Maz Abulnaga is a PhD candidate whose work is changing the way researchers and physicians study the placenta with the goal of improving health outcomes for mothers and babies. Specifically, Maz develops algorithms for studying the placental shape in fetal magnetic resonance imaging, an imaging modality that is being used to track the health and function of the placenta, identify pathology, and support patient outcomes. Maz has made several contributions to the theoretical and algorithmic foundations of volumetric geometry processing and has successfully applied his models to clinical research of the placenta. With the support of his MathWorks Fellowship, Maz will develop geometry processing and machine-learning algorithms to quantify placental shape and functional changes throughout pregnancy, which are necessary to identify pathology and plan pregnancy outcomes. He has recently developed a novel approach to provide a standardized representation of placental shape that is being used by researchers globally. He envisions developing a set of open-source tools to enable clinical studies to improve our understanding of placental and fetal development with the goal of revolutionizing fetal-maternal health.

Bernardo is a PhD student in mechanical engineering whose research examines the synthesis of robust strategies for grasping and dexterous manipulation. Specifically, he is examining the problem of caging, a geometric property by which a set of fingers manipulate an object by trapping it rather than immobilizing it. He has developed an explicit optimization approach to formulate the caging condition as a convex mixed-integer optimization problem. This was the first of its kind, since caging had previously been approached as either a topological or computational geometry problem. More recently, he has extended the formulation to synthesizing grasping strategies with certificates of correctness, a problem that is of high interest today both in academia and industry. He uses MATLAB’s Robotics Toolbox, Optimization Toolbox, and Deep Learning Toolbox as the main prototyping interface. In addition, his work has resulted in several tools that have been made open to the robotics and MathWorks communities, including ABB ROS Interface, Contact-TrajectoryOptimization Models, and Certified Grasping Toolbox. Bernando earned a BSc in electronics engineering from Universidad Simón Bolívar, Venezuela

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.

Shashank is a PhD student in mechanical engineering whose research primarily focuses on the development of reduced-order, rate-dependent methods for a variety of granular intrusion problems, such as meteorite impacts and animal and vehicular locomotion in sands and deserts (a field referred to as terramechanics). He uses large-scale, detailed numerical simulations to understand the physics of such scenarios, which in turn allows him to develop robust reduced-order models that can be run in real time. He uses MATLAB for various purposes in his research, most importantly for the implementation of reduced-order models for granular intrusion, allowing for the real-time simulation of diverse intrusion scenarios with different system properties and intruder shapes. Shashank earned a BTech in mechanical engineering fromIITGandhinagar, India and an SM in mechanical engineering from MIT.He also worked as a scientist at the Defense Research and Development Organization, India before joining MIT

Mohammad is a PhD student in civil and environmental engineering whose research is focused on explaining and predicting biodiversity changes using mathematical models. Specifically, his goal is to provide closed-form solutions using computational methods in order to skip the impossible task of simulating all possible parameter values and combinations and to establish rigorous and testable estimates about the probability of persistence of species. He is developing his own code and integrating libraries from MATLAB with the goal of generating a computational package that will allow researchers to work with different ecological models, derive the range of parameter values (or combinations of parameter values) compatible with the persistence of a given species in an ecological system, and estimate the probabilities (or risks) of species extinctions or species invasions. Mohammad earned a BS in mathematics and a BS in electrical engineering from Rensselaer Polytechnic Institute and an SM in civil and environmental engineering, an SM in electrical engineering and computer science, and an SM in computation for design and optimization from MIT.

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.

Pasquale is a PhD student in aerospace engineering. He is developing general-purpose tools for estimation that are robust to corrupted information and adversarial attacks, run in real-time, and require minimal tuning. His interests include safe and trustworthy perception with application to single and multi-robot autonomous systems. He uses MATLAB on a regular basis, including the Computer Vision Toolbox and Robotics System Toolbox for his work on robust estimation. He earned his SB in computer science from University of Pisa, Italy, an SM in embedded computing systems from Scuola Superiore Sant’Anna of Pisa, Italy and an SM in aeronautics and astronautics from MIT. Prior to MIT, he was a research scientist at the United Technologies Research Center, Ireland.

Nicolas is a PhD student in electrical engineering and computer science whose research focuses on medical imaging technology. He is trying to solve challenging MRI problems by creating and distributing the hardware and computational tools needed for simultaneous optimization of coil geometries, encoding field patterns, and received-signal reconstruction algorithms. He has observed that the steps, or modules, that make up most MRI sequences often have conflicting patient-specific field pattern requirements. Nicolas developed a general MATLAB-based framework for use at scan time that optimizes the field patterns for each sequence module. The framework reads patient rapid-scan data from the imager, exploits the coil pre-characterization and convex formulations to efficiently compute sets of sequence-module optimized coil currents, and then transfers the coil currents sets, along with triggering conditions, to the coil driver array. Nicolas earned an SB in electrical engineering from MIT.

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.

Toros Arikan is a PhD candidate whose interdisciplinary work integrates signal processing, machine learning, and computational imaging to significantly expand the capabilities of sensing systems for acoustic, radio frequency (RF), and optical modalities. With the support of a MathWorks Fellowship, Toros will pursue innovative research that takes a new, and potentially highly useful, view of system design. Traditionally, system designs are based on relatively low-dimensional models. Toros’s work, by contrast, embraces high-dimensional models and finds potential in that complexity for better performance and surprising new capabilities. One example is the presence of unknown complex obstacles and reflecting structure in physical environments that can improve object localization and scene imaging performance. MATLAB tools play a central role in Toros’s research, serving as his algorithm prototyping and experimental data analysis environment. The tools, methods, and insights emerging from this work hold important potential for future research and applications, including the acoustic and RF communities. To help realize these new possibilities, Toros will make the resulting tools of his research widely available through the MathWorks File Exchange.

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

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.

Maanasa Bhat is a PhD candidate who is integrating interests in health, energy, and materials science to develop innovative new approaches to synthesis of nanomaterials. As a MathWorks Fellow, Maanasa is designing a flame-based synthesis process to manufacture nanomaterials for low-cost sensors and photocatalysts with a wide range of potential environmental and health applications. The goal of Maanasa’s work is to build a broader understanding of the physical and chemical mechanisms involved in the flame synthesis process. The aim is to strengthen connections between the input operating conditions and the composition and morphology of output products to achieve high controllability. MathWorks tools have been indispensable in this research. As her project progresses, Maanasa hopes to tailor the novel flame synthesis process she has developed for specific uses such as the detection of methane leaks, pollutant emissions in air, as well as breath diagnosis for hard-to-detect conditions such as endometriosis and fibroids.

Harsh G. Bhundiya is a PhD candidate who is applying structural engineering, computational modeling, and design techniques to purse his interest in space structures. His work is ushering in a new paradigm for constructing large structures in orbit, called in-space manufacturing, with an enormous potential impact on space applications. Drawing on MathWorks tools, Harsh has reached exciting milestones, including the development of a path-planning framework called Bend-Forming, which enables the creation of paraboloids, isogrid columns, and other structural elements that can be linked together to form large structures such as RF reflectors and solar sails. As a MathWorks Fellow, Harsh will characterize the compression behaviors of these structures and study the effects of processing defects on their buckling response through experiments and nonlinear finite element analysis. Simulink and Simscape Multibody will play critical roles in this work. Harsh was pleased to share previous work on MathWorks File Exchange, and will continue to share his research, as he seeks to create a new generation of large space structures for diverse applications.

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.

A PhD student in electrical engineering and computer science, Roberto is a member of the Organic and Nanostructured Electronics Lab. He uses MATLAB’s versatile Partial Differential Equation (PDE), Global Optimization, and ParallelComputingToolboxes to solve, model, and fit the group’s experimental data sets on carrier recombination and diffusion in semiconductors for optoelectronic applications. Leveraging the PDE Toolbox, his most recent work, “AccurateDetermination of Semiconductor Diffusion Coefficient Using Confocal Microscopy,” explores common pitfalls in modeling charge carrier diffusion in semiconductors that were elucidated by simulations carried out using the PDE toolbox. He is now focused on modeling semiconductors for solar applications that present anisotropic diffusion and complex boundaries using the PDE Toolbox coupled with the Parallel Computing and the Global Optimization Toolboxes to optimize multi-variable minimization problems. The Parallel Computing Toolbox has been essential in bringing the computation times down to manageable time frames, allowing him to explore multiple schemes that could represent the complex carrier diffusion anisotropy that is observed. He earned an SB in electrical engineering and physics fromMIT.

Peng Cao is a PhD candidate who has demonstrated innovative use of MathWorks tools to make fundamental contributions to machine learning and digital health. Specifically, Peng’s research focuses on sensing systems, data analysis, and radio signals. MATLAB is an essential tool in this work, for numerical optimization, complex signal processing, and analysis and manipulation of radio signals and electromagnetic fields. The MathWorks Fellowship will support Peng’s cutting-edge research in several domains. She has developed new approaches to learning from crowdsourcing labels, including robust information-theoretic loss functions that enable learning from multiple labels and noisy labels, as well as semi-supervised, multimodal learning. Peng has also made notable advances in human-motion modeling with a transformer-based deep learning model for generative modeling of 3-D human motion. Another project yielded a new approach to enable robots to move quickly in indoor environments without colliding with people—one of the first such projects to demonstrate indoor localization around corners using radio signals. Most recently, Peng developed the first contactless system to monitor blood oxygen by employing a neural network to assess oxygen levels within 2 to 3 degrees. This work, which was validated with Covid-19 patients, has enormous potential for telehealth applications.

Yifan (Henry) Cao is a PhD candidate working at the frontier of a new materials science field: high- entropy alloys (HEAs). HEAs offer valuable traits, such as high strength and fracture resistance. As a MathWorks Fellow, Henry will be expanding a new method he has developed to optimize machine learning interatomic potentials (MLP) in cross-scale atomistic simulations, thereby predicting HEA
properties with greater efficiency than current methods. Although HEAs are created by mixing many chemical elements, local chemical ordering (LCO), the prominent mechanism underlying many of these properties, remains poorly understood. Henry’s research seeks to close that knowledge gap. Two MathWorks tools will play a central role in the project: Simulink, which will enable the development of a well-rounded MLP training set, and MATLAB, which will be used to extract physically important features and plot relevant data obtained from HEA cross-scale simulations. Henry also plans to explore potential applications of these tools in mesoscale, atomic, and quantum mechanics modeling approaches.

Katherine is a master’s student in aeronautical and astronautical engineering and member of the Engineering SystemsLaboratory. A longtime MATLAB and Simulink user, her current research involves population modeling and simulation of pedestrian activity through transit environments. She is implementing agent-based modeling and simulation in order to capture the emergent phenomena of population groups moving across complex sites, both here on Earth and on Mars. She has used MATLAB to numerically simulate pedestrian flows in hallways using a social-force model and is currently developing a Java-based integrated, flexible platform to simulate agent behavior across multiple agent pools and simulation scenarios. Katherine earned a BS in aerospace engineering fromUniversity of Illinois.