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 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 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 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 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 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 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 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.

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.

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 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 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 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 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 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 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 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 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 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 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.