstrom
Civil and Environmental Engineering

Erik Helstrom is a PhD candidate in civil and environmental engineering whose research explores the atmospheric oxidation of organic compounds with a focus on the formation of fine particulate matter (PM). Supported by a MathWorks Fellowship, Erik will pursue several innovative projects in instrument development and laboratory oxidation, including the development of a total suspended organic carbon instrument for laboratory use; comprehensive comparison of different mass spectrometric measurements of PM composition; measurement of various reactive nitrogen species; and use of a series of explicit chemical model simulations and laboratory experiments to survey the relationship between peroxy radical (RO2) chemistry and the properties of organic PM. Erik’s research, which draws on a number of MathWorks tools, such as the Framework for 0-D Atmospheric Modeling, provides a suite of novel analytical techniques that are advancing the study of atmospheric chemical mechanisms. The use of these tools in laboratory experiments could develop new insights into the evolution of reactive organic carbon, in particular, the formation and transformation of PM in differing atmospheric conditions.

Wenyuan (Roger) Hou
Aeronautics and Astronautics

Wenyuan (Roger) Hou is a PhD candidate in aeronautics and astronautics whose research interests are focused on design and structures for emerging propulsion applications. Roger has used MATLAB to model structural evolution processes during melt-based additive manufacturing of oxide dispersion-strengthened superalloys, a novel class of high-temperature materials with potential applications in reusable staged-combustion rocket engines. Supported by his second MathWorks Fellowship, he will build on these insights to explore how these alloys can enable novel turbine architectures that mitigate thermal fatigue. Roger’s work will integrate computationally efficient, reduced-order models of fatigue life with classical bounding theorems to rapidly sample the turbine design space and determine turbine designs that develop favorable residual stress profiles that suppress thermal fatigue. His research has strong potential to further our understanding of the structural evolution processes that govern the properties and performance of additively manufactured materials and, ultimately, to deliver novel turbine designs for reusable heavy-lift launch vehicles, unlocking next-generation space economics and space accessibility.

Yi-Hsuan (Nemo) Hsiao
Electrical Engineering and Computer Science

Yi-Hsuan (Nemo) Hsiao is a PhD candidate in electrical engineering and computer science who is developing bumblebee-inspired aerial robots powered by artificial soft muscles, with the goal of achieving resilient robotic flight in cluttered environments. Nemo’s second MathWorks Fellowship will allow him to extend his testing and analysis of a 750 mg robot. During the past year, Nemo has worked extensively with engineers from Speedgoat and MathWorks to solve challenges related to on-board software, and ultimately resolved those challenges, adding many hardware safety designs in the process. After these upgrades, Nemo realized transformative results in the flight arena, successfully demonstrating a 1,000-second flight in his 750 mg robot, a result that is nearly two orders of magnitude longer than most existing sub-gram robots. He has also demonstrated double somersaults with a body angular velocity of 7,000 degrees per second, which is four times faster than the best quadrotor and two times faster than fruit flies. Nemo’s research continues to offer tools and approaches that significantly benefit the MathWorks community and is advancing the next generation of nature-inspired micro-robots.

Riley J. Hultquist
Nuclear Science and Engineering

Riley J. Hultquist is a PhD candidate in nuclear science and engineering whose research investigates structural materials for next-generation nuclear reactors. Using advanced synchrotron-based techniques combined with multiscale calculations, Riley seeks to gain an atomic-level understanding of defect behavior and thermophysical properties of specific materials. As a MathWorks Fellow, he will investigate strain in nanocrystals using diffraction patterns. The manufactured 3D crystal starts as several hundred 2D grayscale images that encode information about the crystal’s structure. Computational algorithms are employed in MATLAB to reconstruct the crystal from these data and study the strained substructures. Riley has used X-ray diffraction experiments to explore the inner workings of the atoms in the crystal lattice and is now studying how lattices respond to irradiation. Using MATLAB, Riley is investigating how crystals form faceted surfaces, and how some of these surfaces are better fit to withstand the damage of bombardment with energetic particles. Riley’s research is delivering key insights into the material properties of nanocrystals and could have important implications for next-generation nuclear reactors, fuel design, and sustainable energy.

Mahmudul (Ridul) Islam
Materials Science and Engineering

Mahmudul (Ridul) Islam is a PhD candidate in materials science and engineering whose research aims to improve understanding of the properties and structure of metal alloys. Specifically, Ridul is examining the chemical short-range order of high-entropy alloys (HEA) to better understand and harness valuable characteristics of HEAs, such as mechanical strength and hardness, wear and corrosion resistance, and radiation damage resistance. His current work involves large-scale atomistic simulations using state-of-the-art machine-learning potentials to investigate chemical mixing during the plastic deformation of HEAs. He has also developed a physics-based model, assisted by machine learning and information theory techniques, to interpret the simulation results. This work has yielded the first observation of nonequilibrium steady states of chemical order during plastic deformation of metals, an insight Ridul plans to apply in modeling various additive manufacturing approaches. Ridul’s research in metallic alloys offers potential benefits in a wide range of industries, research disciplines, and spheres of technology development, from construction and automotive applications to aerospace, electronics, and energy production.

Hannah Jackson
Institute for Medical Engineering and Science

Hannah Jackson is a PhD candidate in medical engineering and medical physics whose research is focused on improved treatment approaches for drug-resistant epilepsy using a recently developed implantable device to deliver micro-doses of drugs to small regions of the brain. Supported by her second MathWorks Fellowship, Hannah will continue to hone her novel device and improve its clinical translation. Her immediate objective is to optimize seizure detection algorithms to ensure compatibility across all EEG recording setups and to support the collaboration of researchers across institutions and experimental set-ups. She is also exploring the possibility of combining EEG monitoring with the drug delivery system to create a closed-loop system that can detect seizure activity and deliver drugs on an as-needed basis. To pursue this goal, Hannah will explore machine learning and AI techniques for pattern recognition. MATLAB and associated computational tools will continue to be indispensable assets in her work. Hannah’s research has great potential to expand our understanding of epilepsy, and to advance new treatment approaches that will improve patients’ lives.

Hyeonseok Kim
Mechanical Engineering

Hyeonseok Kim is a PhD candidate whose research interests are focused on in-space manufacturing of nanosatellite (CubeSat) hardware, with the goal of delivering innovative, lower-cost technologies to democratize access to space. Specifically, he is addressing the challenge that a majority of CubeSats have no means of propulsion, which greatly limits their capabilities. Supported by a MathWorks Fellowship, Hyeonseok will expand his work on 3D-printed electrospray thrusters for CubeSats that are compatible with in-space manufacturing and could be the first of their kind shown to work uniformly. These space engines harness electrohydrodynamic phenomena to eject propellant droplets at a high speed, producing thrust. A key element of his project is the development of novel microfluidics for uniform flow distribution, delivering uniform flow across all emitters against fabrication errors and dynamic outlet conditions caused by non-uniform electric fields and interfacial effects. MATLAB is essential to his design, optimization, analysis, and data visualization. Hyeonseok’s work has the potential to offer key insights into the basic physics of electrospray propulsion and unlock significant new potential for CubeSats in space exploration and research.

Evan King
Civil and Environmental Engineering

Evan King is a PhD student in civil and environmental engineering whose research is focused on quantifying how mountainous watersheds control the supply of water and its quality to dependent downstream communities. Specifically, Evan investigates how landscape properties, such as topography, geology, and vegetation type, influence key hydrologic-biogeochemical processes. Evan’s current work, centered on the Sierra Nevada and the Rocky Mountains, explores how subsurface structure combines with variable climate forcing to determine the distribution of water available for plant communities and streamflow levels. To conduct this analysis, she uses a combination of high-resolution remote sensing and geospatial datasets, along with field-based observations, and has used, developed, and shared numerous hydrology-focused MATLAB codes in the course of her work. Evan’s ultimate goal is to establish robust, transferable relationships between vegetation dynamics, energy, and water availability on the watershed scale. Her research has the potential to advance our understanding of mountainous watershed behaviors and enable the prediction of large-scale hydrological responses of vegetation communities in the face of a warming climate.

Pip Knight
Materials Science and Engineering

Pip Knight is a PhD candidate in materials science and engineering who seeks a deeper understanding of nanoscale kinetics to further the design of nanomaterials. Specifically, she is studying the growth and epitaxy of 3D metals and semiconductors on 2D materials. As a MathWorks Fellow, Pip will investigate titanium silicides and titanium oxides, which can catalyze the splitting of water into hydrogen and oxygen using energy from the sun, a clean way to produce hydrogen without fossil fuels. The addition of 2D materials enhances efficiency due to their light absorption properties and their ability to slow electron-hole recombination in the device. Pip is recording in situ videos of how titanium reacts with a silicon-containing gas (disilane) to form titanium silicides; how titanium reacts with oxygen; and how titanium influences the reactions of gold with silicon to grow silicon nanostructures with novel shapes for plasmonic optoelectronic devices. Pip’s research, which relies significantly on MATLAB, has yielded valuable insights into nanoscale kinetics and has the potential to advance nanomaterial design through in situ microscopy for a variety of applications, including clean energy.

Kota Kondo
Aeronautics and Astronautics

Kota Kondo is a PhD candidate in aeronautics and astronautics whose research is focused on autonomous multi-agent trajectory planning for unmanned aerial vehicles (UAVs). As a MathWorks Fellow, Kota will expand his innovative work to develop planners capable of supporting complex missions that demand intricate coordination between multiple agents. His achievements include major contributions to RMADER, a multi-agent trajectory planner that generates collision-free trajectories even when communications between agents are delayed. Kota was also centrally involved in the creation of PUMA, a multi-agent trajectory planner that addresses uncertainties around known obstacles and their directions of motion. In addition, he has contributed to REAL, an adaptive model leveraging large language models for UAV control; to SOS-Match, a system for detecting and matching objects in unstructured environments; and to an innovative approach leveraging diffusion models for constraint-satisfied trajectory planning for UAVs. MATLAB plays a crucial role in all of these research threads. Kota’s pathbreaking work has the potential to advance safe and reliable UAV technologies for a wide variety of applications, from package delivery to surveillance and rescue.

Jimin Kwag
Chemical Engineering

Jimin Kwag is a PhD candidate in chemical engineering whose research is focused on the self- assembly of colloidal semiconductor nanocrystals (NCs). As a MathWorks Fellow, Jimin will explore the fundamental principles of self-assembly in lead sulfide (PbS) NCs, which exhibit appealing properties such as a direct optical band gap, multiple exciton generation, and infrared absorption. By manipulating the NC size, core shape, and organic ligand chemistry, it is possible to tune their chemical, optical, and electronic properties. Jimin is particularly interested in the role of organic ligands in the self-assembly process of PbS NCs and in elucidating the structure of the organic ligand shell. To that end, she is applying neutron and x-ray scattering to characterize the structure of inorganic NC cores and small-angle neutron scattering to characterize the role of the organic ligand shell. MATLAB plays a pivotal role in processing and interpreting her experimental results. Jimin’s research is shedding new light on NC self-assembly and paving the way for new applications in photovoltaics and optoelectronics in a variety of fields, including imaging, solar energy harvesting, displays, and communications.

Regina Lee
Mechanical Engineering

Regina Lee is a PhD candidate in mechanical engineering whose research at MIT’s Laser Interferometer Gravitational-Wave Observatory (LIGO) Lab aims to measure extremely small space-time vibrations from gravitational waves from sources such as black hole mergers. As a MathWorks Fellow, Regina will focus on modeling, construction, and testing of suspension prototypes with the aim of designing the next generation of LIGO suspensions. A key aspect of her work is creating accurate analytical models that allow for an intuitive understanding of suspension redesigns. Her new MATLAB-based model includes the dynamics of all six degrees of freedom for each of the four suspension masses, allows for quick alterations to the suspension geometry, and is entirely in state space form, which enables extremely accurate calculations of the cavity dynamics, including the effects of laser power. Regina’s pioneering work is part of LIGO’s collaborative efforts with dozens of universities around the world. Her research sets the stage for the design of an even more complex suspension system envisioned for Cosmic Explorer, the next leap in gravitational-wave detection, which aims to observe gravitational waves across the entire universe.

Kunzan Liu
Electrical Engineering and Computer Science

Kunzan Liu is a PhD candidate in electrical engineering and computer science whose research aims to advance metabolic and structural imaging of living tissues and next-generation imaging tools in biomedicine. As a MathWorks Fellow, Kunzan will pursue a project in 3D optical microscopy aimed at capturing the cellular metabolic dynamics deep within intact and living biological systems. His deep-tissue label-free 3D imaging platform enables precise characterization of metabolic and structural changes in living engineered human multicellular microtissues and significantly outperforms existing techniques. Its modular design (multimode fiber with a slip-on fiber shaper) is anticipated to allow wide adoption of this methodology for demanding in vivo and in vitro imaging applications, including cancer research, autoimmune diseases, and tissue engineering. MATLAB plays a central role in Kunzan’s work, supporting simulation, signal processing, and machine learning, and he is developing open-source tools of value to researchers in imaging, sensing, optics, and optimization. Kunzan’s work has strong potential to advance a plethora of new imaging applications, from health monitoring and pharmaceutical research to disease diagnosis and food sensing.

Evan Massaro
Mechanical Engineering

Evan Massaro is a PhD candidate in mechanical engineering whose research focuses on finding fast and efficient Monte Carlo algorithms to model and simulate rarefied gas dynamics. As a MathWorks Fellow, Evan will work to validate and characterize a new model that addresses specific drawbacks of the direct simulation Monte Carlo (DSMC) algorithm. DSMC is the current state-of-the-art and runs on US Department of Energy supercomputers to model atmospheric entry of hypersonic flights; however, when the simulated gas density spans many orders of magnitude, DSMC becomes statistically inefficient, requiring a prohibitive amount of computational resources. Through computational experiments in systems with large density gradients, Evan discovered an efficient and accurate Monte Carlo algorithm based on importance sampling, which led to a 10 to 100 times reduction in computational resources compared to DSMC. MATLAB has been an instrumental resource in his work. Evan’s research has yielded an innovative synthesis of disparate computational methods to solve a class of problems that have challenged researchers for decades. His work has great potential to deliver innovative solutions to real-world problems through computational physics.

Leticia Mattos Da Silva
Electrical Engineering and Computer Science

Leticia Mattos Da Silva is a PhD candidate in electrical engineering and computer science whose research seeks to advance applications of nonlinear partial differential equations (PDE) in the fields of geometry processing and computer graphics. With the support of her second MathWorks Fellowship, Leticia will explore the use of stochastic differential equations (SDE) associated with a parabolic PDE for an image task application. A second avenue for future work in Leticia’s PhD involves developing a new numerical approach to solving a PDE known as the Landau equation. All of Leticia’s work has been implemented in MATLAB, and she is making significant contributions to the constellation of MathWorks tools through her research. Her plans include developing new techniques for a broader class of nonlinear PDE and investigating applications for their corresponding SDE. Her work has the potential to advance far-reaching applications, including a numerical scheme for the Landau equation to model particle collisions in plasma simulations, and a new algorithm for image generation.

Carissma McGee
Aeronautics and Astronautics

Carissma McGee is a PhD candidate in aeronautics and astronautics whose research is focused on high-contrast imaging and exoplanet characterization. As a MathWorks Fellow, Carissma will pursue several goals connected to the Nancy Grace Roman Space Telescope. The goal of Carissma’s work, which draws significantly on MATLAB, is to ensure precise mass measurements and optimize observational strategies for the mission using a combination of advanced optical systems, deformable mirrors, and sophisticated control algorithms. By modeling the behavior of light as it passes through the telescope’s optics, interacts with the deformable mirrors, and reaches the science camera, Carissma will ensure that the Roman Space Telescope achieves and maintains the desired high-contrast conditions in the dark zone. Her work is yielding new MATLAB models and algorithms for high-contrast imaging and wavefront control of value to other researchers. Carissma’s research and critical support of the Roman Space Telescope mission have the potential to offer critical new insights into the formation and evolution of planetary systems.

Adriana Mitchell
Aeronautics and Astronautics

Adriana Mitchell is a PhD candidate in aeronautics and astronautics whose research is centered on autonomous visual navigation for spacecraft. Visual navigation is a powerful tool for planetary landings but is restricted in its application due to its susceptibility to variations in illumination conditions. To address this challenge, Adriana is developing algorithms for robust navigation under variable illumination conditions, focusing specifically on terrain-relative navigation during planetary entry, descent, and landing. Supported by her second MathWorks Fellowship, Adriana is collaborating with NASA’s Jet Propulsion Laboratory to apply her algorithms to Lunar and Martian image datasets. Her approach involves matching terrain features that remain constant throughout the day, thereby enabling the real-time descent imagery to be localized to an a-priori map with different lighting. Consequently, image alignment can be maintained, ensuring safe and precise spacecraft landings. MathWorks products, including MATLAB’s Image Processing Toolbox, have been pivotal in her work. By offering new approaches for safe and precise spacecraft landings on planetary bodies, Adriana’s research has strong potential to advance aerospace engineering and space exploration.

Bria Morse
Aeronautics and Astronautics

Bria Morse is a PhD candidate in aeronautics and astronautics whose research aims to enhance knowledge of the impacts of patient transport via ground and air on human physiology. Increased use of unmanned or remotely operated vehicles for casualty transport from combat zones and disaster areas is anticipated, yet the impacts of such transport on the body are not well understood. As a MathWorks Fellow, Bria will study the impact of transport-related stressors, such as acceleration force and vibration, on a hemorrhaging patient. Her project has two primary aims: 1) to isolate key environmental and physiological parameters for influencing decision-making in transport environments, and 2) to determine the dose-response effect of patient severity and key transport parameters on patient stability using a novel lower-body negative pressure chamber and custom vacuum to simulate hemorrhage in a dynamic transport environment. MATLAB and Simulink will be important resources for data visualization and analysis. Bria’s research has the potential to provide a fuller understanding of the impacts of casualty transport via unmanned or remotely operated vehicles to improve patient outcomes and save lives.

Abhishek Mukherjee
Electrical Engineering and Computer Science

Abhishek Mukherjee is a PhD candidate in electrical engineering and computer science. His research aims to broaden the understanding of strain engineering in 2D materials to develop hyperspectral IR photodetectors that leverage the flexoelectric effect. Abhishek is investigating the effect of engineered in situ strain on semiconductors to break inversion symmetry and enhance the photogalvanic effect (PGE) in novel material candidates. PGE enables light-to-current efficiency exceeding the Shockley-Queisser limit, offering exciting opportunities for energy harvesting, photodetection, optical rectification, spintronics, imaging, and LIDAR technology. Utilizing MATLAB’s Deep Learning Toolbox, Abhishek plans to create a portrait of an ideal PGE material candidate, using data from materials experimentally shown to exhibit high PGE and identifying patterns in optoelectronic properties that manifest in the form of a high flexoelectric coefficient. Incorporating machine learning will enable the rapid evaluation of hundreds of materials and help unlock new functionalities in photonic devices. Abhishek’s research has the potential to advance fundamental knowledge in his field and pave the way for revolutionary photonic device design in many spheres, including night vision, celestial navigation, optical computing, and renewable energy.

Simo Pajovic
Mechanical Engineering

Simo Pajovic is a PhD candidate in mechanical engineering whose research addresses challenges in nanophotonics and light-matter interactions, with critical applications in energy efficiency and medical imaging. As a MathWorks Fellow, he will focus on understanding electromagnetic nonreciprocity in the context of radiative heat transfer, which may unlock exciting opportunities in engineering emitters and absorbers violating Kirchhoff’s law of thermal radiation, in addition to unidirectional waveguides and optical isolators and circulators in the infrared spectrum. His recent projects have included the development of theory and MATLAB-enabled numerical methods to utilize magneto-optical nonreciprocity for control of thermal radiation in both the macro- and microscopic regimes and engineering radiative heat transfer using nanophotonics to improve thermal management in X-ray tubes. By expanding our understanding of nanophotonics and applying those discoveries to conceptualize new, light-based energy conversion devices, Simo’s work could advance technology in many spheres, from the thermal management of electronics, medical equipment, and buildings, to radiative energy harvesting and conversion.

Gyutae (Jack) Park
Nuclear Science and Engineering

Gyutae (Jack) Park is a PhD candidate in nuclear science and engineering whose research is focused on the design and optimization of microreactors, also known as nuclear batteries (NB), a potentially critical source of carbon-free energy that is smaller than a traditional reactor. Specifically, Jack is pursuing the design optimization of two NB design concepts: a sodium-cooled, graphite-moderated, thermal-spectrum microreactor and an organic-cooled (hydrocarbons), water-moderated thermal-spectrum microreactor. Jack is considering the factors of core neutronics and thermal-hydraulics safety, balance of plant design, and plant cost. In particular, he hopes to identify the sensitivity of each design parameter to determine which inputs play a key role in determining each reactor’s figures of merits, such as cycle length, operating power, cost, and electricity generation. MATLAB has been one of the primary coding platforms for Jack’s project. His research has the potential to advance microreactors, which represent a renewable energy source with a wide range of applications, including industrial heat and/or electricity sources, disaster relief, remote defense installations, maritime and space propulsion, and surface power for moon and planetary exploration.

Sanghyun Park
Mechanical Engineering

Sanghyun Park is a PhD candidate in mechanical engineering whose research focuses on developing long-acting implantable systems to enhance patient acceptability and adherence to treatment, particularly addressing preventive healthcare for HIV, tuberculosis, malaria, and contraception for family planning. As a MathWorks Fellow, he is developing biodegradable osmotic pumps that have demonstrated the ability to provide constant drug release through osmosis- driven mechanical infusion and become absorbed into the body at the end, eliminating the need for invasive removal surgery. In addition, he is working on transformative injectable formulations leveraging the self-assembling mechanism of drug crystals upon injection. Utilizing AI and advanced computational methods, he is expanding the applicability of the platform technologies to different areas of therapeutics by overcoming the conventional trial-and-error approach for formulation screening and analyzing preclinical data more efficiently. MATLAB plays a central role in his work, and he is sharing tools that will foster innovation in the MathWorks community. Sanghyun’s work has strong potential to offer pioneering advancements at the intersection of biomedical engineering and AI, addressing various health and clinical issues.

Daniel Pfrommer
Electrical Engineering and Computer Science

Daniel Pfrommer is a PhD candidate in electrical engineering and computer science whose research seeks to advance the theoretical analysis and algorithmic development of high- dimensional control systems. As a MathWorks Fellow, Daniel will focus on the approximation of classical control algorithms by neural network-based models. His current work aims to develop new methods for learning latent state representations in high-dimensional, partially observed dynamical systems, performing filtering over the learned representation, and finally, developing new algorithms for learning control policies based on these methods. Daniel’s broader goal is to create a general filtering algorithm capable of performing inference over learned high-dimensional state representations directly from video data. This could yield a tractable approach for mining an entire “world model” from large quantities of unsupervised video data in a scalable manner. His work, which draws heavily on MathWorks products, has implications for the field of machine learning, enabling more efficient compression algorithms, new algorithms for AI-based content editing, and better model-based reinforcement learning algorithms. These capabilities could advance work in many domains, from the deployment of robotic systems to economic and social policy decision-making.

Randall A. Pietersen
Civil and Environmental Engineering

Randall A. Pietersen is a PhD candidate in civil and environmental engineering whose research focuses on creating new deep-learning tools and hyperspectral imaging analysis techniques for USAF airfield damage assessment and the detection of unexploded explosives (UXO). A previous MathWorks Fellowship enabled Randall to validate the principles of his automated sensor calibration methodology and evaluate the process; a second MathWorks Fellowship will support his ongoing efforts to develop synthetic hyperspectral data generation pipelines in support of new spatiospectral machine-learning models that use reflectance-corrected spectral data to detect UXO. He has contributed to numerous MATLAB toolkits, including Deep Learning and Hyperspectral Data Processing. Randall’s research directly addresses the need for improved methods for predictive performance when fielding machine-learning models exposed to limited training data. His work has the potential to advance a broad range of applications of near-surface hyperspectral imaging, from military contexts to mining, agriculture, disaster response, and commercial infrastructure management.

Diego Quevedo-Moreno
Mechanical Engineering

Diego Quevedo-Moreno is a PhD candidate in mechanical engineering whose research is focused on the development of implantable, biomimetic, medical devices that augment or assist native function. With the support of a MathWorks Fellowship, Diego will work on the design and testing of an implantable ventilator to treat respiratory failure. For patients with severe diaphragm dysfunction who are not candidates for minimally invasive treatments, the current alternative is permanent airway tethering to a mechanical ventilator via tracheostomy, which can severely compromise autonomy and quality of life. To address this, Diego is creating an implantable soft robotic ventilator capable of restoring respiratory function. He has successfully demonstrated the ability of the device to assist in respiration and is now creating a MATLAB-based computational model and a physiologically relevant benchtop platform to fully simulate the respiratory biomechanics of the rib cage and diaphragm. MathWorks tools and products have been critical in Diego’s research. His work has exciting potential to create an innovative therapeutic alternative for artificial ventilation, ultimately improving patient quality of life and advancing the field of medical robotics.

Kristen Riedinger
Civil and Environmental Engineering

Kristen Riedinger is a PhD candidate in civil and environmental engineering whose research focuses on environmental micropollutants. Specifically, Kristen investigates drinking water pollution by N-nitrosamines, a family of organic compounds that are ubiquitous in the environment and potentially harmful to human health, with a particular focus on N-nitrosodimethylamine (NDMA). As a MathWorks Fellow, Kristen will pursue two primary objectives: to study the distribution of NDMA within treated water networks and to develop a quantitative understanding of NDMA formation within these systems. Currently, she is investigating NDMA in the public water systems of two communities with historical water quality issues through sample collection and analysis. She further plans to collect samples from sites across the country. Kristen has used MATLAB to manipulate a large EPA dataset representing 1,198 public water systems with the ultimate goal of developing an empirical model to predict NDMA occurrence in treated drinking water systems. The insights emerging from Kristen’s research have the potential to contribute critical knowledge to assess and mitigate water contamination and protect communities from potential adverse health effects from their environment.

Mumin Jin Sass
Electrical Engineering and Computer Science

Mumin Jin Sass is a PhD candidate in electrical engineering and computer science whose research integrates signal processing, machine learning, and array processing to expand the capabilities of sensing systems for radio frequency, acoustic, and other modalities. Supported by her first MathWorks Fellowship, Mumin concentrated on machine-learning approaches to enhance automotive radar imaging in autonomous vehicles. Her second MathWorks Fellowship will enable her to explore a related line of inquiry at the intersection of signal processing and systems and circuits: rethinking analog-to-digital conversion. She aims to create application-specific data converter architectures that leverage the availability of abundant and inexpensive digital signal processing to dramatically reduce the number of bits required to achieve target performance levels. This architecture could enable strongly interference-resistant data conversion. MATLAB has been a critical resource in Mumin’s research, and she has created multiple tools of value for the broader MathWorks community. By developing new approaches to forming high-quality representations of signals with few measurements, Mumin’s work has the potential to advance a wide range of applications, from automotive radars to medical imaging.

Peter Satterthwaite
Electrical Engineering and Computer Science

Peter Satterthwaite is a PhD candidate in electrical engineering and computer science whose research aims to create scalable approaches for fabricating devices and systems from novel nanomaterials, leveraging their most promising properties. His work to date has explored the clean, direct integration of 2D materials, demonstrating a completely dry and sacrificial layer-free approach to 2D material processing that addresses the long-standing challenge of degradations induced by processing. Supported by a MathWorks Fellowship, Peter will extend that promising line of research and investigate the integration of molecular materials into active nanoscale devices. Peter is also working on the scalable fabrication of molecular devices with built-in metrology that allows for the study and control of device performance at the atomic scale. MathWorks software has been an indispensable tool in Peter’s work to translate novel nanodevices into practical systems. His research is speeding up the creation of next-generation computing and sensing platforms to address the growing demands of data-intensive applications in machine learning, artificial intelligence, and the internet of things.

Miranda Schwacke
Materials Science and Engineering

Miranda Schwacke is a PhD candidate in materials science and engineering whose research seeks to develop new energy-efficient electrochemical programmable resistors, or electrochemical random-access memory (ECRAM), for physical implementations of neural networks that mimic the connectivity of the brain. Her first MathWorks Fellowship enabled her to explore new approaches to informed ion selection and materials design and to demonstrate a proof-of-concept for ECRAM using Mg2+ as the working ion. This work showed how careful choice of the ion-channel material system can improve device stability and compatibility with silicon processing. Her second MathWorks Fellowship will enable Miranda to explore the importance of channel microstructure in ECRAM devices. Her recent results suggest that ion and electron insertion into polycrystalline WO3 can strongly impact grain boundary space charge resistances, and this can be the dominant resistance modulation mechanism in ECRAM devices for low channel ion concentrations. Miranda’s work, which draws significantly from MATLAB tools, is yielding novel insights with important implications for the study and application of ECRAM devices and could inform the development of next-generation brain-inspired technologies.

Devang Sehgal
Institute for Medical Engineering and Science

Devang Sehgal is a PhD candidate in medical engineering and medical physics whose research aims to improve the clinical utility of electrical stimulation techniques for the treatment of neurological disorders. Specifically, Devang seeks to address the shortcomings of current trial- and-error-reliant methods by building a deeper understanding of the neurophysiological effects of stimulation and the brain network dynamics underlying diseases. He is testing a new multisite stimulation approach that could enable modulation of the brain network at scale. Devang’s project includes conducting experiments to compare the effects of single and dual-site stimulation in epilepsy patients, investigating the changes to the brain’s functional connectivity induced by delivery of stimulation from two sites, with the goal of contextualizing results in a patient-specific manner to aid eventual clinical deployment. MATLAB tools such as Blackrock’s Neural Processing Kit and Chronux are integral to Devang’s work, and he has made significant contributions to the development of MATLAB API CereLAB. His research is expanding our understanding of the underlying mechanisms of intracranial stimulation which is critical for developing new stimulation approaches to treat epilepsy and other brain disorders.

Devosmita Sen
Chemical Engineering

Devosmita Sen is a PhD candidate in chemical engineering whose research aims to better understand and model the micro- and macroscopic properties of polymer networks, which are widely used in common products, from rubber tires to contact lenses, and are critical for cutting- edge applications such as drug delivery and soft robotics. As a MathWorks Fellow, Devosmita will expand her innovative approaches in modeling the topology of complex polymer networks to enable more accurate prediction of desirable properties such as elasticity and toughness. To that end, she is developing MATLAB-based numerical models to quantify network topologies and illuminate aspects such as the dynamic bond exchanges shaping that topology. She has also developed a novel algorithm to elucidate the correlation between cyclic network structures and nonlinear fracture properties and plans to design simulations to understand the fundamental mechanism behind network fracture. Devosmita’s work offers exciting new directions in the study of polymer networks to advance novel material design, and insights from her research may have useful applications in many network types, from biology to the internet to transportation.

Dongchel Shin
Mechanical Engineering

Dongchel Shin is a PhD candidate in mechanical engineering whose research focuses on creating quantum and precision metrology platforms that probe fundamental physics and pave the way for future industrial technology. As a MathWorks Fellow, Dongchel will take a lead role in a cutting- edge experiment to observe gravitational interaction between two quantum systems, with the goal of answering a central question in modern physics: “Is gravity quantum?” His contributions include leading experiment design, which involved studying the effects of noises that can impact the motion of milligram scale masses with gravitational interactions of interest. He also created a novel theory of interferometric optical lever detection, which he used to demonstrate a record-setting quantum-noise-limited angular motion readout at the level of 10–12 rad. Finally, he is developing novel optimal signal processing techniques to extend and augment MATLAB’s multi-taper spectral estimation. Throughout his work, MATLAB has been an indispensable tool. Dongchel’s cutting-edge research in quantum optics, quantum measurements, and signal processing has the potential to deliver valuable knowledge and capabilities to the growing field of quantum engineering.

Oswin So
Aeronautics and Astronautics

Oswin So is a PhD candidate in aeronautics and astronautics whose research seeks to develop safe machine-learning methods for critical autonomous systems and bridge the gap between simulation and real-world systems. In previous research, Oswin has drawn on MathWorks’s Simulink to develop safe controllers for cases where the dynamics may be complicated but are known. As a MathWorks Fellow, he will build on this work, applying tools from reachability analysis and reinforcement learning to develop techniques that can provide safety guarantees even in cases where modeling errors are present. Concurrently, he will pursue new methods to perform adaptive safety by combining robust safety with online adaptation techniques such as adaptive control and Gaussian processes. Oswin has made significant contributions to the modeling and design of complex systems, drawing on MATLAB and Simulink, and enabled the development of advanced control algorithms that are both reliable and efficient. His research holds strong potential to deliver new tools and methods to advance real-world, safety-critical systems.

Yixuan (Cassie) Song
Materials Science and Engineering

Yixuan (Cassie) Song is a PhD candidate in materials science and engineering who is investigating novel phenomena enabled by special magnetic materials, termed ferrimagnets, in which the magnetism of neighboring atoms differs in strength and points in opposite directions. Supported by a MathWorks Fellowship, Cassie will pursue research to better understand this unique property, which promises a much more effective method for developing devices that perform data storage and other spintronics phenomena. Cassie has developed an algorithm for a macrospin model to simulate the magnetization reversal process in systems with complex anisotropy landscapes. Other important areas of inquiry in her research are the magnetic dynamics in the frequency range of GHz to THz, which may have important implications for future technologies, and demagnetization fields in magnetic materials that exhibit asymmetric shapes, such as thin films and cylinders. Using MATLAB to conduct the finite element analysis plays a central role in Cassie’s work. Her research has the potential to deliver important new knowledge and design approaches to advance the field of spintronics and terahertz materials science.

Mayuri Sridhar
Electrical Engineering and Computer Science

Mayuri Sridhar is a PhD candidate in electrical engineering and computer science who focuses on privacy-preserving computation. As a MathWorks Fellow, Mayuri will expand her work on providing statistical guarantees regarding robustness and security for algorithms in realistic deployed settings. Her recent work focuses on PAC privacy, where she has shown the intrinsic relationship between the stability of algorithms and their potential for privatization. This work provides a tight theoretical characterization of the noise required to privatize any algorithm as a function of its covariance, along with extensive experimental results validating the utility of these algorithms. In particular, she shows that privatizing complex algorithms like SVM or K-Means can be done in a black-box manner with a negligible impact on efficacy. MATLAB’s Parallel Computing Toolbox and Deep Learning Toolbox could enable her to extend her work to more complex models like BERT or even GPT. Mayuri’s work has strong potential to advance privacy-preserving computing with broad applications, from providing privacy guarantees when interacting with large public models like ChatGPT to robust learning guarantees on sensitive medical and financial data.

Stephan Stansfield
Mechanical Engineering

Stephan Stansfield is a PhD candidate in mechanical engineering who conducts basic research to understand the neural control of movement and applied research to develop assistive devices. His MathWorks Fellowship will support work in two areas. The first is a study of human interactions with dynamically complex objects, combining human subject experiments with MATLAB-based simulations using an input-shaping control strategy. His results suggest that that the brain may greatly simplify representations of flexible object dynamics during manipulation and exploit mechanical impedance to deal with errors. Stephan’s second project involves the development of an assistive device for older adults to help them stand up and sit down with the goal of preserving independence and preventing falls. He has created a soft, wearable “exosuit” and is preparing for human subject studies to characterize device interactions with the body. During these experiments, he will gather motion capture, force plate, and EMG data to analyze and visualize with MATLAB. Stephan’s work has significant promise to deepen our understanding of neuromotor control and advance new assistive robotic devices that could improve health and quality of life.

Soumya Sudhakar
Aeronautics and Astronautics

Soumya Sudhakar is a PhD candidate in aeronautics and astronautics whose research is focused on decision-making algorithms for miniature or long-duration robots that are energy- efficient in actuation and computing. As a MathWorks Fellow, she will investigate new methods for measuring uncertainty in machine learning in a computationally efficient way. To address the high computational costs of current methods, Soumya has proposed a novel method to exploit temporal correlation across the inputs such that only a single inference is required per input, for much greater computational efficiency. She is also investigating efficient online learning, an essential problem for robots with limited resources in the era of extremely large AI models, as well as the topic of computing’s climate impact, an important subject given the large amount of computing required for the navigation of autonomous vehicles. Soumya’s research makes significant use of MATLAB, and her research offers exciting new directions in energy-constrained perception, planning, and actuation to advance miniature and long-duration robotics in novel applications.

Anantha Narayanan Suresh Babu
Mechanical Engineering

Anantha Narayanan Suresh Babu is a PhD candidate in mechanical engineering whose research is focused on stochastic modeling and Bayesian learning of sea ice dynamics, as well as scientific deep learning and neural closure modeling for ocean dynamics. Anantha’s second MathWorks Fellowship will enable him to extend this work. His first focus will be utilizing a MATLAB-based Gaussian mixture model filtering and model learning framework for discriminating between competing sea ice model formulations and discovering new models from data. His second focus will be developing and refining physics-inspired neural architectures and training methodologies to model complex fluid and oceanic processes, including sea ice. By combining the strengths of physics-based numerical modeling with the adaptability and efficiency of deep learning, the resulting neural closure models promise enhanced accuracy and predictive capabilities at a significantly reduced computational cost. Anantha’s work holds the potential to advance our understanding of sea ice and ocean dynamics, which could have applications in climate modeling, environmental monitoring and forecasting, marine ecosystem protection, resource management, maritime transport, and policy and decision-making for the future of our planet.

Nishat Tabassum
Chemical Engineering

Nishat Tabassum is a PhD candidate in chemical engineering whose research aims to assess the feasibility of engineering small proteins as alternatives to monoclonal antibody therapeutics. Specifically, Nishat is working to design a non-immunoglobulin protein that targets the cytokine tumor necrosis factor-alpha and prevents it from binding to its receptors, thus preventing an inflammatory response. A common challenge in treating chronic inflammation in autoimmune diseases is that patients may become unresponsive to immunoglobulin-based treatments. Non- immunoglobulin proteins are promising alternatives as they are small and easily produced in peptide synthesizers. Nishat has developed a preliminary model that can significantly speed the identification of promising design targets and offers a clearer understanding of which parameters must be optimized for binder design. As a MathWorks Fellow, Nishat will further develop this model into a more detailed physiologically based pharmacokinetic model with results obtained experimentally. Her work has the potential to advance powerful new therapies for autoimmune diseases, and the MATLAB models and methods she has pioneered may aid other researchers in informing design targets for protein engineering in drug development.

Hao Tang
Materials Science and Engineering

Hao Tang is a PhD candidate in materials science and engineering whose research is focused on developing and applying computational methods to simulate physical systems ranging from quantum defects and functional alloys to electronic devices. A second MathWorks Fellowship will enable Hao to extend his work, which draws significantly on MathWorks toolkits for modeling, simulations, data analysis, and visualization. His recent and current projects include: the refinement of a new reinforcement learning method to accelerate the atomistic simulation of defect diffusion; the application of an equivariant graph neural network to improve the accuracy of electronic structure calculations of molecules; the development of a computational method to calculate color centers’ quantum sensing performance; and the creation of new computational methods for evaluating the energy levels, wavefunctions, and lifetime of neutron bound states. Hao’s research has already delivered new discoveries and methods in computational materials science, and his ongoing work has strong potential to push the boundaries of his field while also offering computational tools of value to researchers in many disciplines.

Jacob Toney
Chemical Engineering

Jacob Toney is a PhD candidate in chemical engineering whose research aims to develop machine learning-accelerated quantum chemistry tools. As a MathWorks Fellow, Jacob will pursue several objectives, the first of which is building deep-learning models to predict these catalyst structures and their reactive intermediates. He has already created a model capable of predicting ligand denticity and coordinating atoms with very high accuracy. Jacob is building tools to characterize the transition states of these catalysts, once formed, to predict rates of catalysis and deduce mechanisms. Next, Jacob will use the deduced structures and mechanisms to predict and optimize catalysts based on datasets of experimental outcomes. Finally, Jacob is leading projects aimed at unlocking greater predictive power from deep-learning models trained to predict the properties of transition metal complexes. Jacob’s research offers key insights into how catalyst structures inform reaction outcomes and is producing tools broadly useful to those working on catalyst design. His work also has potentially significant impacts on industry and the enhancement of materials syntheses using organometallic chemistry and could serve to make these processes more productive and energy efficient.

Julian Ufert
Chemical Engineering

Julian Ufert is a PhD student in chemical engineering whose research is focused on ethylene production, the second-highest greenhouse gas emitter in the chemical industry and ubiquitous in manufacturing around the globe. As a MathWorks Fellow, Julian aims to develop a clean, economical ethylene production process utilizing novel high- and intermediate-temperature electrochemical approaches, which may offer a way to electrify the process while also coping with byproduct and separation issues. Primary objectives of his project include conducting techno- economic analysis and life-cycle assessments of the synergistic integration of steam crackers with existing and technologically mature processes and developing two novel catalytic routes to ethylene production—electrochemical oxidative coupling of methane and electrochemically assisted ethane dehydrogenation—that are selective and inherently electrified. Julian draws on MATLAB for many aspects of this research and is creating reactor models and other MathWorks tools of prospective value for other researchers. By designing new, clean ethylene production methods that are economically competitive with incumbent technology, Julian’s research work has significant potential to help decarbonize the chemical industry.

Ilan M. L. Upfal
Civil and Environmental Engineering

Ilan M. L. Upfal is a PhD student in civil and environmental engineering whose research focuses on sustainable energy system design. With the support of a MathWorks Fellowship, Ilan will expand his work in wind farm design and translate those efforts into industry-ready MATLAB tools. His research aims to consider collective flow control in the optimization of wind farm layouts. Ilan’s work could include two valuable outcomes: a gradient-based optimization approach utilizing automatic differentiation to efficiently solve the high-dimensional control and layout optimization problem, which otherwise would be computationally infeasible using finite-difference-based optimizers, and a physics-based modeling approach that uses a generalized momentum model to accurately predict the impact of turbine control on wind farm power production. Ilan has demonstrated that considering collective flow control during wind farm design yields significantly smaller land-area footprints while reducing the cost of wind energy and increasing the lifetime profit of wind farms. Ilan’s research has the potential to advance wind power that maximally accelerates decarbonization, improves grid reliability, and provides valuable tools for research in aerodynamics, modeling, control, and sustainable power.

Thomas W. O. Varnish
Nuclear Science and Engineering

Thomas W. O. Varnish is a PhD candidate in nuclear science and engineering whose research focuses on magnetic reconnection, an explosive reconfiguration of magnetic field topology in a plasma that rapidly dissipates magnetic energy by heating and accelerating the plasma. Specifically, Thomas is exploring a special case of this process known as guide-field magnetic reconnection, in which a component of the magnetic field is directed out of the plane of reconnection. As a MathWorks Fellow, he is playing a lead role in the construction of PUFFIN, a new microsecond pulsed-power generator that will enable the first quasi-steady-state studies of many plasma phenomena. Through PUFFIN, Thomas will study how the stability and evolution of the reconnection layer are affected by a guide field and will implement advanced diagnostics such as Faraday imaging. MATLAB Simulink and Simuscape are essential to PUFFIN’s construction and Thomas’s work in general. His research has the potential to offer important insights into magnetic reconnection and plasma physics and associated phenomena such as solar flares, black hole accretion disks, and supernovae.

Alexander A. Velberg
Nuclear Science and Engineering

Alexander A. Velberg is a PhD student in nuclear science and engineering whose research lies at the interface of machine learning and nonlinear plasma dynamics. Specifically, Alex aims to develop a systematic program of data-driven equation discovery for plasma physics. His work builds on recent research demonstrating that it is possible to discover a known reduced plasma model from first-principles simulation data. Alex is exploring how to extend this methodology to the discovery of equations where some terms are unknown, namely, the data-driven discovery of so-called sub-grid closures. MATLAB’s Deep Learning Toolbox is the main research platform for this project. First-principles descriptions, including the detailed physical effects of discrete particles in plasmas, are extremely computationally intensive; therefore, the ability to accurately represent those effects in terms of an effective closure with a significantly reduced computational cost, would be transformational in terms of our ability to simulate plasmas. Alex’s work has the potential to offer these powerful capabilities and help usher in a new era of exploration in plasma physics, with applications to astrophysical plasmas and fusion energy.

Trent Weiss
Chemical Engineering

Trent Weiss is a PhD candidate in chemical engineering whose research is focused on electrochemical technologies, particularly lithium-ion batteries (LIBs), a promising technology for decarbonizing transportation, energy, and other sectors. As a MathWorks Fellow, Trent will address shortfalls in current LIBs using MATLAB-based modeling to explore an alternate cell-level architecture with the aim of developing a convection-enhanced LIB with improved storage capacity and charging/discharge rates. A central aim of his project is to advance our understanding of how convection improves mass and thermal transport. His future objectives include explorations of the structure-property relations for convection battery electrodes and identifying trends that inform specific values for different chemistries, cell configurations, and applications to reduce the experimental design space. His work in conventional LIBs may be generalizable to other intercalation chemistries, such as alternative metal-ion batteries and redox reservoirs useful for electrochemical energy storage or ion separation. Trent’s research has the potential to advance new concepts for electrochemical energy storage, thereby helping to enable the deep decarbonization of electricity generation, transportation, and industrial processes.

Duo Xu
Mechanical Engineering

Duo Xu is a PhD candidate in mechanical engineering whose research integrates materials science, heat transfer, and radiation transport in the development of polymer-based materials. His prior research included the creation of a polyethylene-based multifunctional composite material for space exploration; testing and modeling of an olefin block copolymer material for solid-state thermal management by mechanocaloric effect; and demonstrating strain-induced tunable thermal conductivity in the same olefin block copolymer, supported by a proposed theoretical explanation. MATLAB-based computational tools developed by Duo were pivotal in preparing materials from his lab. The multifunctional composite material he developed was sent for testing under real conditions on a recent Axiom Ax-2 mission launch. His second MathWorks Fellowship will support his work to create new elastic fibers for active and passive thermoregulation technologies and to explore methods like radiation crosslinking to enhance the reversible mechanical properties of materials he developed without compromising their performance and recyclability. Duo’s current and future research has excellent potential to advance materials that will have a global impact on energy efficiency and sustainability.

Haike Xu
Electrical Engineering and Computer Science

Haike Xu is a PhD candidate in electrical engineering and computer science whose research is focused on analyzing and designing algorithms. With the vast amount of data generated and collected today, nearest-neighbor search algorithms find applications in various domains, from constructing information retrieval systems to enhancing large language models for improved factuality. Supported by a MathWorks Fellowship, Haike will expand on his successful work on the nearest neighbor search problem, including building a theoretical understanding of why one such algorithm, DiskANN, is more efficient in practice than in theory, and identifying the relevant properties of datasets for which the algorithm is efficient. Going forward, Haike plans to explore additional new directions in nearest neighbor search, such as efficiently searching according to non-similarity-based metrics and constructing search indices with limited space and time resources. His research holds the potential to offer valuable new directions in algorithm analysis and design and serve broader needs for fast, efficient data processing and computing for research and industry.

Zi Yu (Fisher) Xue
Electrical Engineering and Computer Science

Zi Yu (Fisher) Xue is a PhD candidate in electrical engineering and computer science whose research addresses the critical need for energy-efficient and high-performance computing systems. Specifically, Fisher’s work focuses on applications of tensor algebra, a computing paradigm used across a wide variety of application domains, including scientific simulations, data analytics, and recommendation systems. As a MathWorks Fellow, Fisher will focus on designing new architectures to better perform sparse computation for both existing and emerging applications, with the goal of developing more advanced hardware that enables more efficient computational systems. Recently, he has demonstrated the viability of overbooking-based speculation in sparse tensor algebra accelerators, an innovative approach that could support more efficient hardware design. His current work focuses on designing hardware to enable larger and more accurate symmetry-equivariant neural networks, which efficiently represent interactions of physical systems and thus have both extreme data efficiency and robust generalization when modeling systems such as molecules, airfoils, climates, or galaxies. Fisher’s work has strong potential to support the development of essential hardware for computing systems combining high performance and energy efficiency.

Sungyun Yang
Chemical Engineering

Sungyun Yang is a PhD candidate in chemical engineering whose multidisciplinary research aims to develop innovative new approaches in pharmacokinetic modeling, materials science, and micro-robotics for biomedical applications. As a MathWorks Fellow, Sungyun will expand his work in several areas, including MATLAB-based simulations of physiological glucoregulatory systems to develop novel insulin therapeutics and designing colloidal-scale microrobots and microscopic sensors for deployment in confined fluidic environments such as microfluidics or circulatory systems. His current objectives include expanding the glucoregulatory system model from human models to other animal model species and enhancing the model with extended hepatic functions to capture complex dynamics, such as the mixed signaling of glucagon and insulin, and glycogen dynamics. Sungyun’s work is yielding novel insights and applications in multiple areas, from the development of novel 2D materials to new directions in diabetes treatment to highly original design strategies in colloidal-scale microrobots. In addition, the methods and tools Sungyun is creating to support his work hold great promise to advance research more broadly in chemical engineering, materials science, and biological engineering.

Yuheng Yang
Electrical Engineering and Computer Science

Yuheng Yang is a PhD student in electrical engineering and computer science whose research is focused on the development of end-to-end security verification frameworks for hardware designs. With the support of a MathWorks Fellowship, Yuheng will work to bridge the gap between computer architecture and formal verification, making formal tools easily accessible to hardware designers, including tools for both the early and later register-transfer level (RTL) stages. Currently, he is exploring verification techniques targeting RTL implementations. Yuheng’s vision is to innovate verification schemes, achieving better scalability by leveraging architectural insights, so that hardware designers can be involved in the verification loop. To that end, he is designing verification schemes for secure speculation defenses with two objectives: to allow hardware designers to assist the verification process by writing shadow logic machinery interacting with processor logic and to boost the verification scalability by exploring the taint tracking technique. Yuheng’s research could significantly contribute to MathWorks’s HDL tools by improving how the industry approaches hardware design through more effective methods and greater involvement of hardware engineers in the verification process.

Lale Yilmaz
Mechanical Engineering

Lale Yilmaz is a PhD candidate in mechanical engineering whose research focuses on nonlinear mechanics and granular media. Specifically, she is working to develop simulation tools to model uprooting processes in different soils and has created a MATLAB-based software package that combines nonlinear beam theory and a novel variant of the Resistive Force Theory (RFT) of granular media, enabling her to create the first simulations of flexible root-like structures that interact mechanically with soil during the process of uprooting. The resulting elastic RFT (eRFT) incorporates elasticity and captures the resistance provided by the granular soils in uprooting systems. As a MathWorks Fellow, Lale plans to adapt this paired eRFT-inextensible roots solver to more complex geometries, using a MATLAB-based root geometry generator she has created to manipulate the defining parameters of the root structure. She will also introduce viscosity to the solver and extend it to a wider range of soil models. Lale’s research has the potential to offer next- generation computational engineering tools supporting research in many complex systems and phenomena.

Nomi Yu
Mechanical Engineering

Nomi Yu is a graduate student in mechanical engineering whose research utilizes deep learning techniques to innovate the mechanical design process and to expand AI capabilities in design and manufacturing. As a MathWorks Fellow, Nomi will pursue two primary projects with the objective of creating models of additive manufacturing processes to predict the “manufacturability” of various geometries and incorporating these models in generative design tools. The first project aims to build a differentiable model for manufacturability-rule prediction, incorporating heuristic-based design rules to assess the printability of a given geometry. The second project seeks to produce an empirical model from scanned 3D prints to explore the potential of deep learning models to detect features associated with poor manufacturability that people may have a harder time noticing. Nomi makes extensive use of MATLAB and plans to make valuable contributions to the MATLAB community, including the implementation of 3D deep-learning networks and a centralized toolbox for geometric analysis. Their research could deliver novel techniques and insights with the potential to elevate design and manufacturing through AI and deep learning.

Rachel Zale
Mechanical Engineering

Rachel Zale is a PhD candidate in mechanical engineering whose research is focused on the physiology of the cardiovascular system and the global vascular effects of mechanical circulatory support (MCS). As a MathWorks Fellow, Rachel will pursue several objectives: to understand the impact of continuous-flow percutaneous ventricular assist device (pVAD) support on renal and cerebral autoregulatory function; to determine the impact of cardiogenic shock (CS) and added pVAD support on the distribution of blood flow to different organ systems; and finally, to identify effects of autoregulation on ventricular-vascular coupling. The aim of Rachel’s work is to test the hypothesis that there exist complex, multidimensional interactions between autoregulatory flow control mechanisms, volumetric flows to different organ systems, MCS device flow, and cardiac loads, with important implications in CS and added MCS. MATLAB is an essential tool for processing in vivo hemodynamic data and elevating the efficiency and quality of data analysis. Rachel’s work has the potential to broaden our understanding of the integrated physiology of the cardiovascular system and could ultimately help to improve the clinical framework and utilization of MCS devices.

Akshat Zalte
Chemical Engineering

Akshat Zalte is a PhD student in chemical engineering whose research involves innovative molecular representations for application to machine learning in chemistry. His research also spans process modeling and techno-economic analysis of future fuel systems to decarbonize long-haul trucking. As a MathWorks Fellow, Akshat will pursue research in two primary areas: making key improvements to Chemprop and evaluating various liquid energy carriers for their potential value in long-haul trucking. He plans to create a model that can learn based solely on the connections within a given molecule without knowledge of bond order and integrate optical and geometrical isomerism to create 2.5D approaches capturing chirality without the need for full 3D structural data. He will also expand an existing MATLAB framework to assess the economic and emissions implications of vehicle technologies and liquid fuel options such as Fischer-Tropsch diesel and liquid organic hydrogen carriers. Akshat’s work has the potential to yield discoveries that will accelerate the decarbonization of long-haul transportation, as well as provide elegant tools to help researchers in cheminformatics test and hone machine-learning architectures.

Baopu (Bob) Zhang
Chemical Engineering

Baopu (Bob) Zhang is a PhD candidate in chemical engineering whose research seeks to develop innovative approaches in microchip fabrication to meet rapidly growing demands for enhanced computation performance and energy efficiency. Block copolymer (BCP) directed phase separation (DPS), or the utilizing of guiding templates to direct BCP phase separation, is a promising technique for large-scale and cost-effective microchip fabrication. To date, most work in this area has been in two dimensions. Supported by a MathWorks Fellowship, Bob will utilize his expertise in polymer physics and microelectronics to study this BCP DPS and the feasibility of a three-dimensional (3D) approach. Using MATLAB, Bob plans to develop an efficient optimization algorithm to identify the optimal guiding template configuration for 3D target BCP patterns. This new model will be beneficial for accelerating guiding template design and screening for complex 3D target patterns and could help to realize the potential of BCP DPS for 3D microchip fabrication. Bob’s research has the potential to offer vital new knowledge and applications that address the need for rapid, efficient, and economical computing capabilities.

James H. Zhang
Mechanical Engineering

James H. Zhang is a PhD candidate in mechanical engineering whose research aims to develop interfacial solar evaporators as an alternative, cost-effective method of producing clean water. His previous MathWorks Fellowship enabled James to design experimental systems and transport modeling to better understand the performance of solar evaporators for desalination applications. With a second MathWorks Fellowship, James will extend this work, exploring the mechanisms underlying relevant phenomena, such as the coupling between heat and mass transport from evaporating porous interfaces. Beyond the evaporating performance in open laboratory conditions, he has also developed a comprehensive model to understand solar still device performance and identified the limiting transport resistances in the system. By combining a colleague’s MATLAB- based ray-tracing code and experiments on the light coupling effects with the air-liquid interface, James will study their effects on the water production rates of solar desalination devices. His research has strong potential to improve our mechanistic understanding of the thermodynamics of these effects and speed the development of solar technology to meet global needs for clean drinking water.

John Z. Zhang
Mechanical Engineering

John Z. Zhang is a PhD candidate in mechanical engineering whose research examines the interaction between electrostatic and mechanical forces in piezoelectric materials and in high- intensity electric fields. Through modeling, he seeks to understand these interactions and provide useful guidelines for design. As a MathWorks Fellow, John’s first project involves designing a totally implantable microphone and amplifier for cochlear implants. The sensor uses a bending piezoelectric cantilever to measure eardrum motion as a proxy for free-field sound. The second project concerns the active shaping of in-space manufactured ultra-large diameter reflector antennas using electrostatic pressure. Both systems involve specialized electronics; the cochlear implant microphone requires an exquisitely low-noise and low-power sensing amplifier, while the reflector requires high-voltage electronics to adjust the electric fields that shape it. MATLAB is an essential tool for both projects and in the course of his work. His research is yielding innovative approaches for modeling and designing continuum electromechanical systems and has the potential to advance a variety of applications beyond hearing implants and space communication systems.

Yuexuan (Vincent) Zu
Chemical Engineering

Yuexua (Vincent) Zu is a PhD candidate in chemical engineering whose research is focused on engineering microorganisms for the renewable production of biofuels. Specifically, Vincent aims to engineer cells capable of converting renewable feedstocks into intracellular lipids with high titer, productivity, and yield. Supported by a MathWorks Fellowship, he will use MATLAB-based in silico metabolic models to explore a novel co-feeding approach, pairing glucose with the less-preferred substrates. This approach significantly increases biomass production and product yield; a deeper understanding of this phenomenon could shed light on the underlying metabolism of metabolic adaption. Vincent has created a metabolic model for the yeast Yarrowia lipolytica and identified a promising target gene that has been cloned into cells. He plans to develop a predictive model to guide the genetic design of strains for further performance improvement. In addition, Vincent has developed an automated data acquisition/processing pipeline to monitor real-time CO2 production in bioreactors. His work has strong potential to advance the development of renewable biofuels and to promote innovative applications of MathWorks tools within the field.