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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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