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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:

Sayed Saad Afzal
Electrical Engineering and Computer Science

Sayed Saad Afzal is a PhD candidate whose research interests lie in developing novel low-cost and low-power distributed sensor technologies and systems for climate monitoring, environmental sustainability, and food security. Specifically, Sayed is working on ocean sensor technologies to monitor vital signs such as biodiversity and carbon balance. With the support of a MathWorks Fellowship, he will advance his successful work on an underwater sensor utilizing backscatter, a technique that enables communication with 1 million times less power than existing systems. He recently used this technology to build one of the world’s first battery-free underwater cameras, enabling long-duration monitoring in remote ocean locations. MathWorks tools such as MATLAB’s DSP System Toolbox and Signal Processing Toolbox are crucial tools in his research. Sayed has already made notable technological contributions with implications for climate monitoring, food security, and ecology. His future work has tremendous potential to advance sensor technologies, supporting innovative research and a sustainable future.

Jasmine Jerry Aloor
Aeronautics and Astronautics

Jasmine Jerry Aloor is a PhD candidate in robotics, aerospace engineering, and artificial intelligence whose research seeks to address the challenge of balancing efficiency and fairness in multi-agent teams using reinforcement learning. As a MathWorks Fellow, Jasmine will pursue research exploring motion planning and control for dynamic multi-agent systems, with a special emphasis on safety, robustness, and human interactivity. Jasmine’s past research endeavors include work in imitation learning for social navigation, control barrier functions, and coverage path planning; recent projects include the development of a safe and socially aware motion planning method using imitation learning for general aviation aircraft in multi-aircraft shared airspace and the design and testing of an improvised box-wing micro air vehicle. MATLAB supports design, modeling, and analysis across her research interests. Jasmine’s innovative work has the could improve machine learning and autonomous systems and help them realize their full potential.

Abtin Ameri
Nuclear Science and Engineering

Abtin Ameri is a PhD candidate whose research interests lie at the intersection of quantum computing and plasma physics, with the goal of advancing the use of fusion energy. Specifically, Abtin is working on the development of quantum algorithms for the numerical simulation of fusion reactors by identifying candidate problems suitable for a quantum computing approach and exploring how to mathematically frame those problems to maximize the added efficiency that quantum computers may offer. With the support of a MathWorks Fellowship, Abtin will investigate whether the problem of determining plasma stability in fusion reactors can be efficiently computed on quantum platforms. MATLAB has been an indispensable tool at each step of his work. Abtin’s ambitious research, if successful, will be the first example of a quantum algorithm with immediate relevance to fusion research and could expedite progress toward putting fusion energy on the grid, contributing to sustainable energy solutions and mitigating the impacts of climate change.

Attias Ariel
Civil and Environmental Engineering

Attias Ariel is a PhD candidate whose research is focused on designing resilient structures in urban environments that are better able to withstand natural disasters. With the support of a MathWorks Fellowship, he will develop a novel approach, assessing the resilience of urban structures by combining finite elements and statistical physics principles. This strategy characterizes fracture at the element level using spins, describing fracture in a statistical ensemble, effectively bridging the gap between brittle fracture theory and statistical physics. Through his research, which takes extensive use of MathWorks tools, Attias is providing solutions to several computational challenges associated with structural resilience, including fracture initiation, fracture branching, computational efficiency, and in-situ prediction of fracture propagation. His goals include creating a new framework for considering the fragility of structures and the analysis of their life cycle. Attias’s research could have a significant impact on design for the structures of tomorrow, ultimately improving the safety and sustainability of our communities.

Taylor Elise Baum
Electrical Engineering and Computer Science

Taylor Elise Baum is a PhD candidate whose research interests lie in developing systems that enhance clinician decision-making processes for cardiovascular system management. Currently, Taylor is working on a closed-loop blood pressure control (CLBPC) system to improve blood pressure management in intensive care units and operating rooms. A MathWorks Fellowship will allow her to advance the system toward eventual human testing. Milestone achievements include her development of a physiologically based theoretical framework for the system; a cardiovascular state estimation method to inform control decisions; and an end-to-end hardware system for monitoring blood pressure and controlling vasoactive medications in swine. Notable is her method for cardiovascular state estimation based on the Windkessel model, which has improved accuracy and time resolution compared with current approaches. She is now leading preparations for upcoming animal testing, a crucial step toward human testing. Taylor makes extensive use of MathWorks tools and is pursuing research with strong potential to enhance clinical decision- making in cardiovascular medicine, leading to improved patient outcomes and future innovations in engineering applications for health care.

Artittaya (Tiya) Boonkird
Nuclear Science and Engineering

Artittaya (Tiya) Boonkird is a PhD candidate whose primary research interest is in developing machine learning models to predict material properties, with an emphasis on materials containing defects. Specifically, Tiya is examining how the introduction of defects into materials may enhance their mechanical, electrical, and magnetic properties, and how these defects can be controlled to enable useful applications, such as in the semiconductor industry. A MathWorks Fellowship will support Tiya’s work to develop a machine-learning model to extract information about defect type and distribution from x-ray diffraction data collected at various parameter ranges that are typically fixed in conventional measurements. These insights could enable another way to experimentally characterize defects, leading to a better understanding of how they influence material properties. MATLAB is an indispensable tool in Tiya’s work, which has strong potential to advance materials research and offer new avenues for material development in industry.

Lauren Clarke
Chemical Engineering

Lauren Clarke is a PhD candidate whose research focuses on electrochemical methods for carbon dioxide (CO2) capture and concentration, with the goal of mitigating emissions during the transition to a carbon-constrained, renewable-energy economy. Electrochemical approaches may enable lower energy CO2 separations than current processes, as well as direct integration of renewables, modular deployment, and safer operation. A MathWorks Fellowship will enable Lauren to explore the processes that control the performance and cost of electrochemical separation strategies for post-combustion capture, a carbon-neutral application, and direct air capture, a carbon- negative application. Her objectives include developing electrochemical cells for CO2 separation and capture, building models to identify favorable combinations of sorbent properties and device characteristics, and assessing the role of resistive losses in electrochemical CO2 separation systems. Lauren’s work, which makes extensive use of MathWorks tools, has the potential to provide valuable insights into electrochemical CO2 capture processes, which may drive innovation in discovery science and technology and could be applicable to other electrochemically driven separation processes in sustainable manufacturing.

Andres Garcia Coleto
Electrical Engineering and Computer Science

Andres Garcia Coleto is a PhD candidate who is conducting innovative research in the field of silicon photonics, and developing new applications of integrated silicon photonics for industry and health care. In his prior work, Andres has made valuable contributions in optics for telemedicine and created a new biomedical imaging device in the form of a swallowable capsule. Supported by a MathWorks Fellowship, Andres seeks to develop 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. Additionally, he is working on a chip-based solution for resin-based 3D printing, and using integrated silicon photonics to interface with the body through optical trapping. MATLAB is an essential tool in each of these pursuits. Andres’s research has great potential to advance silicon-photonics-based solutions for augmented reality, 3D printers, and other industrial applications, as well as next-generation medical tools offering significant benefits in human health.

Leticia Mattos Da Silva
Electrical Engineering and Computer Science

Leticia Mattos Da Silva is a PhD candidate conducting innovative research in the fields of geometry processing and numerical methods. Specifically, Leticia’s work concerns the analysis of partial differential equations (PDE), a ubiquitous technique in computer graphics, geometry processing, and adjacent fields, with a focus on the use of second-order parabolic PDE. Her research is implemented entirely in MATLAB. A MathWorks Fellowship will support Leticia’s ongoing work in developing new frameworks to solve a larger number of nonlinear and challenging PDE over discrete curved surfaces. Her existing method of leveraging a splitting integration strategy for second-order parabolic PDE represents a significant improvement over classical frameworks. Leticia’s plans include applying her current framework for second-order parabolic PDE to a wide array of uses in graphics and geometry processing, including position-based flow using the G-equation for realistic models of thin flames and fire and new approaches to stochastic heat kernel estimation on curved triangle meshes using the Fokker-Planck equation. Her research has the potential for far-reaching impacts in many fields, from physics-based simulation to CAD design.

Benjamin Dacus
Nuclear Science and Engineering

Benjamin Dacus is a PhD student whose research interests include the study of how structural materials respond in nuclear reactor environments and the fabrication of new radiation-tolerant materials. Ben has made significant contributions to a first-of-its-kind in situ ion irradiation transient grating spectroscopy (TGS) called I3TGS, enabling precise replication of conditions in the first commercial fusion power reactors, and increasing data throughput by a factor of over 1,000,000 using a MATLAB toolchain. His second MathWorks Fellowship will enable him to continue his productive research using TGS to show correlations between thermal properties and microstructural change in materials. By offering new insights into material conditions, Ben’s work could help to inform decisions about reactor lifetimes; extended reactor lifetimes could result in major savings in the energy sector while identifying reactors ready for decommissioning can improve safety. Ben’s work has already led to exciting progress in making I3TGS a robust and commercializable technique for materials analysis, and his research has strong potential further to expand nuclear engineering research and the energy sector.

Mary Dahl
Aeronautics and Astronautics

Mary Dahl is a PhD candidate whose research interests involve using artificial intelligence to optimize CubeSat (small satellite) data collection, autonomous systems for the safe, on-orbit assembly of small satellites, and systems engineering for CubeSat missions. A MathWorks Fellowship will support Mary’s work to develop technologies for more effective data collection in space. Her current project is focused on the design of CubeSats for orbital wildfire prediction by integrating powerline emissions. MATLAB is a critical tool in Mary’s work, which she has applied to tasks such as experimental data analysis, machine learning algorithm development, and satellite constellation planning. Going forward, Mary plans to continue integrating MathWorks-based software into her wildfire prediction work and her sensor fusion platform for Earth observation. She will also make her work and custom MATLAB tools available to other researchers. Mary’s research could contribute to new technologies that advance space exploration and climate science.

Ippolyti Dellatolas
Mechanical Engineering

Ippolyti Dellatoas is a PhD candidate whose research explores the mechanics of multiphase materials, specifically composites composed of constituents of different phases, such as alloys, emulsions, and reinforced concrete. The goal of her research is to develop a comprehensive framework of the mechanics and dynamics of composite systems, focusing on how attractive interactions between components impact the mechanical properties of these materials at large scales. Supported by her second MathWorks Fellowship, Ippolyti will extend ongoing investigations of filler-reinforced hydrogels, where filler nanoparticles are embedded into a polymer hydrogel, yielding a composite gel with improved mechanical properties and additional functionalities conferred by the fillers. MathWorks has provided critical support for Ippolyti’s research, which is helping to advance innovative applications of technology in the development of new materials and could have major impacts on materials science, mechanical engineering, geoscience, and environmental science.

Jie Deng
Civil and Environmental Engineering

Jie Deng is a PhD candidate in systems ecology whose interdisciplinary research is focused on the emerging area of theoretical community ecology. Specifically, Jie is developing a series of potentially groundbreaking hypotheses about how living systems form and develop. Her central proposal—that living systems assemble in ways that maximize the probability of persistence—could be used to forecast and engineer these systems. She is developing a probabilistic theoretical framework, built in MATLAB, to model and predict the assembly and disassembly processes governing ecological communities and verified that framework using experimental data on fruit fly gut microbiota. A MathWorks Fellowship will help her extend these frameworks to invasion processes (e.g., E. Coli), and test those theories with data from human gut microbiota. Jie’s innovative research is offering valuable insights into the functioning of ecological communities under varying environmental conditions and considers many species. This work has the potential to enhance our understanding of ecological services and functions, from the human immune system to soil formation.

Louis DeRidder
Institute for Medical Engineering and Science

Louis DeRidder is a PhD candidate whose research is focused on developing a closed-loop drug delivery system for chemotherapy drugs. Specifically, Louis seeks to create a system that can individualize chemotherapy doses with greater precision and efficacy than the commonly used method based on body-surface area. Louis has successfully developed a system for 5-fluorouracil (5-FU), a common chemotherapy drug, in which drug concentration is measured by a sensor, and this value is input into a control algorithm that adjusts the infusion rate to achieve the desired concentration. Supported by a MathWorks Fellowship, Louis will work on adapting this system to other chemotherapies. He is also at work on a novel medical device that is part of an emerging class of therapeutics called electroceuticals. MathWorks tools have been critical to Louis’s research, which holds great promise for improving chemotherapy and advancing the development of innovative medical devices to improve patient’s lives.

Somayajulu Dhulipala
Mechanical Engineering

Somayajulu Dhulipala is a PhD candidate whose research interests are in the fields of computational and experimental solid mechanics and architected materials. Specifically, he is working to develop non-periodic self-architected metamaterials with tunable mechanical properties, which overcome drawbacks of conventional additively manufactured materials such as stress concentrations and poor scalability. With the support of a MathWorks Fellowship, Somayajulu will develop self-architected spinodal morphologies that can be fabricated through a phase-separation process. In the course of his research, he has developed MATLAB-based scripts enabling a wide range of essential tasks, including reading data from a nanoindentation tool and simultaneously providing material properties of interest; syncing stress-strain data to SEM videos of nanomechanical experiments; and transforming large, complex datasets from a nano X-Ray CT tool into a 3D model of a materials’ microstructures. Somayajulu’s innovative work has tremendous potential to advance the field of architected materials, powering future research and the development of valuable materials and products.

Yuqin Sophia Duan
Electrical Engineering and Computer Science

Sophia Duan is a PhD candidate whose research interests are focused on the development of novel quantum technologies. Her current work is aimed at designing accurate large-scale computational models to describe the behavior of quantum devices, considering both the quantum object and the complex experimental chain required for device operation. Such models could enable a greater understanding of the fundamental limits of laboratory measurements and quantum physics. A MathWorks Fellowship will enable her to continue work on a novel model that leverages the “digital twin,” concept, and adapts the “smart factory” paradigm to a “smart research” paradigm, to produce a full-scale physical-digital integrated twin system with a “world view.” This digital twin continuously receives data from its physical twin (i.e., the experimental setup), allowing for comprehensive simulation and performance analysis. Her novel methodology significantly shortens the time required for direct training of physical setups while maintaining simulation accuracy, optimizing experimental designs, and predicting device behavior. Sophia’s work is yielding valuable contributions to MATLAB’s quantum codebase and has the potential to break new ground in quantum science and technology.

Souha El Mousadik
Civil and Environmental Engineering

Souha El Mousadik is a PhD candidate in environmental fluid dynamics whose research addresses areas of critical importance for decarbonization and climate mitigation, including the impacts of deep-sea mining and modeling strategies for ocean carbon sequestration. Souha’s first MathWorks Fellowship enabled her to explore the characteristics and hydrodynamics of seabed sediment generated by deep-sea mining and to develop and test instrumentation to characterize suspended sediment properties in-situ at abyssal depths of over 6000m. She also conducted fundamental fluid flow experiments to study the suspensions created by turbidity currents in the ocean, which arise in settings such as cable laying for offshore wind farms and deep seabed mining. Supported by her second MathWorks Fellowship, Souha will focus on marine carbon dioxide removal (mCDR) and utilize her sensor technology to characterize the settling rates of particulate organic carbon created by mCDR approaches, a critical parameter for modeling to support global trading of carbon credits. Souha’s work makes extensive use of MathWorks tools and has the potential to offer vital insights that will shape emerging practices and industries in the renewable energy economy.

Nathan Ewell
Chemical Engineering

Nathan Ewell is a PhD candidate whose research interests focus on aerosol filtration and developing improved respiratory PPE technology, such as facemasks and respirators, essential tools for mitigating the spread of viral diseases such as Covid-19. Specifically, Nathan is working to develop PPE tools made from electro-spun nanofiber nonwoven materials that improve efficacy, manufacturing cost, and sustainability. He has successfully demonstrated an alternative to the traditional N95 technology, a new design diagram for evaluating N95 technologies. A MathWorks Fellowship will support Nathan’s work to extend these improvements to filters of any material composition, including biodegradable compositions that are more sustainable than the industry standard, polypropylene, and to further explore theories and models of aerosol filtration. MATLAB is an indispensable tool in Nathan’s work. His innovative research and the models he is sharing with the research community hold great promise to inform the design and manufacture of next- generation respiratory PPE, increasing our preparedness against viral respiratory disease.

Zi Hao Foo
Mechanical Engineering

Zi Hao Foo is a PhD candidate whose research interests focus on water treatment strategies for chemical separations. Specifically, he is working on sustainable systems for accessing non- traditional sources of critical minerals such as lithium. A MathWorks Fellowship will support his innovative research in two primary areas. The first is creating computational models for multicomponent solution-diffusion in forward and counterflow reverse osmosis; his most recent models reproduce data more accurately than existing models by a factor of 10. His second focus is the use of dimethyl ether (DME) for solvent extraction of water from high-salinity brines. Zi Hao has created high-accuracy models for the conditions likely in a practical water-DME separation system that can dewater brines (to produce clean water) or cause fractional precipitation of solutes (useful in the recovery of minerals and metals from saline streams). Zi Hao’s research, which makes extensive use of MATLAB tools, has strong potential to advance novel approaches in sustainable lithium and water harvesting and to move the clean energy industry forward.

Camilo L. Fosco
Electrical Engineering and Computer Science

Camilo L. Fosco is a PhD candidate whose research integrates computer vision and cognitive science to build innovative models that can mimic the brain’s intricate processes to solve computer vision tasks. Currently Camilo is leveraging generative models to produce images and videos that maximize a specific cognitive metric, an approach that will enable generative techniques that are expressive and accurate, and also controllable in dimensions typically hard to quantify, like attention-grabbing potential and memorability. A MathWorks Fellowship will support his work on a ground-breaking, MATLAB-based project to analyze functional MRI and EEG data from subjects watching memorable and non-memorable short videos, which will serve as the basis for training models to regenerate the videos from the brain signals. This could lead to novel applications related to thought visualization and shed light on video processing in the brain. By combining cutting-edge generative models with insights from cognitive neuroscience, Camilo’s work has the potential to enhance our understanding of the brain’s mechanisms and help create more effective, human- centric AI solutions.

Amelia Gagnon
Aeronautics and Astronautics

Amelia Gagnon is a PhD candidate whose research draws on multiple disciplines to develop cutting-edge instrumentation to monitor human health and performance for pilots, astronauts, and others who operate in high-performance environments. With the support of her third MathWorks Fellowship, Amelia will pursue two projects, both of which rely on MathWorks tools. The first project explores the use of functional near-infrared spectroscopy (fNIRS) as an additional sensory modality to support the objective measurement of cognitive load in operational environments. The second project focuses on hurricane and tropical storm monitoring using microwave radiometers onboard CubeSats; data from this project will help to advance weather monitoring with greater temporal resolution. Amelia’s innovative and interdisciplinary work has yielded valuable contributions for both satellite systems and human space exploration and has the potential to offer many more useful insights and tools for air, space vehicles, and Earth-based applications.

Chinmay Gangal
Chemical Engineering

Chinmay Gangal is a PhD candidate whose research examines flow-induced crystallization (FIC) in polymers using computational methods. Specifically, he is developing computational methodologies to study the first stage of FIC, the flow-enhanced nucleation (FEN) of crystals in polymers, a process with relevance to a number of important industrial processes such as the fabrication of fibers, films, packaging, and composites. A MathWorks Fellowship will support Chinmay’s ongoing research to understand FEN through molecular simulations that examine four key FEN parameters: different flow types, temperatures, strain rates, and polydispersity. He is also developing a new methodology for cases where the activation barrier for crystal nucleation is too high to surmount by brute force molecular dynamics simulations, using an advanced technique called forward flux sampling. In the future, he plans to apply machine learning techniques to the identification of crystalline clusters within his simulations. Chinmay’s work is bringing new capacity in molecular-level modeling to the MathWorks community and has the potential to provide important new insights and strategies in polymer modeling, with significant benefits for research and industry.

Chloe Gentgen
Aeronautics and Astronautics

Chloe Gentgen is a PhD candidate whose research focuses on developing critical capabilities to support space exploration, including mission architecture, propulsion systems, and in-situ resource utilization. Her notable achievements include a successful demonstration of in-space refueling through a microgravity experiment; leading MIT’s winning team in the RASC-AL 2022 challenge to design an architecture for propellant production on Mars; and working on a NASA mission proposal to explore Enceladus, a moon of Saturn. Supported by her second MathWorks Fellowship, Chloe will develop a MATLAB-based design methodology framework for planetary exploration, using the future Uranus mission as a case study. Her goal is to offer design modifications to enhance the mission concept currently selected by the National Academies, including identifying new technologies that can increase the spacecraft’s performance and understanding the feasible sequences of fly-bys that will allow the spacecraft to meet its scientific goals at Uranus. Chloe’s path-breaking work has already yielded important contributions to space systems engineering, and her work holds significant potential to advance future explorations of our solar system.

Samuel Dutra Gollob
Mechanical Engineering

Samuel Dutra Gollob is a PhD student whose research interests focus on the design, modeling, and fabrication of soft robotics. To advance this work, Sam has built a generalized modeling tool for soft actuator design and multi-modal models for use in controls and the design of other soft robotic architectures. With the support of a MathWorks Fellowship, he will extend these approaches to create a modular multi-physics modeling tool to design novel soft robotic pneumatic power sources that address the limitations of pneumatic actuation approaches by leveraging reactions with propellants to power lightweight, fast, and controllable pneumatic devices. Sam’s ultimate goals are to build a design framework for propellant-based pneumatic power that integrates existing and novel architectures and to develop a fully coupled model to design propellant power sources.

MathWorks tools are central to Sam’s research. His contributions to fundamental research and practical tools could have a significant impact on future soft robotics devices, from implantable and wearable medical devices to technologies for deep-sea sample collection and search-and-rescue operations.

Minghao Guo
Electrical Engineering and Computer Science

Minghao Guo is a PhD candidate whose research interests are in polymer informatics at the intersection of machine learning and computer graphics, with a focus on computation design for chemistry, material, and shape modeling. A MathWorks Fellowship will support Minghao’s ongoing work to create a polymer informatics system for efficient exploration and automated optimization for polymer design. Currently, polymer synthesis is a resource-intensive process reliant on very small amounts of experimental data; Minghao’s comprehensive system enables combined property prediction and molecular generation within the constraints of small data sets.

He has also conducted promising research in 3D shape design within the challenging domain of multi-objective optimization with the aim of designing manufacturable 3D shapes that exhibit optimal balance between competing performance objectives. His development of grammar-based representations for modeling chemical data provides an efficient, novel avenue for the discovery of unique molecular structures and has important implications in many fields within the MathWorks and design communities. Minghao’s pioneering research has the potential to support future breakthroughs in predictive modeling, chemical system analyses, and material design.

Dat Quoc Ha
Civil and Environmental Engineering

Dat Quoc Ha is a PhD candidate whose research focuses on innovative structural design methods, with the goal of improving access for all users to generative design tools. Specifically, Dat is interested in topology optimization, a process that has been shown to yield new and surprising solutions that typically outperform conventional designs. With the support of a MathWorks Fellowship, Dat will continue his successful work on the framework called “Human-Informed Topology Optimization” (HiTop) in his recently published paper, which combines the automatic nature of optimization algorithms with the ingenuity of the human engineer. Dat has relied on MathWorks tools, like MATLAB’s Global Optimization and Image Processing Toolbox, to move this promising work forward. His research is opening exciting possibilities in emerging fabrication processes such as fused filament and concrete 3D printing and has the potential to make topology optimization more interactive and accessible so that designers across diverse sectors can benefit from this novel design paradigm.

Chase M. Hartquist
Mechanical Engineering

Chase M. Hartquist is a PhD candidate whose research focuses on developing and understanding soft materials and systems. Specifically, Chase investigates the mechanical and thermal responses of soft materials such as hydrogels and elastomers with controlled network architecture, with the aim of advancing new technologies and applications in this field. He has developed an elastomer that contains a homogenous network architecture and intrinsically toughens to prevent failure through strain-induced crystallization. Additionally, he has proposed that simulating macroscale polymer-like versions of these networks can produce similar relations between individual chains and bulk fracture properties. With the support of a MathWorks Fellowship, he will further characterize the structured elastomer and simulate polymer-like networks to investigate the interplay between network design, fracture theory, and performance. MathWorks tools have been an imperative component of this work. Chase’s research has the potential to advance pioneering applications of advanced-performance soft materials in many domains, from tissue engineering and wearable devices to soft robotics and solid-state cooling.

Ryann Hee
Aeronautics and Astronautics

Ryann Hee is a PhD candidate whose research interests center on the human aspects of aerospace engineering. Her current work, supported by a MathWorks Fellowship, is a project to define the optimal design space of a micro-electrical-mechanical (MEMS) scale Stirling engine. This work is modeled using MATLAB’s Simulink software. Her previous research endeavors include studies of the neuro-vestibular response in mixed gravitational environments and the development of virtual reality applications in zero gravity contexts. She has also made valuable contributions to the Platform for Expanding AUV exploRation to Longer ranges (PEARL) project and to the development of path planning and recovery methods for autonomous underwater vehicles with MOOS-IvP, an autonomy framework for marine vehicles. Ryann’s future work has strong potential to advance aerospace engineering through the development of innovative methods and applications.

Wenyuan (Roger) Hou
Aeronautics and Astronautics

Wenyuan (Roger) Hou is a graduate student whose research is focused on thermal modeling techniques for laser powder bed fusion, a metal additive manufacturing process. Specifically, he is using MATLAB tools to perform numerical integration on analytical solutions for point-source heat conduction to account for the dispersed nature of laser heating and the effect of latent heat during melting. With the support of a MathWorks Fellowship, Roger will continue his work to implement a module for modeling the nucleation of oxides during metal additive manufacturing. Additionally, he is using MTEX, an open-source MATLAB toolbox, to analyze grain orientation data collected via electron microscopy and characterize the grain texture and phase distribution in a novel, additively manufactured superalloy. Roger’s work is helping to elucidate precipitate dispersion and slag formation in the printing process and identify the optimal set of printing parameters to produce the most suitable material for post-processing and eventual use in high-temperature aerospace applications.

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

Yi-Hsuan (Nemo) Hsiao is a PhD candidate whose research lies at the intersection of soft robotics, micro-robotics, and aerial robotics. Specifically, Nemo is leading the development of bumblebee-inspired aerial robots powered by artificial soft muscles, with the goal of achieving resilient robotic flight in cluttered environments. Drawing on numerous MathWorks tools, he has successfully developed robots with novel insect-like functions such as low-cost localization, damage resilience, extended flight endurance, nimble maneuverability, and collaborative behaviors. A MathWorks Fellowship will support his continued research to implement state-of-the-art control algorithms to enable highly agile maneuvers, study the collective intelligence of robot swarms, and advance the capabilities of these robots to execute complex tasks such as assisted pollination. Nemo’s path-breaking research is yielding tools and approaches of significant benefit to the MathWorks community and holds great promise to advance micro-robotics for a broad spectrum of applications.

Siying Huang
Materials Science and Engineering

Siying Huang is a PhD candidate whose research interests are in magnetism and spin dynamics, topics that are central to the advancement of spintronic materials and devices. Specifically, she is working on the generalization of an analytical framework for magnetic domain wall dynamics to accommodate multi-sublattice ferrimagnetic materials, which are emerging as a major material class of interest in the field. Siying has successfully developed a novel method to approximate domain wall dynamics in ferrimagnets with an arbitrary degree of compensation, which enabled her to investigate the origins of very fast domain wall motion driven by electric currents in magnetic insulating ferrimagnets. A MathWorks Fellowship will support her ongoing work to experimentally verify a novel mechanism that she has shown mathematically could fully account for fast domain wall motion. Siying’s research, which makes extensive use of MathWorks tools, has the potential to have a major impact on spintronic research technologies and to deliver a paradigm-shifting new means to drive ultrafast magnetization dynamics.

Richard Ibekwe
Nuclear Science and Engineering

Richard Ibekwe is a PhD candidate whose research explores the behavior of defects in large- scale high-temperature superconducting magnets for fusion energy applications. A MathWorks Fellowship will enable Richard to pursue a highly innovative project that examines these defects from a superconducting physics perspective and attempts to demonstrate that they can be tolerated to a much higher degree than previously believed and may even be utilized as tools to control the behavior of superconducting currents and to function as real-time diagnostics. Currently, he is designing and testing novel prototypes of superconducting cables with customized instrumentation and electronics for precision measurement, along with physics-based MATLAB models of the electrical behavior of these cables. Ultimately, Richard’s research has the potential to make large-scale superconducting magnets for industries—from fusion energy to wind turbines to MRI machines—more affordable, accessible, capable, and operationally robust.

Hannah Jackson
Institute for Medical Engineering and Science

Hanna Jackson is a PhD candidate whose research interests focus on the development of new treatment approaches for drug-resistant epilepsy using a recently developed implantable device to deliver micro-doses of drugs to small regions of the brain. Specifically, Hannah is working to adapt this method to treat focal epilepsy, the most common form of the disease, by delivering anti-seizure medications directly to the focal point of a seizure, thereby reducing side effects and improving overall seizure management. MathWorks tools have enabled Hannah to demonstrate the efficacy of this method in a mouse model, and she has created and shared a number of customized tools that will be useful to other researchers in the MathWorks community. With the support of a MathWorks Fellowship, she hopes to demonstrate chronic seizure reduction in a large animal cohort, and further develop the implantable device to optimize drug delivery and improve its clinical translation. Hannah’s work holds significant promise to advance our understanding of epilepsy and accelerate the development of new treatments for this disease.

Kyle Jiang
Mechanical Engineering

Kyle Jiang is a PhD candidate whose research leverages electrochemistry to develop lithium (Li) metal anodes for applications requiring greater capacity and energy density than currently achievable with Li-ion batteries. Specifically, Kyle is investigating factors limiting the cycling performance of Li metal anodes, which primarily originate from the thermodynamic instability of the solid-electrolyte interphase. In previous work, he successfully isolated molecular structural features of electrolyte additives correlated with enhanced cyclability. As a MathWorks Fellow, Kyle will expand on this work to analyze problematic heterogeneities in operating cells and develop strategies to mitigate them to extend battery lifetimes. Using MATLAB, Kyle has developed a framework to efficiently parse data streams from diverse formats, enabling rapid analysis and visualization of experimental outcomes. His hypothesis is that hidden lateral pressure gradients in coin cells impact Li anode cyclability, and his initial findings identify certain variables within cells that affect the pressure distribution on electrodes. This work is a key step toward measuring the true intrinsic performance of Li battery systems. His future goals include constructing a “smart coin cell” that dynamically reports internal cell parameters. Kyle’s work seeks to produce new mechanical platforms that could accelerate battery research and advance clean-energy technologies.

Mumin Jin
Electrical Engineering and Computer Science

Mumin Jin is a PhD candidate whose research integrates signal processing, machine learning, and array processing to significantly expand the capabilities of sensing systems for radio frequency, acoustic, and other modalities for a host of contemporary applications. Specifically, Mumin is applying machine learning to problems in sparse signal approximation and array processing, and working to train neural networks to enhance automotive radar imaging in autonomous vehicles. A key element of her current research, supported by a MathWorks Fellowship, is her use of machine learning to create powerful high-dimensional priors on the scenes being imaged by radar systems. MathWorks toolkits including Phased Array and Autonomous Driving are essential to her work, which has yielded additional tools useful for the radar community. Mumin’s innovative research has the potential to significantly advance the use of machine learning in sparse signal reconstruction and array processing problems in autonomous vehicles and a variety of other applications, and inspire other researchers to explore strategies for integrating machine learning and signal processing.

Kruthika (Kru) Kikkeri
Electrical Engineering and Computer Science

Kruthika (Kru) Kikkeri is a PhD candidate whose research is focused on developing fast, affordable point-of-care (PoC) systems for the detection of biomarkers. Specifically, she utilizes microfluidics, electronics, and machine learning to create a tunable, inexpensive, and user-friendly sample-to-answer PoC system with clinically relevant analytical capabilities and rapid turnaround times. Building on previous research, Kru has made important contributions by developing the upstream processing to move from blood acquisition to electrochemical detection and is now focusing on decreasing costs. A MathWorks Fellowship will support her ongoing work to significantly reduce device costs and fabrication times by integrating tape-based microchannels with inkjet-printed multiplexed electrodes. She will also work on an intelligent control model enabling adaptable detection of biomarkers for more personalized therapies. MathWorks has been a crucial resource in Kru’s efforts to design next-generation PoC systems. Her work has the potential to advance personalized and holistic disease detection and management.

Nicholas King
Chemical Engineering

Nicholas King is a PhD candidate whose primary interests are in transport phenomena and soft matter and in creating computational models for medical applications. His current research, supported by a MathWorks Fellowship, explores disease-fighting leukocytes (white blood cells) in the immune system with the goal of advancing the development of leukocyte-based drug carriers. Specifically, Nicholas is developing a new biophysical model of leukocyte extravasation, which describes the migration of leukocytes from the bloodstream to infected tissues during inflammation. This model will be valuable in expanding our understanding of immune mechanisms, precisely identifying interventions to regulate immune responses, and efficiently exploring large parameter spaces for drug design to accelerate experimentation. MATLAB plays a central role in Nicholas’s work. His research has the potential to advance the promising fields of biomedical fluid mechanics and personalized biomedicine, with significant benefits to human health.

Ashutosh Kumar
Materials Science and Engineering

Ashutosh Kumar is a PhD student whose research investigates the interactions between cancer and the bacteria that inhabit certain cancer cells and cancer tumors and the role that bacteria may play in chemotherapy resistance and modulating responses to immunotherapy. Specifically, Ashutosh is researching bacteria associated with ovarian cancer, with the goal of improving detection and treatment for this lethal form of cancer through a deeper understanding of this population of bacteria. Supported by a MathWorks Fellowship, Ashutosh will apply a combination of experimental techniques, microbiome sequencing, and artificial intelligence to attempt to predict microbial-based biomarkers and cluster them based on cancer types to understand changes in the microbiome, which can be correlated to poor patient outcomes. His research, which draws heavily on MathWorks toolkits, is yielding novel observations on the effects of bacteria on cell growth, cell adhesion, migration, and cytoskeleton organization and has the potential to offer additional new insights into the mechanics of cancer metastasis, for improved diagnostics and therapeutics.

Eunseok Lee
Electrical Engineering and Computer Science

Eunseok Lee is a PhD candidate whose research interests focus on the use of terahertz (THz) integrated circuits and systems for energy-efficient hardware security. With the commercial adoption of millimeter-wave spectrum in 5G connectivity and automotive radar sensing, there is growing interest from industry and the research community in hardware that utilizes the (THz) spectrum (0.1~10 THz) for beyond 5G applications. Eunseok’s research seeks to develop THz semiconductor chips, a vital component of those technologies; specifically, he is focused on the miniaturization and security of wireless nodes. With the support of a MathWorks Fellowship, Eunseok will advance his successful work to improve the energy harvesting capacity and energy efficiency of THz semiconductor chips, and to develop a novel, anti-tampering function into THz radio-frequency ID tags for merchandise, using a unique analog fingerprint. MathWorks has been an invaluable tool in his research, which has the potential to offer important contributions to integrated circuits, high-frequency electromagnetics, and hardware security.

Andrea M. Lehn
Mechanical Engineering

Andrea M. Lehn is a PhD candidate whose research integrates analytical science, solid mechanics, and environmental chemistry to investigate microplastics (MP), which pose a serious and growing threat to the health of living things and our planet. With the support of a MathWorks Fellowship, Andrea will continue to refine and evaluate her novel physical model for predicting the generation of MP pollution from the fragmentation of plastic debris; this model accounts for environmental conditions, including UV exposure and changes in material properties to the generation of MP-sized particles, not addressed by current studies. Andrea is also exploring new mechanisms connecting UV exposure and fracture mechanics that may deepen our understanding of MP formation. MathWorks tools have enabled the development of analytical techniques and experimental protocols underlying this work. Andrea’s research has the potential to greatly improve our ability to predict the fate of plastics in the environment, guide product design to mitigate MP pollution, and inspire further interdisciplinary research.

Lexy LeMar
Chemical Engineering

Lexy LeMar is a PhD candidate in atmospheric chemistry whose primary research interests are in the process of oxidation. Specifically, Lexy studies the oxidation of volatile organic compounds (VOCs) in the atmospheric aqueous phase through laboratory research and MATLAB-based mechanistic modeling. With the support of a MathWorks Fellowship, she will utilize her novel model to obtain kinetic parameters from chamber and bulk aqueous phase experiments and gain new mechanistic insights on oxidation pathways. She is also developing a model for multiphase experiments involving gas-phase and aqueous aerosol-phase kinetics, with the goal of obtaining rates and products of individual steps from the time-dependent concentrations of a given chemical system. Lexy’s work has great potential to expand our understanding of the atmospheric chemistry of particle formation and growth, processes with profound impacts on air quality, human health, and climate.

Peter Zhi Xuan Li
Electrical Engineering and Computer Science

Peter Zhi Xuan Li is a PhD candidate conducting interdisciplinary research at the intersection of robotics and low-power computing hardware, to enable energy-efficient computing for augmented reality, virtual reality, and autonomous navigation. His paradigm-shifting research addresses a key challenge in efficient computing, which is the cost of storing and accessing data from memory, by taking a memory-centric approach to designing algorithms and computing hardware. A MathWorks Fellowship will support Peter’s promising work in several exciting projects, including a new localization and mapping system-on-chip (SoC) that is highly energy efficient, robust to operating in challenging dynamic environments, and able to produce and store high-quality 3D maps in real- time. By co-designing new algorithms and specialized hardware, the SoC efficiently determines the position and orientation of any device using only visual and inertial measurements to enable a variety of exciting applications, from extended reality on wearable headsets to autonomous navigation of micro-robots. Peter’s research, which makes extensive use of MathWorks tools, offers exciting advances in energy-efficient computing and has great potential to power research and technology development at the intersection of many fields, including robotics, computer architecture, and circuit design.

Tianhong Li
Electrical Engineering and Computer Science

Tianhong Li is a PhD candidate whose research utilizes machine learning (ML) approaches in the development of novel sensing systems. His research has provided important new insights into ML problems, such as learning from unbalanced datasets and improving the robustness of self- supervised learning, enabling it to deal with new modalities. A MathWorks Fellowship will support Tianhong’s work on several exciting projects. The first is a groundbreaking system that provides highly accurate human pose estimation through walls and occlusions by leveraging the properties of RF signals in Wi-Fi frequencies. The second is RF-Pose3D, the first system that infers 3D human skeletons from RF signals; potential applications include gaming, surveillance, and healthcare. Finally, he is conducting a project in unsupervised learning for signals beyond human perception, to leverage unsupervised learning algorithms to utilize unlabeled RF data for pre-training. Tianhong’s algorithms are already in use to monitor the motion of patients with Parkinson’s disease and other motion disorders, and his wide-ranging work offers tremendous promise to advance unique sensing and ML applications and bring new capacity to MathWorks-driven research.

Liang “Charles” Lyu
Electrical Engineering and Computer Science

Liang “Charles” Lyu is a PhD candidate whose research interests include improving our understanding of social media and, more broadly, the impact of algorithms on humans and society, with the goal of developing effective strategies to mitigate potentially undesirable impacts, such as the spread of misinformation. Specifically, his research explores the interactions between platforms, users, creators, and content; the effects of platform algorithms and decisions on user behavior; and how these elements relate to societal issues. Supported by a MathWorks Fellowship, Charles will design new algorithms and models to address challenges in real-world scenarios, such as the interplay between social media engagement and diversity, and broader-level work, such as the creation of a framework to examine how taxation of social media advertising might affect behaviors. MATLAB tools are an important component of his work, which has the potential to deepen our understanding of the complex interactions between people and algorithms, maximizing benefits while guarding against possible negative outcomes.

Chika Maduabuchi
Nuclear Science and Engineering

Chika Maduabuchi is a master’s student whose research integrates the study of heat transfer and computer vision to explore boiling phenomena, a process with important implications in industries from energy to manufacturing. With the support of a MathWorks Fellowship, Chika will expand his path-breaking project to employ MATLAB for the high-throughput analysis of bubbles from high- speed video camera images and to develop novel algorithms to extract key operating parameters, such as the dry area fraction and contact line densities, that quintessential to understanding and improving boiling phenomena. Chika’s innovative approach of combining thresholding algorithms with convolutional neural networks, particularly within the MATLAB environment, represents a significant advancement in data-driven analysis for boiling phenomena. His research has great potential to advance basic knowledge in this sphere of research and could also enhance the safety and efficiency of nuclear reactors and other thermal systems, with broad societal and industrial impacts.

Colin R. Marcus
Electrical Engineering and Computer Science

Colin R. Marcus is a PhD candidate whose research is focused on biomedical applications, with an emphasis on novel sensor platforms and algorithms to enable long-term, personalized healthcare. In recent work, Colin developed a paper-thin, flexible sensor array that can be laminated onto surgical masks and N95 respirators. This innovative technology allows for real-time monitoring of variables such as temperature, humidity, air pressure, and mask fit. Utilizing machine learning techniques, it also provides instant assessments of breathing rate and the position of the mask on the user’s face, among other factors. A MathWorks Fellowship will support Colin’s ongoing work on mechanically adaptive ultrasound patches for large-scale, soft tissue imaging. He has designed a novel ultrasound patch designed specifically for breast cancer detection, enabling individuals to conduct self-scans and capture multiple ultrasound images over several weeks, all without any exposure to ionizing radiation. This technology promises to improve breast cancer survival rates by expanding access to screening with earlier and more accurate screening while lowering costs.

Colin’s exciting research in medical ultrasound imaging and biomedical sensors is yielding valuable new techniques and tools that could move biomedicine forward and improve human health.

Aditya Misra
Institute for Medical Engineering and Science

Aditya Misra is a PhD candidate in medical engineering and medical physics whose research explores inflammation, a central feature of many prevalent human diseases, including ulcerative colitis, diabetes, and sepsis. Specifically, Aditya aims to develop and apply new technological capabilities to better understand how tissues respond and adapt to inflammation, with the goal of identifying interventions to diagnose and reverse the pathophysiology of these diseases. With the support of a MathWorks Fellowship, Aditya will create new frameworks and tools to spatially and functionally characterize tissue immune responses. The MATLAB-based tools he has built in the course of this work will be valuable to the broader immunology research community in exploring how spatial structures and cell circuits evolve, and how they exacerbate or resolve inflammation. Aditya’s work holds great promise for expanding our understanding of how inflammation remodels tissues and their immune profiles and may offer new directions for the diagnosis and treatment of many inflammatory diseases.

Adriana Mitchell
Aeronautics and Astronautics

Adriana Mitchell is a PhD candidate whose research interests are focused on space exploration, and the application of machine learning (ML) techniques to improve space-based optical navigation under variable illumination conditions during entry, descent, and landing on planetary bodies. A MathWorks Fellowship will support her ongoing project to develop terrain-relative navigation applications, drawing on MATLAB, by creating deep learning approaches for this application.

Adriana has worked with fellow researchers at NASA’s Jet Propulsion Laboratory (JPL) to validate and implement an ML method to robustify terrain-relative navigation during entry, descent, and landing under variable illumination conditions on NASA orbital lunar and Mars imagery. She has also collaborated on an ESA project to validate and implement a tool to determine camera and orbit requirements for visual navigation of a CubeSat around a near-Earth asteroid for ESA’s M-ARGO mission. Adriana’s work holds the potential to give rise to ML techniques that advance space exploration and innovations in aerospace engineering.

Moses C. Nah
Mechanical Engineering

Moses C. Nah is a PhD candidate whose research interests lie at the intersection of robotics engineering and motor neuroscience. Specifically, Moses seeks to understand how humans achieve dexterity far superior to modern robots and apply this understanding to bridge the gap between human and robot performance. Through close consideration of the ways in which humans manipulate infinite-dimensional objects with complex dynamics (e.g., a bullwhip), he is testing the hypothesis that humans achieve their remarkable dexterity by using a limited “library” of highly stereotyped actions. Using optimization and computer simulation of several models, Moses has shown that a single such movement was sufficient to achieve accurate target acquisition. MathWorks has been a fundamental tool in Moses’s work, and he is a co-developer of Explicit, an open-source MATLAB-based robotics software, which implements robot kinematic and dynamic equations based on the product-of-exponentials formulation with remarkable computational speed.

A MathWorks Fellowship will enable Moses to expand this successful body of work, which has the potential to help solve challenges in soft robot control and advance research and technology development in robotics at large.

Amin Heyrani Nobari
Mechanical Engineering

Amin Heyrani Nobari is a PhD student whose research explores mechanical design automation and applied machine learning (ML) for mechanical design, with the goal of making major advancements in engineering design and product development. His current work focuses on automating CAD generation using sequential ML models and large language models, introducing ML models for expediting or replacing high-fidelity physics simulations, and designing under constraints using ML. With the support of his third MathWorks Fellowship, Amin will expand ongoing work to create generative models for inverse kinematic design of linkage mechanisms, which involves developing methods that allow algorithms to suggest 2D planar linkage mechanism designs for any specified target. Furthermore, he will be working on developing generative optimization methods combining deep generative models and optimization methods in engineering design problems. To facilitate his work on generative ML models for linkage synthesis, Amin has created an algorithm that simulates mechanisms 800 times faster than previous methods. His work, which makes extensive use of MathWorks tools, has resulted in several important contributions to computational generative design and has great potential to drive future innovations in the engineering field.

Juliet Okorie
Chemical Engineering

Juliet Okorie is a PhD candidate whose research interests focus on characterizing the energy production and storage capacity of electrodes. Specifically, Juliet is working to optimize a scalable electrochemical cell for methane oxidative coupling (MOC), a process that could potentially convert the greenhouse gas methane into higher-value carbon molecules for jet fuel in a clean and energetically feasible way. With the support of a MathWorks Fellowship, Juliet will continue her work to model this green, inexpensive, and selective electrochemical approach to valorizing methane by creating a corresponding mathematical model that predicts key performance metrics of the process in an electrolyzer so that MOC can eventually be scaled up for commercial use.

Juliet’s MATLAB-based model is expected to be broadly applicable for research in electrochemical catalytic processes. By advancing new methods aimed at turning greenhouse gases into safer and higher-value products, Juliet’s research has strong potential to advance cutting-edge bioelectrochemistry and address significant societal challenges.

Alan Papalia
Mechanical Engineering

Alan Papalia is a PhD candidate whose research in ocean engineering and robotics focuses on novel algorithms for certifiably correct robotic perception, a critical capability for real-world deployment of autonomous systems in performing runtime verification of their models and understanding of the world. Specifically, Alan’s research has developed a certifiable state estimation technique to incorporate range measurements into simultaneous localization and mapping (SLAM) systems, dubbed certifiably correct range-aided SLAM, or SLAM CORA.

A MathWorks Fellowship will enable Alan to continue his pursuit of two innovative goals: to develop distributed techniques needed for a swarm of robots to cooperatively navigate while meeting mission objects and under severe communication restrictions and to develop robust algorithms capable of self-certifying that computed solutions are optimal. These algorithms, which were developed as MATLAB libraries and shared as open-source software, have substantial value to a wide range of robotic systems. Alan’s research holds exciting potential to advance the development of robotics systems, including cooperative AUV networks capable of executing complex missions with high integrity.

Jane (Eugene) Park
Materials Science and Engineering

Jane (Eugene) Park is a PhD candidate whose research explores the structure and property control of a new class of two-dimensional magnetic materials with exciting promise for next-generation quantum computing devices. A MathWorks Fellowship will enable Jane to expand this fruitful work, with the specific aims of elucidating the structural and magnetic properties of air-stable 2D magnets (in particular those showing strong anisotropy in-plane) using various microscopy and theoretical modeling techniques and identifying novel quantum architectures that can serve as optimal platforms for next-generation technologies. MathWorks software is an indispensable tool for Jane’s work, supporting detailed analysis of microscopy data and spin structure simulations critical to understanding the resulting properties. Jane’s research has the potential to advance the design and creation of quantum computing technology and support the development of innovative applications such as topological transistors; magnetic tunnel junctions and other data storage devices; and powerful new approaches in quantum simulation.

Joshua David John Rathinaraj
Mechanical Engineering

Joshua David John Rathinaraj is a PhD candidate whose research focuses on soft materials, such as complex fluids and soft solids, that are crucial to many industries, including consumer goods, automotive, and oil and gas production industries. Specifically, Joshua’s work addresses the challenges of characterizing and mathematically modeling the mechanics and rheological properties of soft materials. He accomplishes this by developing advanced signal processing techniques to characterize the material’s temporal and frequency dependent rheological responses, as well as by developing governing constitutive differential equations for modeling the rheological properties. His most recent work involves using Gabor transforms to enable the characterization of the temporal and frequency responses of soft materials. This work extensively utilizes MATLAB, and it is now freely available through MathWorks and is being employed by numerous research groups. With the support of a MathWorks fellowship, Joshua plans to employ the Gabor transform techniques to distinguish viscoelasticity and thixotropy in complex fluids. He also aims to use neural differential equations for modeling complex phenomena, such as the yielding and thixotropy of soft materials.

Sebastián Ruiz-Lopera
Electrical Engineering and Computer Science

Sebastián Ruiz-Lopera is a PhD candidate in the field of biomedical optics whose research combines cutting-edge optical imaging systems with physics-informed post-processing tools to study tissue such as the retina. Specifically, Sebastián is developing novel approaches in optical coherence tomography (OCT), an imaging technique that provides diverse information about the macro- and micro-structure and functioning of tissue. Previously, he created the first technique for high-resolution imaging of polarization parameters of the retinal nerve fiber bundles in a human subject in vivo, which are hypothesized to encode information on axonal degeneration induced by neurodegenerative diseases. MATLAB is a foundational tool in this work, aiding the development of advanced computational methods to make OCT an even more powerful tool. Supported by a MathWorks Fellowship, he will work to develop a robust processing framework for the estimation of tissue contrast with OCT imaging techniques and to explore innovative AI applications with the potential to vastly accelerate this complex signal process and advance valuable new tools for biomedical optics.

Kariana Moreno Sader
Chemical Engineering

Kariana Moreno Sader is a PhD candidate whose research explores new strategies for decarbonizing the trucking sector, a crucial but challenging part of the economy to decarbonize in the interest of mitigating climate change. Kariana’s previous work included investigations of battery electric and hydrogen approaches. Her second MathWorks Fellowship will enable her to extend this work to liquid organic hydrogen carriers (LOHCs), which could be substantially more cost- effective than battery electric and compressed hydrogen and also require only minimal changes to the existing fuel infrastructure. Kariana is developing a new powertrain option featuring onboard hydrogen release, in which engine exhaust heat is recovered to power the dehydrogenation reactor, negating the need for compression or high-pressure onboard storage. MATLAB is a vital tool in Kariana’s research. Her proposed concept, if successful, could transform the trucking industry by offering a cost-competitive alternative to diesel that is simple to implement and globally accessible. Ultimately, Kariana’s work has the potential to advance the decarbonization of long-haul trucking, and the development of other LOHC mobility applications, with significant economic and climate impacts.

Miranda Schwacke
Materials Science and Engineering

Miranda Schwacke is a PhD candidate whose research is focused on developing energy-efficient hardware for machine learning and brain-inspired computing, with the broader goal of reducing the fast-growing energy demands of computing (and associated CO2 emissions) while nurturing technological innovation. Specifically, Miranda is designing and testing electrochemical ionic synapses (EIS), an emerging technology for analog resistive switching in which electrochemically controlled intercalation of ions into and out of a channel allows for dynamic doping of the channel, tuning its resistance. Supported by a MathWorks Fellowship, she aims to expand the types of working ions that can be used in EIS. She also seeks to better understand how channel microstructure impacts device performance, enabling the design of novel, all-solid-state, scalable, fast, energy-efficient, and non-volatile EIS for neuromorphic computing. MATLAB has been instrumental in writing custom scripts for device testing and data analysis. Miranda’s research, integrating materials science and engineering, electrochemistry, and device fabrication and characterization, has the potential to yield important contributions to energy-efficient, brain- inspired computing.

Devosmita Sen
Chemical Engineering

Devosmita Sen is a PhD candidate whose research aims to quantify the topology-property relationships in polymer networks, their fracture properties, structure, and dynamics. A MathWorks Fellowship will support Devosmita’s ongoing work to develop MATLAB-based models of complex polymer networks and create a topological framework for modeling dynamic polymer networks, which can adapt and transform through stimuli-induced bond exchanges. Her goal is to better understand the useful macroscopic properties of these materials, such as self-healing and recyclability. By modeling the crucial aspects of dynamic polymer networks, and extending concepts of graph theory, Devosmita is contributing key new insights in the field. Her innovative research has also served to bring an entirely new area of mathematics (net theory) into polymer research. Overall, Devosmita’s work offers exciting new directions in the study of polymer networks and has the potential to advance the design of novel materials for a wide variety of applications.

Arjav Shah
Chemical Engineering

Arjav Shah is a PhD candidate whose research focuses on developing sterility testing methods for cell and gene therapies for human disease. Specifically, Arjav is working on new strategies to make biomanufacturing safer through the real-time detection of adventitious agents, such as viruses, at various points in the manufacturing process. With the support of his second MathWorks Fellowship, Arjav is pursuing a highly promising novel application of nanopore sensors to fingerprint viruses. The overarching goal of his project is to develop a machine learning (ML) algorithm that identifies a biomolecule based on its ionic current signature as it is pushed through a nanopore in a freestanding membrane between two electrolyte-filled reservoirs. Arjav’s research employs a combination of multi-scale simulations and ML-driven analysis of experimental data. MATLAB is a vital tool in this work, from model formulation to evaluation and optimization. If successful, Arjav’s nanopore sensor could enhance safety and efficacy in the development of therapeutics for a broad range of diseases and reduce the time required for sterility testing from 2-4 weeks to just hours. His work is also generating innovative new MATLAB tools that are a valuable resource for the nanopore research community.

Saba Shaik
Aeronautics and Astronautics

Saba Shaik is a PhD candidate whose research interests concern the detailed electrochemistry that occurs in electrospray thrusters. A MathWorks Fellowship will support her work to introduce novel experimental methods to characterize these processes and collect and analyze vast amounts of data in MATLAB to develop numerical models of relevant physical phenomena. Her current efforts are centered on determining the composition (molecular and atomic) of species generated via electrochemical processes. The data are obtained as time-cascade quadrupole mass spectra diagrams in which different peaks represent different species. A principal objective of Saba’s work is to design efficient physics-based pattern recognition algorithms to manage various confounding factors (for example, some species are generated through ion beam interactions with vacuum chamber walls, not electrochemistry) and extract useful information from large amounts of overlapping data. Saba’s work has tremendous promise to produce valuable tools and approaches that could advance the field and support the efforts of other researchers.

Sabrina C. Shen
Materials Science and Engineering

Sabrina C. Shen is a PhD candidate whose research aims to support a more sustainable future through the computational and experimental design of nature-inspired architected materials. Sabrina’s current work, supported by a MathWorks Fellowship, proposes a new paradigm for materials design that uses pre-existing biomass waste streams to build a novel class of architected composites, drawing inspiration from nature and direction from powerful computational methods. By employing tools in bio-fabrication, computing, and theory, she aims to create functional materials that integrate into natural resource cycles in production, use, and disposal, even when scaled to industrial levels. MATLAB computational tools have been integral to her work, and she anticipates contributing open-source models and functions useful to other researchers working at the intersection of advanced experimental and computational methods. Sabrina’s research has exciting potential to advance work in many fields, from structural bio-composites to material design and manufacturing and to move the world toward a more sustainable future.

YoungIn (Ethan) Shin
Civil and Environmental Engineering

YoungIn (Ethan) Shin is a PhD candidate whose research interests lie at the intersection of computational science and renewable energy systems. Specifically, Ethan seeks to advance fundamental knowledge of the atmospheric boundary layer (ABL) and its influence on wind farms, by developing more accurate parameterizations for turbulence in the ABL. His research leverages data assimilation and uncertainty quantifications methods to address two critical challenges in wind energy: the uncertainty in mesoscale and microscale models used for wind flow modeling and the constraints on observations needed for calibrations at the relevant locations for wind farms (~100m above Earth’s surface). A MathWorks Fellowship will support Ethan’s work to build computationally efficient, low-fidelity MATLAB models, characterizing these important environment-energy system interactions for wind farm applications such as wind farm control and wind resource assessment, and to support critical decision-making for energy systems, such as wind farm siting and design.

Ethan’s research has strong potential to further our understanding of renewable energy systems, advance global decarbonization goals, and provide tools to support future research in climate and energy research.

Kaymie Shiozawa
Mechanical Engineering

Kaymie Shiozawa is a PhD candidate whose research interests focus on the development of diagnostic, assistive, and rehabilitative technologies to support balance for post-stroke patients and other individuals with compromised balance. Specifically, Kaymie is investigating the neural control strategies of balance across patient populations to inform the design of novel rehabilitation devices that provide continuous care in and out of the clinic. Recently, she developed a biomechanically plausible MATLAB-based simulation and compared the outputs against human quiet standing data to quantify the neural control strategy of unimpaired balance. A MathWorks Fellowship will enable Kaymie to extend this work and gain insight into the differences in control strategies across various populations, such as older adults or stroke survivors. Kaymie’s work offers valuable tools to fellow researchers and has tremendous potential to advance research and innovation in assistive and rehabilitative technologies.

Sami Yamani Douzi Sorkhabi
Mechanical Engineering

Sami Yamani Douzi Sorkhabi is a PhD candidate whose research explores the flows of dilute polymer solutions. Specifically, Sami is investigating a commercially important phenomenon called polymer drag reduction (PDR), which is used in oil transport and exploited by marine mammals such as dolphins, who secrete a layer of mucin-rich polymer through their skin. Sami has made field-advancing contributions to the emerging area of turbulent fluid physics called elasto-inertial turbulence and has constructed a novel Schlieren imaging system that enables him to explore turbulent mixing flows involving dilute polymer solutions with unprecedented sensitivity. For the first time, he has employed this system to image the mixing of dilute aqueous polymer solutions into a stagnant domain of water. MathWorks tools are foundational to his work. A MathWorks Fellowship will support his continued explorations of PDR and strategies for optimizing drag reduction while minimizing polymer consumption. His research has great potential to contribute to basic science and to advance applications of PDR for marine vehicles and pipelines, with significant energy savings and environmental benefits.

Lee Strobel
Aeronautics and Astronautics

Lee Strobel is a PhD candidate whose research interests are focused on numerical modeling of physics problems, in particular those related to gas discharge and atmospheric plasma physics. In his work to date, Lee has made notable contributions to streamer discharge physics, a field of great relevance for nonthermal plasma generation, a phenomenon that appears naturally during lightning and other atmospheric electricity events and is key for applications such as plasma-assisted combustion, plasma medicine, water treatment, and CO2 conversion. With the support of his second MathWorks Fellowship, Lee will continue to develop numerical models of these discharges and extend them to capture longer timescales relevant to chemical processing and self-pulsating behavior under DC conditions. His research holds the potential to offer major advances in the understanding of self-pulsating discharges under DC voltage, which would represent a seminal contribution to the field.

Rachel Sun
Mechanical Engineering

Rachel Sun is a PhD candidate whose research is focused on the mechanics of solids and structures as they apply to materials, sustainable technology, and medicine. Specifically, she is exploring acoustic metamaterials and reconfigurable architected materials. Supported by a MathWorks Fellowship, Rachel will continue her promising explorations of the acoustic properties of metamaterials at the microscale, with the goal of designing and analyzing an acoustic braced cubic micro-lattice with bandgaps in the MHz frequency range. This work may advance technology such as microscale waveguides or logic switches and potentially lead to advances in microcomputing and medical ultrasound. In addition, Rachel is engaged in a MathWorks seed project on magnetically actuated reconfigurable architected materials. Her goal is to develop a new class of 3D reconfigurable architected materials and mechanisms with microscale features that exhibit magnetic-field-induced reconfiguration and actuation. MATLAB has been instrumental in Rachel’s research. Her work has the potential to yield valuable new technologies to support next-generation medical and computing devices and smart multifunctional materials.

Anantha Narayanan Suresh Babu
Mechanical Engineering

Anantha Narayanan Suresh Babu is a PhD candidate whose research combines computational modeling, uncertainty quantification, Bayesian learning, and scientific machine learning, with applications to sea ice and ocean dynamics. Sea ice is rapidly changing, and submesoscale currents have recently been found to be ubiquitous in the upper ocean. Anantha’s work seeks to broaden our modeling of these complex, nonlinear sea ice and submesoscale dynamics, by melding fundamental physics with limited data. Supported by a MathWorks Fellowship, he is developing probabilistic models and using Bayesian and machine learning to create sea ice rheologies and submesoscale closure models. These new learning methods can discriminate between existing models, detect missing physics, and discover new dynamics formulations utilizing only sparse, indirect, and noisy measurements. Anantha’s work relies on his custom MATLAB framework and could yield useful numerical tools and schemes for fellow researchers. His work has the potential to impact both ocean and data sciences. Ultimately, his results could be valuable for climate modeling, environmental monitoring and forecasting, marine ecosystem protection, resource management, maritime transport, and policy and decision-making for the future of our planet.

Hao Tang
Materials Science and Engineering

Hao Tang is a PhD candidate whose research explores the use of computational tools to simulate physical systems and provide microscopic insights into the underlying physics and materials design strategy. As a MathWorks Fellow, Hao will advance his highly promising research along four primary lines of inquiry. First, he will study quantum algorithms for machine learning problems where the training dataset is distributed in remote devices. Second, he will conduct quantum transport simulation and phase-field simulation to explore the underlying physics of recently developed denoising technology. Third, he will continue to work in spin defects simulation, which has yielded a novel computational method to calculate the temperature dependence of transition energies in solid spin qubits. Finally, he aims to develop a reinforcement learning-based algorithm for long-timescale atomistic simulation. MATLAB plays a significant role in all aspects of Hao’s work. Through his research, Hao is contributing to the development of advanced tools to simulate physical systems, with the potential for a major impact in the field of computational materials science.

Sunbochen Tang
Aeronautics and Astronautics

Sunbochen Tang is a PhD student whose research interests are in control theory, optimization, and machine learning. Specifically, Sunbochen is developing novel learning-based approaches to control complex dynamical systems with safety guarantees. With the support of a MathWorks Fellowship and drawing on MATLAB and Simulink, he will explore data-driven control methods for safety-critical autonomous systems with uncertain dynamics. The primary objective of his work is to develop efficient control-oriented meta-learning algorithms for systems to adapt to unknown disturbances in complex environments online. Ultimately, Sunbochen aims to develop open-source modular MATLAB scripts and Simulink models to test such learning-based control algorithms in a realistic simulation environment, thereby developing a deeper understanding of their benefits and potential limitations. His research has the potential to improve the data efficiency and safety guarantees of current learning-based algorithms by introducing control-theoretic designs and advancing the development of trustworthy autonomous systems in general.

Wenhui Tang
Mechanical Engineering

Wenhui Tang is a PhD candidate whose research interests lie in understanding complex biological systems using physical and mechanical tools. Specifically, her research focuses on spatiotemporal cellular behaviors in three-dimensional multicellular systems, such as cell migration, packing, and cell-matrix interaction during development and maturation. Wenhui has made extensive use of MathWorks tools in several path-breaking projects exploring cell behaviors, including investigating the role of curvature on collective cell migration and packing in an in vitro model of human lung alveolospheres and their mechanical regulation during development. Her third MathWorks Fellowship will enable her to extend ongoing research, investigating dynamic pattern formation in developing epithelia and human breast cancer models. Wenhui has already contributed valuable new knowledge and tools to her field, and her research has great potential to advance our understanding of cell behaviors and biological systems, with important implications for human health.

Tony Tohme
Mechanical Engineering

Tony Tohme is a PhD candidate whose research interests focus on reliable and interpretable machine learning, with a particular emphasis on white-box modeling and symbolic regression. While supported by his first MathWorks Fellowship, Tony conducted promising work in probabilistic machine learning to improve predictive uncertainty estimation in neural networks for supervised learning tasks. His second MathWorks Fellowship will enable him to pursue several innovative projects in machine learning with the goal of developing fast, accurate, and interpretable white-box modeling techniques. These include exploring the possibility of incorporating symbolic regression in density estimation techniques, invertible maps, and variance reduction methods. MATLAB is a vital tool in Tony’s research, which has the potential to contribute to more reliable and interpretable algorithms and significantly advance state-of-the-art machine learning technology.

Thomas Varnish
Nuclear Science and Engineering

Thomas Varnish is a PhD candidate whose research interests are focused on the study of plasma physics, which underpins fascinating astrophysical phenomena such as solar flares, black hole accretion disks, and pulsar magnetospheres. Specifically, Thomas studies a process called magnetic reconnection, an explosive reconfiguration of magnetic field topology in plasma that rapidly dissipates magnetic energy by heating and accelerating it. Supported by a MathWorks Fellowship, Thomas will investigate this phenomenon experimentally and continue his work on PUFFIN, a new pulsed-power facility at the Plasma Science and Fusion Center that could provide unique capabilities for laboratory astrophysics. As part of his work, Thomas is exploring the use of MathWorks products such as Simulink and MATLAB to carry out a range of tasks key to supporting research on PUFFIN, including circuit modeling, systems control, and data analysis. Through his individual research on other facilities and his work on PUFFIN, Thomas is making important contributions to the field of plasma physics, and helping to create a state-of-the-art facility that could support and inspire cutting-edge research in experimental astrophysics.

Eric Kevin Wang
Mechanical Engineering

Eric Kevin Wang is a PhD candidate whose robotics research focuses on real-time reactive control to advance efficient and natural, autonomous manipulation. Specifically, Eric seeks to develop efficient ways for robots to transport objects through dynamic, nonprehensile movement and remove the inefficiencies of traditional quasistatic models of manipulation. This involves creating new techniques for geometric modeling of dynamic frictional interaction that are amenable to fast trajectory optimization and robust control. With the support of his second MathWorks Fellowship, Eric will continue his efforts to create a new robot arm that is more dynamic and has lower control latency than currently available hardware and develop simulations to demonstrate feedforward acceleration control from an inertial model that can enable these dynamic tasks. MATLAB tools are a core component of this research. By improving robots’ ability to successfully perform difficult dynamic transport tasks, such as throwing, catching, and inverting an object, all while keeping it safely within grasp, Eric’s research has the potential to support major advances in robotics, with broad benefits for industry and the scientific community.

Qiuyuan Wang
Electrical Engineering and Computer Science

Qiuyuan Wang is a PhD candidate whose research interests are in spintronics, a rapidly growing field that leverages the electron’s spin degree of freedom and the associated magnetic moment and offers tremendous potential to improve power efficiency and performance in storage and computing technologies. With the support of a MathWorks Fellowship, Qiuyuan is investigating multiple dimensions of spintronics, including emergent topological materials as new building blocks for spintronic devices and the design of energy-saving computing paradigms utilizing stochastic magnetic tunnel junctions. MathWorks tools such as MATLAB and Simulink have been invaluable in enabling Qiuyuan to study a variety of spin dynamics problems, design and evaluate experiments, and build innovative devices like a stochastic computing hardware accelerator. He plans to share resources developed in the course of his research to inspire others working in the field of post-CMOS computing. Qiuyuan’s work at the leading edge of this field could help bring about pioneering solutions for next-generation electronics.

Reimar Weissbach
Mechanical Engineering

Reimar Weissbach is a PhD and MBA candidate whose research integrates advanced computation, manufacturing, and management science. With the support of his first MathWorks Fellowship, Reimar conducted innovative research in metal additive manufacturing (AM) processes and developed novel spreading strategies for fine and highly cohesive metal powders. His second MathWorks Fellowship will enable him to expand this work to the laser melting process in metal AM to improve the throughput and quality of laser powder bed fusion. The next phases of his work will focus on creating novel processing regimes using thick powder layers and testing his hypothesis that spreading fine/cohesive powders in dense, relatively thick layers can be exploited as an inherent means of process stabilization to increase throughput without sacrificing quality. Reimar’s research holds significant potential to accelerate the adoption of AM by industry by increasing production rates and decreasing costs, all while improving geometrical resolution. His work is also yielding numerous MATLAB-based tools of value to the research community and manufacturing industry.

Christopher Wink
Nuclear Science and Engineering

Christopher Wink is a PhD candidate whose research interests are aimed at advancing the science of inertial confinement fusion (ICF) toward the realization of laser-driven fusion as an unlimited, safe, and clean energy source. Specifically, Chris is working on designing and implementing the next-generation neutron spectrometer for ICF applications at both the National Ignition Facility at Lawrence Livermore National Laboratory and the OMEGA Laser Facility in the Laboratory for Laser Energetics at the University of Rochester. This spectrometer, called the Magnetic Recoil Spectrometer Upgrade (MRSu), is being designed for more accurate measurements of the neutron spectrum from which the yield, ion temperature, and areal density of an ICF implosion could be determined at unprecedented accuracy. With the support of a MathWorks Fellowship, Chris will continue to design and implement the MRSu on OMEGA and the NIF, efforts that heavily rely on MathWorks tools. This work offers vastly improved performance over the current detector suite and could have a significant impact on the mainline ICF programs at OMEGA and the NIF. His work is already yielding important new tools and knowledge in the field of nuclear energy and holds significant potential to advance the goal of clean, safe, abundant fusion energy in the future.

Oliver Xie
Chemical Engineering

Oliver Xie is a PhD candidate whose research interests are focused on block copolymers and the unique properties arising from their self-assembled morphologies. With the support of his second MathWorks Fellowship, Oliver will build on his previous research achievements using MATLAB tools to explore polymer sequence morphology relationships. His objectives include a novel reinterpretation of polymer sequences as not simply a series of chemicals but rather as both a signal with a spectrum, and an image after undergoing functional transforms. He is also investigating how different length scales of interaction in the sequence function can be better represented and manipulated in the Fourier representation. By combining the results of the perturbative study with an inverse-design optimization algorithm using forward or reverse mode automatic differentiation, sequences may be designed based on a desired morphology. Oliver’s work is offering exciting new knowledge and innovative approaches with major impacts on his field. His work has great potential to advance polymer science, and yield valuable tools and insights to power future research.

Duo Xu
Mechanical Engineering

Duo Xu is a PhD candidate whose research focuses on the development of multifunctional, composite materials for space applications. Specifically, he seeks to create materials that meet the performance requirements of this extreme environment, including shielding from ionizing radiation, thermal and mechanical stability under extreme vibrations, and large temperature swings. With the support of a MathWorks Fellowship he will advance on-going research to innovate new materials with the required properties, and fabricate them via additive manufacturing techniques. MathWorks tools support many aspects of this work, including his construction of a computational model to optimize material properties and predict device performance under realistic conditions. Duo’s research has the potential to further critical new technologies for spaceflight and yield valuable open-source tools to model light-matter interactions and heat transport through materials in space conditions. His cutting-edge work has great prospective benefits to industry, the research community, and human space exploration.

Kathleen Yang
Electrical Engineering and Computer Science

Kathleen Yang is a PhD candidate whose research interests lie at the intersection of wireless communications and signal processing. In previous work, Kathleen developed an impulsive modulation scheme that encodes information in both time and frequency, as well as a corresponding compressed sensing receiver. A MathWorks Fellowship will support Kathleen’s current work on GRAND-assisted multi-user detection, which is a technique that combines users’ symbols and codebooks to achieve better detection and error correction. She relies on MathWorks tools for many core aspects of her research, such as utilizing vectorized code to improve the efficiency of her simulations for physical layer and multiple access channel communications. In particular, she heavily uses modulation and error correction-related functions. Kathleen’s research holds exciting potential to advance the field of wireless communication as a whole and to yield tools and strategies that will power the work of many other researchers.

Matthew Yeung
Electrical Engineering and Computer Science

Matthew Yeung is a PhD candidate whose research explores interactions between light and nanostructures for both fundamental research and the development of novel optoelectronic technologies. The focus of his current work, supported by a MathWorks Fellowship, is the development of nanostructured devices that can interact with and measure light fields with sub-femtosecond resolution. These devices enable a critical new tool in visible to near-infrared optical metrology: a sampling oscilloscope for light waves that could offer a fresh time-domain perspective on how light interacts with materials. Matthew’s work has a large number of potential applications, including creating more efficient solar cells, understanding the fundamental energy transfer mechanisms that enable photosynthesis, and characterizing materials with unprecedented sensitivity. MATLAB has been vital in Matthew’s device design, experiment development, and instrumentation interfacing, and he looks forward to sharing the resulting tools with the MathWorks community. His work has the potential to help usher in revolutionary advances in light-based technologies.

Li-Yu Yu
Electrical Engineering and Computer Science

Li-Yu Yu is a PhD candidate whose research seeks to advance the growing field of spectroscopic microscopy, a powerful tool for delivering rich molecular information about an object through the measurement of its spatial and spectral characteristics. Potential applications of spectral microscopy range from medical diagnosis to pharmaceutical research and material identification. Lu-Yu’s current research, supported by a MathWorks Fellowship, addresses the crucial drawback of poor spectral signal-to-noise ratio in conventional dispersive spectrometers and limited spectral tuning capability in conventional laser sources. His goal is to develop a new spectroscopic measurement strategy that leverages illumination encoding and model-based AI to achieve broadband, real-time, super-resolved spatiospectral image reconstruction, offering a platform for advanced functional analysis in a sophisticated multicellular environment. MATLAB has been an essential tool in this work, enabling the simulation of optical physics, optimization, signal processing, and machine learning. Li-Yu’s work has the potential for far-reaching impact in the field of spectroscopic microscopy and could deliver useful new perspectives and open-source data processing tools (including tools to support a core data processing challenge of compressive sensing and spectral unmixing) to advance innovative research across the imaging and sensing, and signal processing communities.

Mingxin Yu
Aeronautics and Astronautics

Mingxin Yu is a PhD candidate whose research is in the fields of robotics and machine learning. As a MathWorks Fellow, Mingxin will contribute to a research program aimed at developing a learning-based method for accelerating motion planning in robot manipulation problems and will help to develop a proposed, new approach that leverages a machine learning-based control barrier functions (CBF) in safety-critical motion planning scenarios to tackle the high sampling complexity of traditional sampling-based motion planning methods. The principal aim of this project is to incorporate CBF into the traditional motion planning methods to steer the system to navigate safely to the goal, while significantly reducing time-consuming sampling tasks and maintaining the guarantees of the original methods. Mingxin’s research has the potential to offer valuable new solutions to a planning problem with significant impact on numerous real-world applications, including manipulation in cluttered environments, multi-arm assembly tasks, and human-robot interaction.

James H. Zhang
Mechanical Engineering

James H. Zhang is a PhD candidate whose research concentrates on efficient, economical strategies for desalinating water using porous, interfacial evaporators. This technology utilizes solar energy to evaporate water from brackish sources wicked inside of a interfacial evaporator. The vapor can then be collected and condensed into clean water. Specifically, James is exploring the thermodynamics of solar-driven evaporation inside porous materials, particularly in the case of super-thermal evaporation rates (2-3 times beyond the thermal limit), to optimize porous materials for use in desalination devices. He has designed a MATLAB-based model to investigate the evaporation process and identify key variables that could be manipulated to maximize evaporation rates. The model, combined with a MATLAB model of light-structure interactions developed by James’s mentee, has the potential to yield a comprehensive understanding of transport phenomena in porous evaporators. A MathWorks Fellowship will enable James to continue his research in super-thermal evaporation rates, which will be critical in the development of desalination technology and could help the growing global need for efficient clean water production.

Ruiqi Zhang
Electrical Engineering and Computer Science

Ruiqi Zhang is a PhD candidate whose research aims to develop novel solar cell technologies to support renewable energy. Specifically, he is devising machine learning (ML) algorithms to predict optimal structures for solar photovoltaic devices that utilize organic-metal-halide perovskites, a class of materials with promising optoelectronic properties for the next generation of solar photovoltaics. Supported by a MathWorks Fellowship, Ruiqi will pursue a research project with the aim of connecting the physical properties of perovskite materials to their operation in completed solar structures. MATLAB is an essential tool in all four phases of this project: photovoltaic device fabrication, data acquisition and processing, physical model development, and ML algorithm development. His predictive work has demonstrated less than 5% between predicted outputs and ground truth performance—a remarkable indicator of potential success. Ruiqi’s work promises to enable rapid, targeted investigations of various novel perovskite compositions and identify the optimal compositions for perovskite solar structures. Ultimately, his work could represent a major step forward in solar energy, helping to meet the need for clean, renewable power.

Tian Zhao
Civil and Environmental Engineering

Tian Zhao is a PhD candidate in the field of environmental fluid mechanics whose research focuses on fluid motion and sediment transport in aquatic ecosystems, which has critical implications for natural habitat provision, water quality protection, flood and erosion mitigation, and carbon sequestration. Supported by a MathWorks Fellowship, Tian is investigating the fundamental hydrodynamic processes in this complex system to understand how aquatic vegetation, like seagrasses or mangroves, interact with flow and sediment and, in turn, how these outcomes affect sediment transport, erosion, and deposition. MathWorks tools enable Tian to work with complex velocity datasets and simulate flow through aquatic canopies with varying geometries. By expanding our understanding of these important earth surface processes, Tian’s work could advance the restoration and building of green infrastructures in coastal and alluvial systems and improve the prediction of restoration outcomes, potentially impacting the lives and livelihoods of millions around the globe.

Xingcheng Zhou
Chemical Engineering

Xingcheng Zhou is a PhD candidate whose research focuses on developing simple, accurate, and economical electrochemical diagnostics, which are especially crucial for healthcare in low-resource settings. Supported by her second MathWorks Fellowship, Xingcheng will extend her productive work on electrochemical sensors to diagnose bacterial and viral infectious diseases by conjugating biomolecules on the surface of electrodes to capture disease biomarkers and convert the capture to an electrical signal. Additionally, she aims to use statistical machine learning models to determine if combinations of signal changes through SWV, CV, chronoamperometry, and electrochemical impedance spectroscopy can decrease false negative rates. MathWorks tools are foundational to Xingcheng’s research, and her shared models could be beneficial to fellow researchers in the electrochemistry community. By providing affordable, accessible, and accurate diagnostic tools, Xingcheng’s work may contribute to improved health and health equity in low-resource settings around the world.