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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:

Davy Deng
Harvard-MIT Program in Health Sciences and Technology

Davy Deng is a graduate student in medical engineering and medical physics, working to model the brain from the ground up. His research centers on Caenorhabditis elegans—a transparent worm with a fully mapped connectome—using it as a platform to understand how nervous systems give rise to behavior. To this end, Davy is developing a high-speed, high-resolution light-sheet microscope that can record voltage activity across the entire nervous system during natural movement. As a MathWorks Fellow, he will pair this imaging system with biologically grounded deep learning models to predict and interpret neural dynamics in real time. Davy uses MATLAB to coordinate microscope hardware, synchronize voltage imaging with behavior tracking, and analyze dynamic neural signals. He also applies MATLAB-based algorithms to build connectome-constrained AI models that mirror the worm’s actual neural architecture. This research could open new pathways in neuroscience and AI by linking whole-brain activity with behavior in a living organism, thereby advancing our understanding of cognition from the level of neurons to networks.

Joshua Marchant
Harvard-MIT Program in Health Sciences and Technology

Joshua Marchant is a graduate student in medical engineering and medical physics working to personalize cancer treatment through advanced imaging and computational modeling. His research focuses on predicting the transport and efficacy of targeted alpha therapies in solid tumors using multiscale MRI and biophysical simulations. As a MathWorks Fellow, Joshua will refine a quantitative imaging framework to quantify key microstructural tissue properties—such as blood flow, vessel density, permeability, and tumor cell size—to estimate patient-specific drug delivery and optimize therapeutic dosing. MATLAB has been a cornerstone of Joshua’s research across diverse domains, from modeling the pharmacokinetics of contrast agents in the lung to simulating heat propagation in laser therapies and acoustic transmission through the skull. He harnesses MATLAB’s machine learning and optimization routines, Image Processing Toolbox, and fast matrix solvers to analyze complex datasets, develop real-time image reconstruction algorithms, and build predictive biophysical models for drug transport. His research could help shift cancer treatment from one-size-fits-all protocols to precision strategies informed by patient imaging, advancing a new frontier in quantitative medicine and image-guided therapy.

Jacob White
Harvard-MIT Program in Health Sciences and Technology

Jacob is a graduate student in medical engineering and medical physics, whose research focuses on the fundamental neuroscience underlying how the brain learns new motor skills. He studies how songbirds—nature’s virtuoso learners—refine complex vocalizations through practice, to identify generalizable neural principles that govern motor learning in humans. Jacob has developed a brain-computer interface for zebra finches that enables direct manipulation and observation of neural activity during vocal learning. This platform allows him to investigate how neural biases are formed at the level of individual neurons, offering new insights into learning, plasticity, and circuit dynamics. MathWorks tools, particularly MATLAB, play a central role in Jacob’s work, supporting real-time neural spike sorting and custom analysis pipelines. As a MathWorks Fellow, he will utilize this system to investigate how specific inputs influence learning trajectories. Jacob’s work could enhance our understanding of learning and recovery after injury, with broad implications for neuroscience, rehabilitation, and neurotechnology.

Frederick Ajisafe
Aeronautics and Astronautics

Frederick Ajisafe is a graduate student in aeronautics and astronautics, developing systems approaches to improve environmental decision-making through satellite-based technologies. His research focuses on municipal landfills, a significant and underregulated source of methane emissions that contribute to global climate risk. Frederick is applying his Environment-Vulnerability-Decision-Technology framework to model the human, technological, and environmental dynamics that shape methane monitoring and management. With his second MathWorks Fellowship, he will collaborate with the city of Rio de Janeiro to explore methane mitigation strategies using real-time satellite data and predictive systems modeling. MATLAB supports his work across data processing, emission estimation, economic impact analysis, and decision space visualization, enabling a robust, multidisciplinary approach. By combining engineering tools with public policy insights, Frederick’s work could shape future strategies for mitigating greenhouse gases, inform the design of satellite systems, and strengthen environmental governance in urban contexts worldwide.

Myrella Cabral
Aeronautics and Astronautics

Myrella Cabral is a graduate student in aeronautics and astronautics, working at the forefront of hypersonic vehicle design. Her research advances understanding of how structural panels behave under the extreme fluid, thermal, and structural loads encountered at hypersonic speeds. Specifically, Myrella is developing computational models to predict nonlinear fluid-thermo-structural responses in compliant panels subjected to high-temperature gradients and unsteady aerodynamics. These models aim to uncover how structural vibrations interact with boundary layers and inform future experimental design. Her current work builds upon experimental studies, leveraging data from Mach 6 wind tunnel experiments. With her second MathWorks Fellowship, Myrella will refine a reduced-order model that integrates unsteady computational fluid dynamics data with structural solvers developed entirely in MATLAB. Her use of MathWorks tools enables the robust analysis of complex aeroelastic phenomena, helping pave the way for safer and more efficient hypersonic systems. Myrella’s contributions could influence vehicle design and testing strategies across aerospace research and defense sectors.

Lilly Etzenbach
Aeronautics and Astronautics

Lilly Etzenbach is a graduate student in aeronautics and astronautics, advancing the design of next-generation rocket engines for reusable launch systems. Her research focuses on liquid propellant rocket engines, modeling trade-offs among performance, reusability, complexity, and cost. As a MathWorks Fellow, Lilly will use a modular simulation architecture built in MATLAB, incorporating the Aerospace Toolbox, Simscape, and SimEvents, to evaluate propulsion system designs under steady-state and transient conditions. She will also apply MATLAB’s Partial Differential Equation Toolbox to model degradation in cooling channels and assess health monitoring strategies using embedded thermocouples and fiber-optic sensors. By capturing relationships among design parameters, refurbishment needs, and operational lifespan, Lilly’s work could guide more efficient design of reusable systems and instrumentation. It could also help lower the cost of access to space while increasing system reliability and sustainability, enabling broader participation in the space economy.

Madison Taylor Hobbs
Aeronautics and Astronautics

Madison Taylor Hobbs is a graduate student in aeronautics and astronautics working to improve communication reliability in hypersonic vehicles. Her research addresses the challenge of plasma-induced blackouts resulting from hypersonic wakes that disrupt signal transmission during high-speed flight. As a MathWorks Fellow, Madison will develop a coupled computational framework that integrates NASA’s LAURA fluid solver with MATLAB-based enhancements to material response modeling, supporting automated geometry adaptation and sensitivity-driven uncertainty quantification. By simulating the geometric and chemical transformations that occur during surface ablation, she aims to generate higher-fidelity predictions of electron density in hypersonic wakes. Her framework leverages MATLAB’s Partial Differential Equation and Image Processing Toolboxes, along with optimization routines from the Statistics and Global Optimization Toolboxes. It also introduces new capabilities for dynamic mesh evolution and ablation visualization. Through this work, Madison seeks to quantify the impact of material response and surface evolution on wake formation and identify the ablation thresholds that trigger ionization. Her contributions could advance hypersonic vehicle design and enable more resilient in-flight communication systems.

Kyle Horn
Aeronautics and Astronautics

Kyle Horn is a graduate student in aeronautics and astronautics, developing technologies to support long-duration robotic exploration of Venus. His research focuses on using solid oxide electrolysis (SOE) to generate buoyant gases from the planet’s dense carbon dioxide atmosphere, enabling aerial platforms to remain aloft for extended missions. As a MathWorks Fellow, Kyle will use MATLAB to build a comprehensive system model that integrates physical principles with empirical lab data, allowing him to simulate performance, evaluate trade-offs, and identify Pareto-optimal designs. He applies MATLAB’s Optimization Toolbox to explore mission design variables and uses data visualization tools to communicate results across technical and nontechnical audiences. Kyle also relies on MATLAB to process experimental data on SOE efficiency and degradation under Venus-like conditions. Kyle’s research could redefine the feasibility of long-duration missions in hostile planetary environments, opening new frontiers in space science and exploration.

Nathanael Jenkins
Aeronautics and Astronautics

Nathanael Jenkins is a graduate student in aeronautics and astronautics, where he is advancing simulation-based methods to improve lightning protection for next-generation aircraft and wind turbines. His research focuses on developing physics-based models that simulate lightning interactions with unconventional aircraft geometries, such as blended-wing bodies and truss-braced wings. Nathanael is creating a computational toolkit to automate the lightning zoning process, which helps determine where protective materials must be placed on an aircraft’s surface. As a MathWorks Fellow, he will explore the use of Simulink and MATLAB’s App Designer Toolbox to implement rotorcraft dynamics and deliver accessible tools for engineering use. His work will also incorporate high-fidelity simulations and validation against experimental data. By advancing both the scientific understanding and practical application of lightning zoning, Nathanael’s research could lead to safer and more efficient aircraft design, as well as inform new industry standards for the aerospace and renewable energy sectors.

Youngjae Min
Aeronautics and Astronautics

Youngjae Min is a graduate student in aeronautics and astronautics whose research focuses on creating rigorous machine learning and control techniques for safety-critical systems. His work tackles the challenge of enabling autonomous systems to operate reliably in uncertain and rapidly changing environments—an essential requirement for applications such as advanced air mobility, smart grids, and robotic systems. Youngjae created HardNet, a neural network architecture that guarantees constraint satisfaction while maintaining universal approximation capabilities, offering a powerful foundation for learning-based control with built-in safety assurances. As a MathWorks Fellow, he will design machine learning algorithms with provable performance and embed control certificates directly into training protocols to enforce verifiable safety across time. MATLAB, along with the Control System and Robust Control Toolboxes, is instrumental to his work, supporting modeling, simulation, and formal verification. Youngjae’s research could enable a new class of adaptive, trustworthy autonomous systems, with broad implications for industries that demand both intelligence and safety from their technologies.

Bria Morse
Aeronautics and Astronautics

Bria Morse is a graduate student in aeronautics and astronautics, applying aerospace physiology to improve medical outcomes during emergency evacuations. More specifically, her research examines how environmental stressors—such as vibration, acceleration, and reduced pressure—impact patient stability during both air and ground transport, particularly in hemorrhagic conditions. As a second-time MathWorks Fellow, Bria will develop a physiological algorithm to support real-time decision-making in casualty evacuation, including autonomous and unmanned missions. She works with complex datasets from field experiments and simulations, using MathWorks tools—especially MATLAB—for signal processing, time-series analysis, and visualization of hemodynamic trends. Under the fellowship, she will enhance these workflows to prototype and validate her model, combining physiological data with environmental factors. Bria’s work could transform evacuation protocols across military, disaster, and civilian contexts, improving survival rates through data-driven transport strategies.

Daniel Sharp
Aeronautics and Astronautics

Daniel Sharp is a graduate student in aeronautics and astronautics, where he is breaking new ground in uncertainty quantification for complex scientific and engineering systems. His work focuses on transport-based approaches to quantization and Bayesian inference, which provide scalable, mathematically grounded methods for enhancing prediction accuracy in high-dimensional models with sparse data. As a MathWorks Fellow, Daniel will exploit the capabilities of MParT—the Monotone Parameterization Toolkit he codeveloped—by using robust MATLAB integrations that support flexible modeling pipelines and provide an intuitive user interface. He utilizes MATLAB and the Partial Differential Equation Toolbox in conjunction with custom MATLAB executable functions to accelerate computations, test new algorithms, and facilitate adoption by researchers and practitioners. Daniel’s approach could streamline the use of advanced inference techniques, supporting faster, more reliable decision-making in areas such as aerospace simulation, energy systems, and environmental modeling.

Anna Wadhwa
Aeronautics and Astronautics

Anna Wadhwa is a graduate student in aeronautics and astronautics and a medical student in the Harvard-MIT Program in Health Sciences and Technology. Her research examines the impact of spaceflight on the human body. Using brain organoids to model the brain in microgravity, she aims to uncover the biological mechanisms behind cognitive and sensorimotor decline during extended space missions. As a MathWorks Fellow, Anna will use MATLAB to process electrophysiological signals, quantify neural morphology, and analyze the effects of simulated microgravity. By integrating experimental neuroscience with aerospace engineering, her work is on track to guide the development of countermeasures that protect astronaut health, inform the design of future space missions, and expand our understanding of how gravity, or its absence, shapes human biology.

Maison Clouâtré
Aeronautics and Astronautics

Maison Clouâtré is a graduate student in aeronautics and astronautics whose research is redefining how we understand and control quantum systems. At the intersection of control theory and quantum mechanics, Maison is helping to establish a new mathematical framework—quantum information control—that uses statistical information measures to guide the design of high-performance quantum sensing, communication, and computing networks. Through a MathWorks Fellowship, he will continue developing computational tools that quantify the performance limits of quantum inference systems. In his work, Maison leverages MATLAB’s Control Systems and Optimization Toolboxes to solve complex design problems in quantum estimation and detection. He is proposing “dynamical information measures”—metrics that reveal how much information can be extracted from a quantum system—and is developing a MATLAB toolbox for computing them. This foundational research could accelerate the practical deployment of quantum technologies across various applications, including sensing, communication, and computing. By bridging disciplines with rigor and creativity, Maison is helping lay the groundwork for the next generation of quantum-enabled technologies.

Fiona (Yihan) Wang
Biological Engineering

Fiona (Yihan) Wang is a graduate student in biological engineering, developing programmable, bioinspired materials that integrate metal coordination and peptide design. Her research integrates molecular design, deep learning, and computational modeling to create peptide-metal networks that respond to both biomechanical and biochemical cues, with applications in drug delivery, tissue scaffolds, and mechanosensitive diagnostics. As a MathWorks Fellow, Fiona will utilize MATLAB to construct, execute, and optimize atomistic simulations of dynamic metal-ligand interactions, automate molecular dynamics pipelines, and develop models that learn structure-function relationships. She uses MATLAB’s Symbolic Math Toolbox to define and manipulate molecular building blocks, the Image Processing Toolbox to extract structural features from simulation snapshots, and the Control System Toolbox to develop closed-loop routines that adjust simulation conditions. These tools enable motif construction, stress-strain analysis, and tracking of bond rupture under force. Fiona’s work could open the door to adaptive metal-peptide systems that sense biomechanical signals and trigger programmable responses, advancing next-generation biomaterials for precision medicine.

Marieke De Bock
Chemical Engineering

Marieke De Bock is a graduate student in chemical engineering whose research addresses the urgent need for efficient and scalable production of RNA-based therapeutics. Her research focuses on optimizing in vitro transcription systems to reduce impurities, improve poly(A) tail uniformity, and lower reagent costs—challenges that are critical to enabling broader access to mRNA therapies. Marieke has developed novel mechanistic models to understand and minimize the formation of double-stranded RNA and applied population balance models to analyze poly(A) tail heterogeneity. To validate her work, she analyzed data from high-performance liquid chromatography files and streamlined the extraction by creating a MATLAB tool. As a MathWorks Fellow, she will expand her work to include immobilized DNA in vitro transcription reactors, using MATLAB for kinetic modeling, analysis, and finite element simulations. She will employ built-in numerical solvers and the Finite Element Method Operations Toolbox to explore flow dynamics and optimize reactor design. Marieke’s work could dramatically reduce production costs and help make advanced gene therapies more accessible worldwide.

Daniela Cavazos Elizondo
Chemical Engineering

Daniela Cavazos Elizondo is a graduate student in chemical engineering, where she is accelerating progress in low-cost diagnostic technologies for global health. Her work focuses on engineering non-immunoglobulin binding proteins to improve the stability, scalability, and analytical performance of paper-based biosensors—tools that hold promise for point-of-care testing in resource-limited settings. As a MathWorks Fellow, Daniela will use MATLAB to automate image analysis, conduct statistical comparisons, and generate curve-fitting models that evaluate biosensor function over time. These capabilities will allow her to process large datasets efficiently and validate diagnostic performance under realistic conditions. Daniela’s work could help overcome key barriers to translating biosensor prototypes into deployable tools, including limited shelf life and inconsistent results in complex samples such as blood. By integrating protein engineering with data-driven analysis, she is poised to strengthen the reliability of low-cost diagnostics and contribute to improved health outcomes in underserved communities.

Jennifer Fang
Chemical Engineering

Jennifer Fang is a graduate student in chemical engineering, creating advanced diagnostic tools to understand how plants respond to infection at the molecular level—work that could transform early detection and treatment of plant diseases. Her research centers on mapping microRNA (miRNA) expression in situ to reveal how different cell types within a plant leaf respond to bacterial pathogens. Jennifer designed a nanoliter-scale platform that isolates and labels miRNA for imaging, enabling the construction of high-resolution spatial maps of gene regulation during infection. She uses MATLAB to automate image processing, generate spatial heatmaps, and apply statistical models to identify patterns of resistance or susceptibility. As a MathWorks Fellow, Jennifer will refine this platform through spectral multiplexing and incorporate spatial machine learning techniques to uncover disease signatures across tissue types. These innovations could accelerate crop protection strategies and have broader applications in cancer diagnostics and biomedical imaging.

Gokulnath Ganesan
Chemical Engineering

Gokulnath Ganesan is a graduate student in chemical engineering, propelling forward platform technologies to improve the design and scalability of cell therapies. His work focuses on developing customized implantable devices that integrate seamlessly with host vasculature, addressing a major challenge in regenerative medicine. As a MathWorks Fellow, Gokulnath will create a MATLAB-integrated development pipeline that automates the design, simulation, and fabrication of multifunctional therapeutic devices. He will utilize Simscape, PDE Toolbox, and Image Processing and Machine Learning toolboxes to simulate design criteria, such as fluid flow profiles and nutrient gradients, and thereby guide 3D printing protocols to fabricate optimized devices. The goal of this fully integrated system is to accelerate design iteration, reduce production costs, and support spatially tailored device architectures. Gokulnath’s work could enable more effective and scalable cell therapies for a range of diseases, setting new standards in biomedical engineering.

Jacob Sass
Chemical Engineering

Jacob Sass is a graduate student in chemical engineering breaking new ground in the simulation, optimization, and control of battery systems. His research tackles the growing need for more reliable, efficient, and interpretable models that support decision-making in battery design and operation. Jacob focuses on integrating data-driven and physics-based approaches to improve the performance and lifespan of lithium-ion batteries. He has developed interpretable machine learning models to predict battery aging from manufacturing data, utilizing MATLAB tools such as fscmrmr and fitrgam for feature selection and model training. He currently builds high-fidelity physics-based battery simulations. As a MathWorks Fellow, Jacob will build efficient implementations of multiphase battery models for real-time control applications, comparing the results with those from the MATLAB battery simulation package, Li-ION SIMulation BAttery Toolbox. His work could inform optimal charging strategies for electric vehicles while minimizing degradation and improving battery lifespans in high-rate scenarios.

Shakul Pathak
Chemical Engineering

Shakul Pathak is a graduate student in chemical engineering dedicated to accelerating battery innovation through advanced modeling. His research focuses on lithium-ion battery performance, with an emphasis on the interplay between materials, electrochemical behavior, and wiring architecture that determines key metrics, such as capacity, efficiency, and lifetime. Shakul develops physics-informed, computationally efficient models of lithium-iron-phosphate and related chemistries, aiming to uncover the fundamental performance limits of next-generation batteries. As a MathWorks Fellow, he will refine a newly developed structurally aware model, integrating experimental data to improve predictive accuracy and guide electrode design. He uses MATLAB extensively to solve coupled differential equations, conduct optimization, and simulate electrochemical behavior under varying conditions. Shakul’s research could help transform battery development workflows, enabling the creation of fast and accurate digital twins for real-time control and diagnostics in electric vehicles and grid-scale energy systems.

Anish Sukumar
Chemical Engineering

Anish Sukumar is a graduate student in chemical engineering focused on improving the sustainability of steel production by advancing low-emission methods for iron extraction. His research focuses on acidic electrowinning, a process that enables the selective recovery of metals from waste streams through electrodeposition. Through a combination of electrochemical testing and continuum-scale modeling, Anish investigates how thermodynamics, reaction kinetics, and species transport influence deposition efficiency and selectivity. His work integrates spatially resolved concentration profiles and mechanistic modeling to understand and overcome challenges such as hydrogen co-evolution and the presence of interferent species. As a MathWorks Fellow, Anish will use MATLAB to develop numerical and analytical models of nucleation and growth dynamics, optimize system parameters, and simulate competing electrochemical reactions. These models hold the promise to support the design of scalable, energy-efficient recovery systems compatible with renewable energy inputs. Anish’s research could inform new strategies for low-carbon metal refining and contribute to a more circular and sustainable industrial economy.

Wallace Tan Gian Yion
Chemical Engineering

Wallace Tan is a graduate student in chemical engineering and computational science and engineering, where he develops advanced stochastic control algorithms for complex chemical systems. His research focuses on stochastic model predictive control (SMPC) and uncertainty quantification using polynomial chaos expansions, particularly for systems governed by partial differential equations. Wallace designs control frameworks that preserve theoretical guarantees while enabling real-time decision-making under parametric and dynamic uncertainty. As a MathWorks Fellow, Wallace will use MATLAB to implement SMPC algorithms, simulate closed-loop control systems, and validate performance under stochastic disturbances. He leverages the Symbolic Math, PDE, and Optimization Toolboxes to prototype control architectures, perform uncertainty quantification, and solve large-scale optimization problems. Wallace’s work could significantly improve the robustness and scalability of control strategies in biomanufacturing, pharmaceutical production, and other industries where high-dimensional uncertainty poses a barrier to automation and quality control.

Brandon Tapia
Chemical Engineering

Brandon Tapia is a graduate student in polymer science and engineering, where he is conducting research in sustainable materials for industrial gas separations. His work focuses on understanding and improving the long-term stability of polymer membranes, specifically polymers of intrinsic microporosity (PIM), used to separate gas mixtures more efficiently than traditional energy-intensive methods like distillation. Brandon investigates how structural and morphological features of PIMs affect gas sorption and diffusion, particularly as membranes age over time. With this second MathWorks Fellowship, he will combine high-throughput experimental testing with computational modeling to uncover structure–property relationships that guide material design. MathWorks tools, including MATLAB’s Optimization Toolbox, Partial Differential Equation Toolbox, and Statistics and Machine Learning Toolbox, are essential to simulating transport phenomena, performing data-driven analysis, and managing large-scale datasets. Brandon’s research could enable more efficient membrane technologies, helping to reduce emissions and energy use in the chemical industry while accelerating the transition to more sustainable manufacturing practices.

Jarrett Turner
Chemical Engineering

Jarrett Turner is a graduate student in chemical engineering who seeks to deepen our understanding of fluids under extreme nanoconfinement—an area with wide-reaching implications for chemical separations, nanofluidics, and materials design. His current research focuses on developing an equation of state for confined fluids and applying this framework to study mass transport through nanopores and nanotubes. Jarrett uses MATLAB extensively to model the thermodynamic and transport behavior of fluids in these systems, leveraging built-in ordinary differential equation solvers, hypergeometric functions, and surrogate optimization techniques. These tools enable him to explore complex, nonlinear systems, validate physical models, and refine theoretical predictions using experimental data. As a MathWorks Fellow, Jarrett will advance simulation techniques for nonequilibrium statistical mechanics in nanoscale systems. His work could inform the design of next-generation membranes and chemical reactors by shedding light on how confinement alters fluid behavior at the molecular level.

Isabella Bowland
Chemical Engineering

Isabella Bowland is a graduate student in chemical engineering working to develop renewable pathways for producing high-value aromatic amines—key ingredients in pharmaceuticals, dyes, and industrial materials. Her research centers on engineering Escherichia coli to convert glucose into these compounds through customized metabolic networks. Under the MathWorks Fellowship, Isabella will build on this work by refining pathway design and advancing predictive models to support industrial-scale fermentation. To support her research, Isabella draws heavily on MATLAB and its suite of toolboxes. She uses COBRA and GECKO to model genome-scale networks, simulate enzyme-constrained kinetics, and pinpoint rate-limiting steps. Additionally, she utilizes MATLAB’s numerical solvers to optimize bioreactor configurations, thereby bridging laboratory-scale discoveries with process development. Through this integrative approach, Isabella’s work could offer a viable alternative to petrochemical-based synthesis, advancing more sustainable and scalable bioproduction strategies.

Seamus Frey
Civil and Environmental Engineering

Seamus Frey is a graduate student in civil and environmental engineering, driving forward research in atmospheric chemistry and climate modeling. His work addresses a growing global need: understanding how volatile organic compounds (VOCs) contribute to particulate matter, a pollutant responsible for millions of premature deaths annually and a major source of climate uncertainty. Seamus’s research focuses on the oxidation of VOCs by chlorine radicals, a poorly understood but globally relevant atmospheric process. As a MathWorks Fellow, he will design laboratory experiments and mechanistic models to simulate and quantify the chemical pathways of chlorine-initiated oxidation and secondary organic aerosol formation. MATLAB is central to this work, enabling Seamus to build and run detailed kinetic models and simulate reaction conditions using the Framework for 0-D Atmospheric Modeling. His research could reshape how global models treat atmospheric oxidation, particularly in remote marine and polar regions, and inform emerging climate interventions aimed at reducing methane through enhanced atmospheric oxidation.

Laxman Kafle
Civil and Environmental Engineering

Laxman Kafle is a graduate student in civil and environmental engineering elevating the field of clean energy innovation. His research focuses on electrical rock fracturing, an emerging alternative that uses high-voltage electric pulses to create and/or extend fractures underground. This method could offer an environmentally friendly alternative to hydraulic fracturing, especially for enhanced geothermal systems. As a MathWorks Fellow, Laxman will lead a novel set of experiments using the electric pulse power setup with triaxial loading capability, along with high-resolution diagnostics, to capture the dynamic progression of rock fracture. MATLAB is foundational to his work: he applies it to calibrate transducers, analyze acoustic emissions, and perform digital image correlation, extracting high-fidelity insights from time-resolved fracture data. He uses the Image Processing and Statistics Toolboxes to quantify damage evolution. His integrated experimental and computational approach aims to improve our understanding of rock fracturing and help advance the design of next-generation geothermal energy systems.

Xinling Li
Civil and Environmental Engineering

Xinling Li is a graduate student in civil and environmental engineering whose research lies at the intersection of AI, optimization, and urban mobility. She designs algorithms that enable large-scale, on-demand transportation systems to operate efficiently and equitably in the face of uncertainty. With a focus on ride-sharing networks that serve large networks with dense demand, Xinling combines multi-agent reinforcement learning with robust optimization to develop adaptive strategies for vehicle dispatching and rebalancing. As a MathWorks Fellow, she will use MATLAB’s toolboxes to model and simulate complex agent interactions under dynamic traffic conditions. Xinling has leveraged MATLAB throughout her academic journey, from prototyping coverage-control algorithms and solving vehicle-routing problems to building real-time visualizations of fleet behavior. Her work is powered by MATLAB’s Optimization, Statistics, Image Processing, and Parallel Computing Toolboxes, which enable rapid iteration, model interpretability, and computational scalability. Xinling’s work paves the way for the future of intelligent urban mobility, with the capacity to transform how cities design, operate, and scale more inclusive transportation systems.

Simone Peter
Civil and Environmental Engineering

Simone Peter, a graduate student in computational science and engineering, is reimagining the design of concrete buildings to significantly reduce their embodied carbon footprint. Her research combines structural mechanics, optimization theory, and machine learning to explore new, buildable geometries that utilize less material without compromising performance. As a MathWorks Fellow, Simone will expand her topology and shape-optimization framework by incorporating symbolic computation and automatic differentiation to customize reinforced-concrete manufacturability constraints and train AI models that accelerate the prediction of efficient slab designs. She has already implemented a simulation and optimization pipeline that evaluates and improves beam–slab networks, incorporating structural logic and manufacturing constraints to guide practical outcomes. By leveraging MATLAB’s unique integration of symbolic computation, numerical solvers, and AI tools, Simone is creating a scalable, multi-component design methodology. Her work could revolutionize sustainable building design, providing engineers with powerful new tools to reduce emissions at the structural level.

Michelle S. Zhang
Civil and Environmental Engineering

Michelle Zhang is a graduate student in civil and environmental engineering, investigating how land–atmosphere interactions shape the evolution of weather and climate between storms. Her research focuses on the thermodynamic processes that govern moist convection, using remote sensing to identify the surface and atmospheric drivers of convective instability. As a MathWorks Fellow, Michelle will continue her work by implementing a suite of MATLAB-based workflows for data processing, visualization, and algorithm development to understand the land surface’s influence on atmospheric heat and moisture, ultimately leading to a better understanding of the next precipitation event. She is also developing an open-source MATLAB toolbox to compute convective available potential energy and simulate parcel ascent using observations, filling a critical gap in earth and atmospheric research infrastructure. By uniting physical theory with reproducible, observation-driven analysis, Michelle’s work could enable an observational benchmark that informs models for weather forecasting, climate predictions, and provides broader access to advanced diagnostic tools across Earth system science.

Xibi Chen
Electrical Engineering and Computer Science

Xibi Chen is a graduate student in electrical engineering and computer science working to advance terahertz (THz) integrated electronic systems for ultra-high-resolution imaging and next-generation wireless communication. His research focuses on developing beam-forming and steering devices, as well as transceiver systems that operate at sub-THz frequencies, integrating custom-designed CMOS chips, antenna-in-package technologies, and novel duplexing techniques to achieve sharp, “needle-like” beams and low-loss signal transmission. As a MathWorks Fellow, Xibi will further develop algorithms and real-time control systems to optimize THz system performance, demonstrating centimeter-scale radar imaging and wireless links exceeding 100 meters. His work relies heavily on MATLAB, utilizing the platform to build unified system interfaces, run phased-array simulations, and develop graphical user interfaces for real-time beam steering and data visualization. These innovations could reshape 4D imaging and communication technologies for autonomous systems, high-speed networking, and satellite communications. With his prototype, which contains custom-designed, fully integrated sub-THz transceiver and antenna array systems, Xibi’s research could help deliver LiDAR-comparable angular resolution and unlock the full capacity of THz frequencies.

Fathima Zarin Faizal
Electrical Engineering and Computer Science

Fathima Zarin Faizal is a graduate student in electrical engineering and computer science, where she is advancing theoretical foundations for decision-making under uncertainty. Her research focuses on decentralized learning in multi-agent systems, with a particular emphasis on game-theoretic interactions and stochastic optimization. As a MathWorks Fellow, she will investigate efficient but meaningful learning dynamics for strategic agents in complex, networked environments. Currently, her work provides the first-known polynomial-time finite-sample guarantees for convergence to approximate Nash equilibria in certain zero-sum and polymatrix games using best-response type dynamics. Fathima uses MATLAB extensively to validate her theoretical results with Monte Carlo simulations; its graph theory toolbox and fast matrix computations enable her to run smoother simulations. Her work could inform the design of robust learning protocols in social networks and distributed systems. Fathima hopes to deepen our understanding of strategic learning and to shape how large-scale, uncertain environments are modeled and managed.

Alina Harbuzova
Electrical Engineering and Computer Science

Alina Harbuzova is a graduate student in electrical engineering and computer science whose work addresses foundational questions at the intersection of statistics, machine learning, and computational complexity. Her research focuses on understanding the computational limits of high-dimensional statistical tasks and developing algorithms with provable guarantees. Alina will use the MathWorks Fellowship to advance two primary areas: establishing average-case equivalence between long-studied statistical models and resolving theoretical challenges in semi-supervised learning on graphs. In both areas, she integrates rigorous analysis with extensive MATLAB-based simulations to guide theoretical insights, test algorithmic hypotheses, and verify model behavior. MATLAB’s linear algebra and regression tools, along with its flexible visualization capabilities, are integral to her workflow. Alina’s work could reshape how researchers define and assess computational difficulty in modern statistics, offering new tools and insights to inform both theory and application across machine learning, data science, and algorithm design.

Laura Landon
Electrical Engineering and Computer Science

Laura Landon is a graduate student in electrical engineering and computer science, accelerating progress in wireless communications by improving reliability and efficiency in next-generation mobile networks. Her work explores network coding as a robust alternative to automatic repeat request and hybrid automatic repeat request in 5G—and possibly 6G—systems. By integrating custom algorithms into proprietary MATLAB-based simulators, Laura evaluates packet delivery performance in ultra-reliable low-latency communication scenarios and develops solutions that meet both technical demands and evolving standards. As a MathWorks Fellow, she will expand her use of the 5G Toolbox, Communications Toolbox, and Wireless Network Simulation Library to design, simulate, and optimize coding mechanisms across a range of real-world conditions. Laura has also developed a graphical user interface that improves simulation accessibility and usability, accelerating deployment across research and industry teams. Her research could shape the evolution of wireless protocol design, paving the way for more resilient and adaptable mobile systems in the 6G era.

Mingyang Liu
Electrical Engineering and Computer Science

Mingyang Liu is a graduate student in electrical engineering and computer science, driving knowledge forward at the intersection of online learning, game theory, and optimization. His work addresses the challenge of designing learning algorithms for strategic interactions. As a MathWorks Fellow, Mingyang will develop scalable, theory-backed methods for computing equilibria in various settings, including repeated games and games with imperfect information. In one project, he will utilize MATLAB’s visualization tools to uncover the structural properties of utility functions, thereby gaining a better understanding. In another, he will use linear programming solvers to find solutions under various formulations, which validates the theoretical models and provides new insights into the properties of the equilibrium. Mingyang’s innovations could help reshape the way strategic AI systems are trained and deployed, with applications in large language models, economics, and large-scale autonomous systems.

Amit Rajaraman
Electrical Engineering and Computer Science

Amit Rajaraman is a graduate student in electrical engineering and computer science, conducting research in theoretical computer science and the analysis of Markov chains. His work addresses a central puzzle: why do Markov chain–based algorithms often succeed in practical inference and optimization tasks, even when they lack formal mixing guarantees? Amit focuses on slow-mixing Markov chains and has introduced the framework of “locally stationary distributions” to analyze their convergence and performance. As a MathWorks Fellow, he will develop deeper connections between Markov chains and message-passing algorithms by designing theoretical frameworks and experimental comparisons. MATLAB supports this work by enabling visualizations, simulations, and empirical investigations that guide theoretical insights. Amit’s research could significantly reshape our understanding of optimization and inference in high-dimensional settings, providing a more principled foundation for commonly used algorithms, such as stochastic gradient descent. His efforts could ultimately advance both the theoretical foundations and practical applications of AI, machine learning, and statistical physics.

Ittai Rubinstein
Electrical Engineering and Computer Science

Ittai Rubinstein is a graduate student in electrical engineering and computer science, developing tools to ensure the robustness and reliability of machine learning models trained on real-world data. His research addresses the challenge of data attribution—understanding how changes in a training set can impact a model’s output—by building algorithms that offer strong theoretical guarantees and practical efficiency, even in high-dimensional regimes. As a MathWorks Fellow, Ittai will advance new techniques, such as higher-order corrections to influence functions, designed to enhance the accuracy of model sensitivity analysis, and develop faster methods for uncertainty quantification. He will use MATLAB’s linear algebra libraries, visualization capabilities, and flexible interface to implement and test these tools across large datasets. Ittai’s work could improve confidence in statistical inferences across fields such as economics, public policy, and healthcare, empowering researchers to audit and interpret their models with greater precision and transparency.

Jinwoo Sim
Electrical Engineering and Computer Science

Jinwoo Sim is a graduate student in electrical engineering and computer science, expanding knowledge on nanoscale energy harvesting. His work focuses on developing quantum-material-based rectifiers that convert ambient gigahertz and terahertz radiation—emitted by wireless networks and the atmosphere—into usable electrical power. These next-generation devices, based on the nonlinear Hall effect in topological materials, eliminate the frequency limits and threshold losses that constrain traditional diodes. As a MathWorks Fellow, Jinwoo will develop a fully integrated energy harvesting system that combines quantum-enabled rectifiers with impedance-matched antennas and power management circuits. MATLAB plays a critical role across Jinwoo’s workflow, enabling automation of complex multi-instrument measurements, real-time diagnostics, and high-throughput data analysis. He has also developed reusable MATLAB toolkits for experimental control and circuit modeling, which he plans to share publicly. His work could lead to self-powered sensors, wearables, and electronics, helping to realize a scalable, battery-free future for connected devices.

Shixin Song
Electrical Engineering and Computer Science

Shixin Song is a graduate student in electrical engineering and computer science who focuses on security in modern computing systems. Her research tackles the growing threat of microarchitectural side-channel attacks—vulnerabilities in hardware that can leak sensitive information despite existing software protections. Shixin focuses on strengthening operating systems and cryptographic software through rigorous, hardware-aware solutions. She developed Oreo, a software-hardware co-design that protects address space layout randomization by restructuring the virtual-physical memory interface and minimizing the exposure of sensitive data to side-channel attacks. In parallel, she is designing an assembly-level transformation framework that enables secure speculative execution in cryptographic libraries by accurately separating secret and public data. As a MathWorks Fellow, she will focus on enhancing MathWorks Polyspace’s static code analysis tools by extending their threat model beyond memory safety to include microarchitectural side-channel vulnerabilities. Her work could advance secure system design across both industry and academia, paving the way for more robust protections in everyday computing systems.

Fan Xue
Electrical Engineering and Computer Science

Fan Xue is a graduate student in electrical engineering and computer science, where she is building integrated microsystems for biomolecular sensing and microfluidic automation. Her research addresses the need for compact, continuous sensing platforms to monitor physiological and environmental conditions without bulky instrumentation. Fan develops electrochemical sensors and gravity-driven microfluidic modules that enable precise, real-time biochemical measurements. As a MathWorks Fellow, she anticipates utilizing MATLAB to analyze sensor data, simulate electrochemical interactions, and implement feedback control for signal compensation. She also seeks to draw on the Signal Processing Toolbox, Image Processing Toolbox, and Statistics and Machine Learning Toolbox to model sensor behavior, calibrate system performance, and optimize data interpretation. Fan’s work could lead to scalable, cost-effective systems for point-of-care diagnostics, wearable health monitoring, and automated laboratory workflows.

Waleed Akbar
Electrical Engineering and Computer Science

Waleed Akbar is a graduate student in electrical engineering and computer science, where he is helping shape the future of sustainable ocean sensing and communication. His research tackles a pressing challenge in subsea environments: how to enable long-term, scalable data collection without relying on batteries or high-power systems. By advancing underwater backscatter, a technique that reflects existing acoustic signals rather than generating new ones, Waleed is working to unlock battery-free, low-cost subsea networks. As a MathWorks Fellow, he will develop new modulation strategies to further enhance the performance and resilience of these systems in dynamic ocean settings. Waleed relies extensively on MathWorks tools, utilizing MATLAB and its Communication, Signal Processing, Image Processing, and DSP Toolboxes to design and test signal pipelines, analyze real-world data, and build open-source models for system designers. His work stands to transform fields such as ocean monitoring, disaster response, and marine infrastructure by making continuous underwater sensing more accessible, efficient, and sustainable.

Sharut Gupta
Electrical Engineering and Computer Science

Sharut Gupta is a graduate student in electrical engineering and computer science, working to build more robust and intelligent AI systems. Her research addresses fundamental challenges in machine learning, including how models adapt to distribution shifts and how they can learn structured, interpretable representations with minimal supervision. As a MathWorks Fellow, Sharut will develop algorithms that endow AI models with world-modeling capabilities, enabling them to reason about unseen scenarios and adapt to dynamic environments. Her work uses MATLAB, Simulink, and toolboxes for symbolic math, statistics, and optimization to simulate controlled environments, formalize theoretical guarantees, and stress-test learning algorithms. These tools support a range of applications, from solving alignment problems in hyperbolic spaces to modeling rigid-body dynamics in simulated scenes. Sharut’s research could improve the reliability and safety of AI in real-world settings, from robotics and autonomous vehicles to scientific discovery. She plans to open-source tools that make this work accessible to the broader research and engineering communities.

Abhiram Iyer
Electrical Engineering and Computer Science

Abhiram Iyer is a graduate student in electrical engineering and computer science, exploring how principles from neuroscience can inform the next generation of AI. His research bridges theoretical neuroscience and AI, examining how biologically grounded models can both explain brain function and inspire more flexible and efficient machine learning systems. As a MathWorks Fellow, Abhiram will advance models that embed synaptic dynamics and microcircuit architectures into artificial networks, offering possible solutions to long-standing challenges such as catastrophic forgetting and inefficient scaling. MATLAB plays a pivotal role in Abhiram’s research, providing a robust framework for simulating neural circuits, mapping cortical connectivity, solving complex dynamical systems, and prototyping biologically inspired learning algorithms. Leveraging these capabilities, he has successfully modeled grid-cell-based cognitive maps and analyzed synapse-level plasticity mechanisms, contributing to advancements in lifelong learning and interpretable AI. Abhiram’s work could deepen our understanding of intelligence, both natural and artificial, and reshape the foundation of machine learning systems.

Dimple Kochar
Electrical Engineering and Computer Science

Dimple Kochar is a graduate student in electrical engineering and computer science, where she is advancing AI-driven circuit design and energy-efficient hardware. Her research addresses two pressing challenges in modern electronics: optimizing analog and mixed-signal circuit design through automation and building ultra-low-power chips that support real-time AI workloads. As a MathWorks Fellow, Dimple will develop new machine learning frameworks for the automated design and characterization of analog-to-digital converters. MATLAB is a central component of Dimple’s work. She uses its tools for signal processing, neural network prototyping, spectral analysis, and hardware performance modeling, including FFT-based SNDR calculations and histogram-based nonlinearity assessments. By combining AI with circuit innovation, Dimple’s work could transform electronic design automation, enabling more accessible, efficient, and scalable chip development. This, in turn, could pave the way for next-generation devices in healthcare, communication, and beyond.

Dooyong Koh
Electrical Engineering and Computer Science

Dooyong Koh, a graduate student in electrical engineering and computer science, explores energy-efficient electronic devices with the aim to redefine the future of semiconductor technology. His research focuses on harnessing ferroelectric materials for ultra-low-power logic and memory, including pioneering work on negative capacitance transistors that enhance performance while reducing energy use. As a MathWorks Fellow, Dooyong will optimize gate dielectrics built from hafnia–zirconia–based materials to overcome traditional scaling bottlenecks, aiming to deliver next-generation logic and memory. He utilizes MATLAB’s Curve Fitting, Statistical Analysis, and Machine Learning Toolboxes to extract parameters, such as charge carrier injection velocities, from ferroelectric device measurements. By uniting material innovation with computation, Dooyong’s work is poised to transform how chips are designed, enabling smaller, faster, and more energy-efficient technologies for the future of computing.

Dip Joti Paul
Electrical Engineering and Computer Science

Dip Joti Paul is a graduate student in electrical engineering and computer science whose work aims to advance superconducting nanowire single-photon detectors for quantum sensing and mid-infrared imaging. His research addresses a long-standing challenge in the field: improving the accessibility of superconducting nanowire single-photon detectors by raising their operating temperature above 4 K, as observed in conventional devices. As a MathWorks Fellow, Dip Joti will explore high-critical-temperature superconducting materials, such as yttrium barium copper oxide, to develop detectors that could function above 77 K, potentially transforming applications from deep-space telescopes to portable quantum devices. Dip Joti relies on MATLAB’s Instrument Control Toolbox to automate high-throughput measurements in cryogenic environments, and the Curve Fitting Toolbox to analyze flux-flow resistivity in superconducting nanowires. He developed a custom MATLAB package, RefractEx, to calculate mid-infrared refractive indices of thin films from Fourier transform infrared spectroscopy measurements, which is now publicly available. His work could redefine performance standards for superconducting nanowire-based photon detectors, enabling more scalable, cost-effective technologies across quantum science, astronomy, and advanced spectroscopy.

Maxine Perroni-Scharf
Electrical Engineering and Computer Science

Maxine Perroni-Scharf is a graduate student in electrical engineering and computer science working at the intersection of fabrication, human-computer interaction, graphics, and sustainability. Her research focuses on the environmental impact of physical prototyping, with a particular emphasis on mitigating the high waste rates associated with 3D printing processes. As a MathWorks Fellow, Maxine will leverage MATLAB’s Partial Differential Equation Toolbox and the CVX optimization package to drive geometry processing and simulations, showcasing how mathematical modeling can support expressive and resource-efficient design practices. Previously, Maxine has worked on the discovery of high-performance 3D-printed microscale metamaterial structures and developed 3D-printable surfaces with view-dependent appearance. Maxine’s work could transform how we approach computational fabrication in the future through eco-friendly design practices, enabling the sustainable growth of fabrication technologies across engineering, industry, and personal use.

Khandoker Nuzhat Rafa Islam
Electrical Engineering and Computer Science

Khandoker Nuzhat Rafa Islam is a graduate student in electrical engineering and computer science, working to enhance the reliability of materials for advanced semiconductor packaging. Her research focuses on copper-to-copper direct bonding for 3D heterogeneous integration—an essential approach for scaling integrated circuits beyond the physical and performance limits of traditional architectures. By studying how microstructural characteristics affect bonding quality, she aims to inform the development of more robust, high-performance interconnects for future-generation devices. As a MathWorks Fellow, Rafa will develop predictive models that connect surface morphology with bond strength, helping to guide materials selection and processing techniques. She uses MATLAB’s Image Processing Toolbox and MTEX to analyze electron microscopy and crystallographic data, extracting features such as grain orientation, texture, and void distribution. Rafa’s research could accelerate innovation in semiconductor manufacturing and demonstrate how powerful computational tools can drive progress at the intersection of materials science and electronic engineering.

Sarina Sabouri
Electrical Engineering and Computer Science

Sarina Sabouri is a graduate student in electrical engineering and computer science, advancing radar-based systems for contactless, continuous health monitoring. Her research addresses the need for scalable, unobtrusive technologies to measure heart rate and respiration, particularly for populations who may have difficulty using traditional wearable devices. Sarina is designing a 60GHz radar integrated circuit that combines millimeter-wave beamforming with adaptive signal processing to enhance vital sign detection in real-world, multi-angle environments. As a MathWorks Fellow, she will expand her MATLAB-based simulations to refine system-level design and validate performance under dynamic conditions. She utilizes the Phased Array, Signal Processing, RF, and Radar Toolboxes to model human subjects, implement beam-steering algorithms, and assess accuracy across various orientations. MATLAB also supports her development of techniques to mitigate transmitter-to-receiver interference and correct beam squint—two innovations that could significantly boost radar sensitivity while reducing system power and cost. Sarina’s work could pave the way for compact, battery-powered radar platforms that seamlessly integrate health monitoring into everyday life.

Xiao (Sean) Zhan
Electrical Engineering and Computer Science

Sean Zhan is a graduate student in electrical engineering and computer science, developing AI-assisted tools to accelerate 3D design and fabrication. His research addresses a central challenge in engineering and design: how to ensure that generative models produce outputs that are not only creative but also mechanically sound and manufacturable. As a MathWorks Fellow, Sean aims to streamline the path from concept to production in engineering workflows by integrating AI with physical simulation. MATLAB supports this work across multiple dimensions, including mesh generation, finite element analysis, optimization routines, and manufacturability evaluation. Sean uses the Partial Differential Equation and Optimization Toolboxes to simulate structural deformation, refine part geometry, and ensure the feasibility of AI-generated designs. His research could reshape how engineers and designers apply generative tools, creating more reliable, fabrication-ready parts while expanding the boundaries of design automation.

Khoi Dao
Electrical Engineering and Computer Science

Khoi Dao is a graduate student in materials science and engineering, developing advanced micro-heater platforms to enable novel material processing directly on-chip. His research addresses a central challenge in semiconductor manufacturing: integrating new materials within the thermal limits of standard CMOS workflows. By combining inverse design with high-resolution thermal control, Khoi is creating programmable micro-heaters capable of executing precise, localized heat treatments previously unachievable with traditional furnaces. As a MathWorks Fellow, he will refine these design algorithms and expand their use in the testing of optical phase change materials and integrating of photonic components. Khoi relies extensively on MATLAB and Simulink to perform thermal simulations, automate resistance mapping, control experimental setups, and optimize device design across thousands of variables. This work could accelerate the integration of new photonic and quantum materials into next-generation chips, driving innovations in computing, sensing, and beyond. Through a powerful combination of modeling and experimentation, Khoi is advancing a new paradigm in microscale materials engineering.

Paul Miller
Materials Science and Engineering

Paul Miller is a graduate student in materials science and engineering, developing a high-throughput nano-laboratory to accelerate materials discovery using ultra-high vacuum transmission electron microscopy. His research addresses a key limitation in advanced materials characterization: the slow pace of in situ experiments needed to observe phase changes, nucleation, and defect dynamics. As a MathWorks Fellow, Paul will design and implement a microfabricated sample platform with embedded microheaters and integrated logic, enabling automated, parallelized experiments under controlled deposition and thermal conditions. He uses MATLAB for parametric device design, fabrication layout generation, and integration with COMSOL simulations via LiveLink. His work also applies MATLAB scripting to automate instrument control, analyze high-resolution imaging data, and drive real-time experimentation across a vacuum-interfaced ASIC platform. Paul’s work could transform how scientists study material growth and transformations, with implications for the development of quantum devices, optoelectronics, and microelectronics manufacturing.

Juno Nam
Materials Science and Engineering

Juno Nam is a graduate student in materials science and engineering, developing machine learning frameworks to accelerate atomistic simulations for materials discovery. His research addresses a central challenge: modeling complex materials with quantum-level accuracy over long timescales and realistic temperatures. As a MathWorks Fellow, Juno will combine generative modeling, enhanced sampling, and alchemical interpolation into a unified platform for dynamics-aware materials design. He and colleagues created LiFlow, a generative acceleration tool that extends molecular dynamics simulations by orders of magnitude and pioneered new approaches to modeling chemical disorder using machine learning interatomic potentials. Juno uses MATLAB for model optimization, symbolic differentiation, neural ODE integration, and high-dimensional data visualization. He also builds user-friendly workflows to make advanced simulations more accessible. By merging data-driven and physics-based approaches, Juno’s work could transform what’s computationally feasible in the design of next-generation materials for energy storage, catalysis, and other critical applications.

Changhwan Oh
Materials Science and Engineering

Changhwan Oh is a graduate student in materials science and engineering working at the intersection of computational chemistry and machine learning to accelerate the design of materials for greenhouse gas capture. His research focuses on metal-organic frameworks (MOFs), a class of porous materials designed to selectively store carbon dioxide and methane. A central challenge is designing MOFs that are not only highly adsorptive but also exhibit diverse forms of stability, especially under industrial conditions. As a MathWorks Fellow, Changhwan will develop multi-objective optimization workflows that integrate machine learning, high-throughput screening, and quantum simulations to discover next-generation MOFs for carbon capture in humid environments. He utilizes MATLAB for deep learning, feature engineering, data parsing, and optimization, leveraging the Statistics and Machine Learning Toolbox, Deep Learning Toolbox, and Global Optimization Toolbox. By bridging chemical physics with data science, Changhwan’s work could enable scalable climate solutions and provide researchers with powerful, user-friendly tools for materials discovery.

Yixuan (Cassie) Song
Materials Science and Engineering

Yixuan (Cassie) Song is a graduate student in materials science and engineering, investigating the magnetic dynamics of ferrimagnetic insulators—complex materials with tunable spin configurations that could enable advances in spintronic and terahertz technologies. Her research focuses on the fundamental behavior of spin textures and auto-oscillations to design nanoscale devices that leverage engineered anisotropy and current-induced magnetization switching. Now in her second year as a MathWorks Fellow, Cassie will continue developing macrospin models and finite element simulations to predict magnetic behavior in thin films under varying conditions. MATLAB remains central to her approach: she uses the Optimization Toolbox to fit experimental data, the Partial Differential Equation Toolbox to simulate magnetization trajectories, and custom scripts to model anisotropy landscapes and demagnetization fields. Her work could pave the way for energy-efficient memory devices, high-frequency signal generation, and spin-based logic, highlighting the role of advanced modeling in accelerating discovery across complex material systems.

Seungyeon Woo
Materials Science and Engineering

Seungyeon Woo is a graduate student in materials science and engineering, where she is developing light-controlled methods for the precise assembly of nanoscale materials. Her research tackles a central challenge in nanotechnology: how to construct complex, programmable crystal structures from colloidal particles with fine spatial and temporal control. She focuses on gold nanoparticles functionalized with DNA, which she organizes into ordered lattices by applying photothermal gradients to guide their assembly. As a MathWorks Fellow, she will use MATLAB to model heat distribution, automate control of experimental parameters, and analyze real-time crystallization dynamics. These computational tools will enable pixel-level modulation of light exposure and support microscopic image-based feedback to monitor and refine growth pathways. By integrating experimental and computational approaches, Seungyeon’s work aims to establish scalable strategies for fabricating tunable photonic materials and reconfigurable nanosystems. Her research holds promise for applications in energy harvesting, optical sensing, and microfluidic systems, demonstrating how programmable matter can be harnessed through precision-guided self-assembly.

Kentaro Barhydt
Mechanical Engineering

Kentaro Barhydt is a graduate student in mechanical engineering whose research focuses on novel robot design for handling historically challenging objects. Specifically, Kentaro develops robotic mechanisms and design paradigms for safe and effective handling of heavy, fragile, and varied payloads such as the human body and heavy industry equipment. His research introduces “loop closure grasping,” a method of grasping that transforms soft robotic mechanisms from open loops to closed loops, bypassing tradeoffs of single-morphology designs to enable simultaneously strong, gentle, and versatile grasps. Kentaro utilizes MATLAB across the full arc of his work to model, design, analyze, and control robotic systems. As a MathWorks Fellow, he will advance the loop closure grasping approach by developing modeling strategies and building MATLAB tools for automatic grasp creation with inflated beam robots. His research holds the promise to expand the role of robotics and soft mechanisms for challenging yet important applications such as assistive care, industrial automation, and emergency response.

Vermeer Bonhomme
Mechanical Engineering

Vermeer Bonhomme is a graduate student in mechanical engineering, designing the next generation of ultra-low-noise suspensions for gravitational-wave detectors. His research supports the Cosmic Explorer observatory, which will extend the arm length of existing interferometers from 4 km to 40 km and increase suspended mass from 120 kg to over 2000 kg. Vermeer’s work focuses on modeling, optimizing, and prototyping precision suspension systems that isolate test masses from seismic and control noise while accounting for optomechanical coupling and scale-induced dynamic effects. As a MathWorks Fellow, he will develop a control-driven design framework for this system, supported by advanced modeling and optimization in MATLAB. He uses Simscape and Simulink to simulate multi-mass pendulum dynamics, test controller architectures, and visualize coupling effects across large-scale optical cavities. Live Scripts and custom modeling tools help his collaborators assess performance under design tolerances. Vermeer’s work could enable earlier, more sensitive detection of gravitational waves, opening new observational windows into the early universe.

Audrey Cui
Mechanical Engineering

Audrey Cui is a graduate student in mechanical engineering working at the intersection of neuroscience and precision engineering to support next-generation brain mapping technologies. Their research focuses on femtosecond laser microtomy, a cutting-edge approach that replaces traditional mechanical slicing with ultrafast laser ablation to enhance accuracy and scalability. As a MathWorks Fellow, Audrey will model and optimize laser parameters to achieve consistent nanometer-level precision and will package these methods into a MATLAB-based tool for use in laser machining and biomedical applications. Audrey’s work utilizes MATLAB to simulate ablation dynamics, evaluate system sensitivity, and analyze trade-offs across various performance metrics. Audrey uses the Optimization Toolbox to identify globally optimal control settings and validate them experimentally. Their contributions could make it possible to reconstruct the human connectome in full detail—an achievement with profound implications for neuroscience—and could advance precision techniques in surgery, imaging, and microfabrication.

Rika Sugimoto Dimitrova
Mechanical Engineering

Rika Sugimoto Dimitrova is a graduate student in mechanical engineering whose work explores how the brain maintains upright posture in the face of delays, noise, and degradation in neuromuscular control. Her research focuses on quiet standing—an everyday task that becomes profoundly challenging with age or neurological impairment—and aims to reveal how balance control strategies adapt or fail across different populations. As a MathWorks Fellow, Rika will develop and evaluate competing models of human balance, including intermittent and predictive controllers, to identify the mechanisms most consistent with physiological behavior. MATLAB plays a central role in her work, enabling high-throughput spectral analysis, system modeling, and delay-based simulations. She utilizes the Signal Processing, Control System, and Statistics and Machine Learning Toolboxes, and shares her custom code openly to promote reproducibility. Rika’s research could inform new approaches to diagnosing balance deficits and inspire future technologies that support mobility, rehabilitation, and fall prevention.

Michael Espinal
Mechanical Engineering

Michael Espinal is a graduate student in mechanical engineering working at the forefront of architected materials design. His research explores spinodal architectures and other shell-based mechanical metamaterials to create lightweight, self-assembled structures with tunable curvature that offer superior mechanical performance compared to traditional forms. By uncovering how geometry governs deformation and energy absorption, Michael aims to develop predictive design tools that accelerate the deployment of these materials across aerospace, transportation, and defense sectors. As a MathWorks Fellow, he will pursue a unified mechanics framework to better understand and engineer the next generation of shell-based materials. MATLAB plays a pivotal role in his work. Michael uses it to generate complex geometries, extract curvature fields, and process microtomography scans to reconstruct the 3D microstructure of self-assembled spinodal architectures. His integrative approach, which combines theory, computation, and fabrication, stands to reshape how engineers design high-efficiency materials for real-world structural and energy-absorbing applications.

Charlotte Folinus
Mechanical Engineering

Charlotte Folinus is a graduate student in mechanical engineering working to redefine how soft and soft-rigid hybrid robotic systems are designed and built. Her research focuses on integrating materials science, fabrication methods, and mechanical modeling to address long-standing challenges in reliability and real-world performance. As a MathWorks Fellow, Charlotte will develop interactive MATLAB-based tools that help designers navigate material trade-offs, predict failure modes, and optimize the geometry of origami-inspired soft actuators. MATLAB is central to every stage of her work, from building a searchable database of over 600 soft materials to processing data from more than 1,000 mechanical tests. Her custom analysis and visualization tools reveal how subtle differences in bonding, geometry, and processing impact mechanical behavior. By challenging assumptions and prioritizing manufacturability, Charlotte is building a rigorous, data-driven foundation for the next generation of soft robotics. Her work could unlock new possibilities for deploying soft robots in clinical rehabilitation, in-home care, and other settings where human interaction and mechanical resilience are equally essential.

Leila Freitag
Mechanical Engineering

Leila Freitag is a graduate student in mechanical engineering whose research has spanned autonomous robotic assembly of satellites and the use of diffractive optics for space-based lidar systems. As a MathWorks Fellow, Leila will develop MATLAB-based simulation frameworks to evaluate the performance of deployable diffractive optics in lidar instruments. These thin membrane optics are a promising technology that could enable large-aperture, low-SWaP (size, weight, and power) mission architectures, but also introduce unique challenges such as deployment and thermal deformations in orbit. Leila’s model simulates the propagation of a laser beam through the atmosphere, where it interacts with clouds and aerosols, estimating the magnitude of the backscattered signal expected at the detector after passing through the optic. By accounting for solar background noise and measurement resolution, her approach generates signal-to-noise ratios to guide mission design. She will validate these simulations with ground-based lidar measurements using a diffractive optic at NASA. Her contributions could enable large-aperture instruments for remote sensing on small satellites, a capability previously limited to much larger spacecraft.

Qiyun Gao
Mechanical Engineering

Qiyun Gao is a graduate student in mechanical engineering, designing personalized medical devices that integrate advanced modeling, embedded systems, and real-world clinical validation. His research focuses on developing a non-invasive, mass-customizable oral appliance to treat obstructive sleep apnea, offering a potential alternative to CPAP and surgical interventions. With a second MathWorks Fellowship, Qiyun will develop a cloud-based pipeline that transforms patient imaging and physiology into 3D-printable device designs, enabling rapid customization at scale. He uses MATLAB to synchronize multi-sensor clinical data, process MRI-based fiber maps for tongue biomechanics, and build custom visualization tools for sleep studies. His software has supported everything from pilot trials to device optimization, streamlining workflows for both clinicians and engineers. With a background that spans prosthetics, gastrointestinal diagnostics, and respiratory therapy, Qiyun brings a translational perspective to medical engineering. His work charts the path toward transforming the treatment of sleep disorders, making therapy more responsive, accessible, and effective.

Shaoxun Huang
Mechanical Engineering

Shaoxun Huang is a graduate student in mechanical engineering working to uncover the physical principles that govern multicellular organization and tissue morphogenesis. His research spans two core areas: modeling collective cell behavior using graph-based neural networks and exploring how cell-extracellular matrix (ECM) interactions drive emergent mechanical patterns. As a MathWorks Fellow, Shaoxun will develop computational frameworks to predict and design tissue dynamics, with applications in developmental biology, regenerative medicine, and programmable living materials. He uses MATLAB for high-throughput image processing, graph construction, mechanical modeling, and dynamic simulation. His custom pipelines integrate segmentation, machine learning, and batch computation to extract structural and topological features from microscopy datasets. He also simulates nonlinear force propagation through fibrous ECMs to explore how mechanical memory and spatial cues influence tissue deformation. Shaoxun’s work could advance our understanding of mechanobiology while enabling the engineering of reconfigurable tissues and bioinspired soft systems that adapt to their environment.

Ahmad Mujtaba Jebran
Mechanical Engineering

Ahmad Mujtaba Jebran is a graduate student in mechanical engineering working at the intersection of soft materials, device physics, and biomedical applications. His research focuses on designing stretchable organic semiconductors for next-generation conformable chips and bioelectronic devices. By introducing microscale geometric patterning into polymer films, he aims to simultaneously enhance mechanical compliance and electrochemical performance—key challenges in wearable and implantable electronics. As a MathWorks Fellow, Mujtaba will expand his computational design framework to automate geometry optimization using AI-driven modeling. He utilizes MATLAB’s Partial Differential Equation Toolbox to simulate stress, strain, and deformation across microstructured polymer geometries under biologically relevant loading conditions. His parameterized design pipeline enables rapid generation, evaluation, and refinement of microarchitectures, with results informing both fabrication and experimental validation. Beyond simulation, he also develops custom MATLAB tools for real-time data acquisition and visualization during mechanical and electrical testing. Mujtaba’s work could reshape how we design bioelectronic interfaces, bringing more robust, conformable, and responsive medical devices closer to clinical reality.

Delace Jia
Mechanical Engineering

Delace Jia is a graduate student in mechanical engineering studying how flow can be used to structure lyotropic chromonic liquid crystals—biocompatible materials with applications in optics, biosensing, and microfluidics. Her research focuses on understanding the self-assembly of dynamic structures and topological defects that emerge when these materials are driven far from equilibrium. As a MathWorks Fellow, Delace will develop tools to map and manipulate local director fields, enabling new strategies for controlling soft matter and designing photonic devices. She uses MATLAB extensively to analyze polarization microscopy data, isolate local orientation and order, and track defect dynamics through custom image processing pipelines. Her algorithms extract quantitative maps of polarization parameters, providing new insight into how orientational order and flow interact. Delace also integrates particle tracking and velocity field analysis to explore how suspended particles respond to flow-structured textures. Her research is poised to advance both the fundamental understanding and practical utility of flow-induced organization in soft materials.

Yang Li
Mechanical Engineering

Yang Li is a graduate student in mechanical engineering, developing fast, stable, and quantum-accurate interatomic potentials for molecular dynamics simulations of thermal transport. His research focuses on improving the Taylor Expansion Potential (TEP), a promising method that combines high accuracy with low computational cost but suffers from stability issues. As a MathWorks Fellow, Yang will build a framework for stabilizing and extending TEP models to enable predictive simulations of heat conduction at scale. MATLAB plays a critical role in his work, from symbolic derivation of translational invariance conditions to parameter interpolation and model validation across scales. He uses the Optimization and Deep Learning Toolboxes to extract interatomic parameters from density functional theory and evaluate thermal conductivity with advanced modal analysis techniques. By bridging quantum calculations and atomistic simulations, Yang’s research could unlock new approaches to designing materials with tailored thermal properties, accelerating progress in electronics, energy systems, and heat management technologies.

Zeyang Li
Mechanical Engineering

Zeyang Li is a graduate student in mechanical engineering advancing the design of reinforcement learning algorithms that are provably safe, robust, and scalable for real-world control systems. His research integrates control theory and machine learning to ensure that AI-driven agents, such as autonomous vehicles or collaborative robots, can make intelligent decisions without compromising safety. As a MathWorks Fellow, Zeyang will develop frameworks for safe reinforcement learning and data-driven safety filters. He leverages MATLAB and Simulink throughout his research to simulate dynamical systems, implement control algorithms, and solve optimization problems. Using the Control System, Optimization, and Deep Learning Toolboxes, he has built and validated algorithms that provide strong theoretical guarantees while remaining practical for complex tasks. Zeyang’s work is helping to lay the foundation for intelligent systems that operate reliably in uncertain, safety-critical environments, unlocking new capabilities in autonomous technology and large-scale decision-making.

Krishna Manoj
Mechanical Engineering

Krishna Manoj is a graduate student in mechanical engineering advancing the quantitative understanding and design of CRISPR-based gene regulatory networks. Her research focuses on modeling molecular interactions using systems of nonlinear differential equations to uncover the fundamental mechanisms that govern CRISPR-mediated regulation. As a MathWorks Fellow, Krishna will design and implement biomolecular feedback controllers to mitigate resource competition and restore modularity in synthetic genetic circuits. She uses MATLAB to simulate complex regulatory dynamics, analyze experimental datasets, and identify key sensitivities across large parameter spaces. MATLAB’s Control System Toolbox and Simulink are central to her controller design workflow, enabling both linear analysis and full nonlinear system simulations. She also applies optimization and statistical tools for parameter estimation and model validation. Krishna’s work could redefine how synthetic biologists engineer scalable genetic programs for applications in screening, bioproduction, and cell-based therapies, bridging control theory and biology to deliver predictive, robust, and tunable systems.

Diego Quevedo-Moreno
Mechanical Engineering

Diego Quevedo-Moreno is a graduate student in mechanical engineering, developing soft robotic systems to advance the treatment and testing of cardiopulmonary diseases. His work combines device design, computational modeling, and closed-loop control to create novel therapeutic platforms for respiratory and cardiovascular interventions. As a second-year MathWorks Fellow, Diego will develop a soft robotic mitral valve simulator that enables high-fidelity evaluation of transcatheter valve replacement devices under realistic loading conditions. Diego uses MATLAB and Simulink extensively for system identification, physiological modeling, and control optimization. He applies the Image Processing Toolbox to extract motion features from imaging data and uses the Optimization and System Identification Toolboxes to develop models of actuator dynamics and tissue interaction. For cardiovascular simulation, he will integrate ultrasound-based anatomy with MATLAB’s parametric valve modeling and Partial Differential Equation Toolbox to study fluid-structure interactions. His work could improve the design and evaluation of next-generation implants, reducing failure rates and accelerating innovation in cardiac and respiratory care.

Aditya Karthik Saravanakumar
Mechanical Engineering

Aditya Karthik Saravanakumar is a graduate student in mechanical engineering and computational science and engineering, working to advance non-hydrostatic ocean modeling for multiscale, high-fidelity simulation of ocean dynamics. His research develops adaptive numerical methods that reduce computational cost by resolving vertical accelerations only where necessary, enabling accurate predictions of submesoscale phenomena around sharp coastlines and topographic features. As a MathWorks Fellow, Aditya will further develop and extend these models, as well as their coupling with large-scale ocean solvers. He uses MATLAB extensively to prototype new solvers, validate numerical schemes, process ocean data, and visualize simulation results. His work includes developing finite-volume MATLAB solvers for rapid experimentation, integrating MATLAB with C++ for processing NetCDF data, and utilizing MATLAB-based tools for classroom instruction. By enhancing our ability to model key ocean processes, Aditya’s research supports applications in climate science, maritime safety, environmental protection, and national security, demonstrating the vital role of computational tools in advancing ocean engineering.

Julie Shen
Mechanical Engineering

Julie Shen is a graduate student in mechanical engineering, advancing new strategies for monitoring and managing cardiogenic shock through real-time physiological analytics. Her research focuses on dynamic heart-device interactions, utilizing mechanical circulatory support systems, such as the Impella CP, to capture beat-to-beat hemodynamic data. By developing a trajectory vector framework that maps changes in cardiovascular state over time, Julie aims to improve the personalization and timing of intervention for patients with acute heart failure. MATLAB is central to every phase of her research, from ECG signal processing and pressure-volume loop analysis to clustering, circular statistics, and polar vector visualization. She employs toolboxes for signal processing, curve fitting, and image analysis to extract physiological metrics and validate their clinical significance. Julie’s work could reshape how critical care decisions are made, moving from static snapshots to dynamic, responsive management informed by high-frequency data, offering a new lens through which to understand and treat life-threatening cardiac conditions.

Dongchel Shin
Mechanical Engineering

Dongchel Shin is a graduate student in mechanical engineering pursuing experimental approaches to test whether gravity behaves as a quantum interaction. His research centers on developing precision measurement systems capable of detecting the angular motion of torsional oscillators at the quantum-noise limit. This work lays the foundation for preparing macroscopic objects in quantum states and probing gravitational interactions. With his second MathWorks Fellowship, Dongchel will advance cavity torsional optomechanics to enhance sensitivity and enable active feedback control in macroscopic quantum systems. He extensively employs MATLAB to model optical systems, simulate mode shapes, analyze feedback performance, and process experimental data. His custom code supports ray transfer matrix analysis, spectral estimation, and noise calibration, enabling the characterization of measurement precision and system stability. His recent demonstration of laser cooling in a centimeter-scale torsional oscillator marks a significant step forward in optomechanical control. This work could open a new experimental pathway toward quantum aspects of gravity, reshaping our understanding of the physical world.

Nomi Yu
Mechanical Engineering

Nomi Yu is a graduate student in mechanical engineering, developing generative AI tools to accelerate design and reverse engineering in the manufacturing industry. Their research focuses on reconstructing and generating editable CAD programs from non-parametric geometries, like point clouds and meshes, enabling engineers to remanufacture legacy parts and refine AI-generated geometries. As a second-year MathWorks Fellow, Nomi will extend this work into a streamlined, end-to-end pipeline for scan-to-CAD reconstruction and automated design analysis. Their framework, GenCAD-3D, combines modality-specific encoders, contrastive learning, and diffusion-based generation to produce high-fidelity CAD feature trees from point clouds and meshes. They will use MATLAB for integrating scan preprocessing, model training, and downstream simulation, leveraging toolboxes for lidar, computer vision, deep learning, and CAD interfacing. Nomi’s work could improve accessibility to generative design tools, reduce engineering cycle times, and expand the practical use of AI in product development across robotics and advanced manufacturing.

Qifan Yu
Mechanical Engineering

Qifan Yu is a graduate student in mechanical engineering, creating design and simulation frameworks for soft robotic systems with embodied intelligence. His research focuses on integrating material behavior, fabrication constraints, and sensor feedback into tools that support the co-design of compliant structures and embedded sensing. As a MathWorks Fellow, Qifan will advance multi-physics optimization strategies that couple actuators, sensor placement, and fabrication in the design of next-generation soft robots. He uses MATLAB to implement topology optimization routines, model soft elastomeric sensors, and simulate performance in high-deformation regimes. His work includes the design of counter-bending robotic fingers inspired by flagella and a soft optical waveguide sheet that reconstructs 3D surface shapes in real-time. Custom MATLAB tools enable him to explore waveguide configurations, simulate optical signal response, and minimize sensing error under fabrication noise. Qifan’s contributions could establish new paradigms for soft robot design, enabling programmable functionality and adaptive sensing in devices for human-robot interaction and soft robotics.

Roger Pallares Lopez
Mechanical Engineering

Roger Pallares Lopez is a graduate student in mechanical engineering, developing deep learning–based tools to quantify musculoskeletal tissue motion from ultrasound imaging. His research addresses the global challenge of musculoskeletal disorders by improving the ability to assess movement quality and soft tissue dynamics in clinical and athletic settings. As a MathWorks Fellow, Roger will adapt vision foundation models to segment and track musculoskeletal structures, such as muscles, bones, and fascia, from b-mode ultrasound videos, marking a major step forward in noninvasive internal elastic tissue analysis. To support this work, he uses k-Wave and MATLAB’s Signal and Image Processing Toolboxes to simulate tissue deformation, model probe behavior, and generate synthetic training data that strengthens model performance. His research could enable new methods for injury prevention, rehabilitation, and movement efficiency assessment, transforming how we evaluate and optimize human motion using wearable ultrasound and AI-powered analysis.

Max Pierce
Mechanical Engineering

Max Pierce is a graduate student in mechanical engineering studying how ocean waves interact with sea ice—a topic critical to understanding climate feedback loops in the rapidly changing Arctic. His research investigates whether increasingly energetic wave activity contributes to sea ice breakup, possibly speeding up sea ice retreat and altering global shipping routes. By combining nonlinear wave theory with scalable simulations, Max is uncovering how wave-induced stresses fracture ice sheets and reshape marginal ice zones. As a MathWorks Fellow, Max will expand his use of MATLAB to model wave-ice interactions across complex floe geometries and distributions. He utilizes MATLAB’s numerical integration capabilities, parallel computing tools, and community-built libraries, such as pMATLAB, to perform large-scale Monte Carlo simulations. His work has already demonstrated that wave stress within an ice floe is primarily independent of shape, significantly simplifying future predictive models. Max’s research could redefine how scientists and policymakers model ice retreat, providing faster, more accurate insights into polar dynamics and their global consequences.

Rodrigo Cavalcanti Alvarez
Nuclear Science and Engineering

Rodrigo Cavalcanti Alvarez is a graduate student in nuclear science and engineering, advancing research in boiling heat transfer to improve the safety and efficiency of thermal systems. His work focuses on the boiling crisis—a phenomenon where heat transfer breaks down—by experimentally investigating the physical mechanisms that trigger this event. With his second MathWorks Fellowship, Rodrigo will use high-speed infrared and visible imaging to test competing theories of the boiling crisis, capturing transient bubble dynamics and surface temperature fields from a novel bottom-view setup. MathWorks tools, particularly MATLAB’s image processing, statistical analysis, and data visualization capabilities, are essential to extracting insights from terabytes of experimental data. Rodrigo will automate image segmentation and feature tracking to identify key boiling parameters, advancing predictive models of heat transfer failure. His work could enhance the reliability of nuclear reactors and other energy systems, contribute custom tools to the MATLAB user community, and support global efforts to enable low-carbon energy through better thermal management.

Marco Graffiedi
Nuclear Science and Engineering

Marco Graffiedi is a graduate student in nuclear science and engineering, researching boiling heat transfer for applications in space exploration and electronic cooling. He integrates experimental data with computational modeling to develop and validate microscopic heat transfer mechanisms using cryogenic and dielectric fluids. As a MathWorks Fellow, he will design a framework that combines image-based detection of liquid-vapor interfaces with finite element solvers to estimate heat flux mechanisms in scenarios where direct measurement of heat flux, such as infrared thermography, is impossible. Marco uses MATLAB’s deep learning models and other tools, such as temporal U-Net architectures and Kalman filtering, to improve the segmentation of high-speed video recordings of boiling surfaces. Finite element thermal modeling is then used to reconstruct the heat flux at the surface based on the segmented video data. Marco’s work could reshape how scientists and engineers characterize thermal behavior in extreme environments, opening new possibilities for efficient heat management in both aerospace systems and high-performance electronics.

Takuya Isogawa
Nuclear Science and Engineering

Takuya Isogawa is a graduate student in nuclear science and engineering, developing advanced quantum sensing techniques grounded in quantum information science. His research focuses on multiparameter quantum estimation using nitrogen-vacancy centers in diamond, solid-state quantum sensors that offer high spatial resolution and operate at room temperature. Unlike conventional quantum sensors that target a single parameter, Takuya’s work enables the simultaneous estimation of multiple physical quantities, opening new possibilities for biological imaging and the study of complex materials systems. As a MathWorks Fellow, he will design and implement protocols that achieve high sensitivity across multiple parameters, even in the presence of experimental imperfections. MATLAB is essential to every stage of this work, from defining pulse sequences and running experiments to simulating quantum models, acquiring and processing data, and optimizing estimation strategies. Takuya’s research could advance the development of compact, high-performance quantum sensors, pushing the boundaries of precision measurement across science, medicine, and technology.

Andrew Lanzrath
Nuclear Science and Engineering

Andrew Lanzrath is a graduate student in nuclear science and engineering, designing diagnostics to advance the study of inertial confinement fusion—a leading platform for achieving net energy from fusion and probing fundamental plasma physics. His research focuses on MagSpec, a high-efficiency charged particle spectrometer that analyzes fusion reaction products from experiments at the National Ignition Facility and the OMEGA laser. These measurements inform models of plasma conditions during fusion implosions, helping researchers optimize performance for clean energy generation and nuclear astrophysics. As a MathWorks Fellow, Andrew will refine MATLAB-based models that simulate charged particle trajectories in magnetic fields, enabling the robust reconstruction of spectra from detector data. He uses the ode45 solver to model Lorentz-force dynamics, the Parallel Computing Toolbox to accelerate Monte Carlo simulations, and the Statistics and Machine Learning Toolbox to conduct Bayesian error analysis. His work could significantly enhance the precision and speed of diagnostic development, thereby strengthening the experimental foundation for fusion energy and contributing to our understanding of the early universe.

Thomas Varnish
Nuclear Science and Engineering

Thomas Varnish is a graduate student in nuclear science and engineering, developing experimental platforms to investigate magnetic reconnection—an energetic plasma process that rapidly converts magnetic energy into particle acceleration and heating. His research focuses on guide-field reconnection, a regime relevant to solar flares and other astrophysical environments with complex magnetic geometries. By embedding magnetic fields within plasma flows, Thomas is designing pulsed-power experiments that probe physics beyond the reach of conventional fluid models. Continuing as a MathWorks Fellow, Thomas is expanding the use of MATLAB and Simulink to support experimental design and 3D magnetostatic modeling for campaigns at the COBRA facility at Cornell. He also contributes to PUFFIN, a next-generation long-pulse generator under development at MIT and Cornell, where he leads efforts in calibration and control system development, as well as automated data acquisition using MathWorks tools. His work could transform how researchers replicate and study high-energy astrophysical phenomena in the lab, advancing experimental capabilities and plasma diagnostics alike.

Zhuo Liu
Nuclear Science and Engineering

Zhuo Liu is a graduate student in nuclear science and engineering, advancing our understanding of turbulent and reconnecting plasmas—fundamental processes that shape phenomena ranging from solar flares to fusion energy systems. His work focuses on electron-only magnetic reconnection, kinetic turbulence, and ion-acoustic instabilities, employing a powerful combination of analytical theory and massively parallel simulations. As a MathWorks Fellow, Zhuo will investigate high-beta plasma environments, utilizing particle-in-cell simulations to model reconnection processes relevant to Earth’s magnetosheath and other astrophysical settings. MATLAB is integral to his computational workflow. Leveraging toolboxes such as Parallel Computing and Signal Processing, Zhuo has systematically characterized reconnection rates, turbulence spectra, and energy transfer mechanisms. His MATLAB-driven workflows also contribute to the development and validation of numerical models widely used in plasma physics research. By unraveling the complexities of energy transport and dissipation in extreme plasma environments, Zhuo’s work is positioned to accelerate fusion energy advancements and deepen our understanding of cosmic plasma dynamics.