Jamison Sloan is a PhD candidate whose research explores new possibilities in the field of quantum optics. Specifically, Jamison’s work aims to use extreme effects in nonlinear optics to generate quantum states of light, which have not yet been achieved. As a MathWorks Fellow, Jamison will pursue several projects that cover new ground in quantum optics, including: applications of machine learning and other data-driven methods to discover new aspects of multimode laser physics; investigations of dynamical vacuum effects with the goal of enhancing these effects using photonic nanostructures; and novel concepts for creating entangled photons with controllable entanglement properties based on sending refractive index perturbations on controlled trajectories. MATLAB is an essential tool across Jamison’s theoretical and experimental research. His contributions—including multiple, new applications of MATLAB—are already gaining major interest among fellow researchers, and his current work has the potential for significant impact in experimental optics and related fields.

Jake Song is a PhD candidate whose research combines materials engineering and soft matter physics with emerging trends in mechanics and rheology. Specifically, Jake studies the structural and dynamical properties of soft materials, from foods and cosmetics to cells and tissues, with the goals of understanding the microscopic origins of these dynamics and developing new constitutive models for engineering applications of soft materials. Through his innovative use of MATLAB, Jake has created valuable user-friendly tools for capturing the mechanical behavior of soft materials that lend new insights on the physical process that underlies the mechanical properties. As a MathWorks Fellow, Jake is refining these tools and applying them to fresh challenges, including recently to “self-healing” metal ion-cross-linked gel materials and to a large-scale review of the macroscopic dynamics of soft materials. His work has already yielded valuable knowledge and methods for soft materials research, and could be of great utility in the chemical, pharmaceutical, foods, consumer product, medicine, and materials industries. In addition, Jake’s current work on the complex dynamics of soft materials has the potential to advance the study of other complex phenomena including ecological cycles, forest fires, and extreme weather.

Rachel Sun is a master’s candidate whose research applies innovative computation tools in the development of new nanofabrication techniques. Supported by a MathWorks seed grant, Rachel developed a groundbreaking fabrication method for microscale magnetic metamaterials with tunable properties, actuated by magnetic fields using a nanoscale 3-D printer. As a MathWorks Fellow, Rachel will continue to explore properties and fabrication approaches for acoustic metamaterials and reconfigurable, architected materials. Acoustic metamaterials that block specific frequency ranges have been realized at larger scales; the goal of Rachel’s current project is to advance understanding of these materials’ acoustic properties at the microscale and higher frequency ranges. She has utilized and created numerous MATLAB tools during her research and shared flexible and innovative tools, such as an acoustic metamaterial analysis script, with the MathWorks community. Rachel’s research could serve to speed the development of valuable metamaterials for a wide range of applications from reconfigurable waveguides and logic switches, to remotely actuated micro-medical and computing devices, and other smart, multifunctional materials.

Nadia Tahsini is a PhD candidate whose research explores the chemistry of atmospheric particulate matter (PM), a factor with significant influence on climate, air quality, and human health. Specifically, Nadia is investigating the role of peroxy radicals (RO2), which are key intermediates in the oxidation of volatile organic compounds (VOCs) emitted into the atmosphere. These insights into RO2 chemistry could serve to increase the accuracy of global atmospheric models. As a MathWorks Fellow, Nadia will pursue a combination of experiments (atmospheric chamber studies) and simulations powered by MATLAB’s Framework for Zero-Dimensional Atmospheric Modeling (F0AM) and the Master Chemical Mechanism to study the impact of key parameters, including temperature, on RO2 reactivity and other VOC oxidation systems. Her results, derived using MATLAB tools, will be incorporated back into F0AM for the benefit of the atmospheric chemistry community. Nadia’s research has the potential to help build a more complete understanding of organic aerosol formation in the atmosphere, leading to stronger global models that better quantify the effects of aerosol on global climate and air quality.

Wenhui Tang is a PhD candidate whose research seeks to provide new understandings of cell behaviors in biological systems from physical and mechanical perspectives. With the support of her second MathWorks Fellowship, Wenhui will pursue a project focused on understanding the mechanics of development and growth of human lung alveolar systems in 3-D. Utilizing images collected by a confocal microscope, and MATLAB tools for computation, modeling, and data processing, she will study collective cell migration and cellular forces that change during the growth of alveolospheres to ascertain cell shape, circularity, area, perimeter, aspect ratio, and volume for hundreds to tens of thousands of cells at once. Her work has already revealed that cells have the ability to collectively sense substrate curvature and regulate their multicellular fluidity in 3-D multicellular systems. Currently, she is digging further into mechanical properties, coupled with cell migration characteristics, to understand more complex physiological and pathological processes. By broadening our understanding of the physical rules that govern complex multicellular systems, Wenhui’s work could support a broad spectrum of research areas in biology, engineering, and human health.

Dousabel Tay is a PhD candidate whose work integrates bioengineering and mathematical modeling to advance clinical diagnostics. Specifically, Dousabel is developing new approaches for rapid diagnostic testing, in a unique integration of protein engineering and microfluidics, to improve the tests’ performance, turnaround time, scalability, and affordability. With the support of a MathWorks Fellowship, Dousabel will expand and refine an end-to-end MATLAB model she has created, which predicts the performance of diagnostic tests and sheds light on which parameters and choices are more likely or unlikely to improve performance. Recently, she began designing and testing microfluidic devices that can be used to screen combinatorial libraries of binding proteins. In addition to developing and sharing tools of value to the wider bioengineering community, Dousabel is making important contributions to the growing sphere of research that approaches medical diagnostic tests as engineered systems and uses innovative mathematical models to better understand the tests and apply them to improve human health.

Nicolas (Nico) Gomez Vega is a PhD candidate whose research in electroaerodynamics (EAD) has led to fundamental advances in our understanding of the physics of solid-state aerodynamic devices. Specifically, Nico has devised new technical solutions to increase the efficiency of EAD propulsors. With the support of a MathWorks Fellowship, Nico will advance his current research exploring the use of EAD devices for in-atmosphere propulsion, with the objective of developing novel technologies to propel aircraft and their payloads cleanly and efficiently. Among other useful outcomes of the project, Nico has demonstrated that multistage ducted (MSD) EAD thrusters may provide significant performance improvements over conventional EAD thrusters. Next steps include experimental demonstrations and characterization of these thrusters. Nico has made extensive use of MATLAB in the experimental and analytical aspects of this research. His cutting-edge work is already helping to set the direction of EAD research and promises to deliver more insights and innovative tools to expand the field.

Eric Kevin Wang is PhD candidate whose research in the field of robotics seeks to develop new techniques to make autonomous robotic manipulation more efficient and rich. Specifically, Eric is developing new planning algorithms for dynamic non-prehensile manipulation, which offers advantages in applications where object size, geometry, and surface properties are uncertain and quasistatic motions limit the solution space of complex transport tasks. With the support of a MathWorks Fellowship, Eric will expand his promising work in geometric modeling of dynamic frictional interactions that are amenable to fast trajectory optimization and robust control. The larger goals of Eric’s research are to provide robust safety for non-prehensile object transport, enable dynamic motions with better time/effort optimality than quasistatic formulations, and enhance safety-critical control when making and breaking contact with objects. MATLAB tools are an essential component of Eric’s work, which could offer important advances in robotic material transportation in many industrial and commercial settings.

Guoqing Wang is a PhD candidate who is exploring the frontiers of physics and quantum science for the purpose of building fundamental theory and creating next-generation quantum-based technologies. His work has already yielded breakthrough applications, such as high-performance quantum sensors, and contributions in theory, including quantum information protection and simulation of novel quantum phases. As a MathWorks Fellow, he will advance several ongoing projects, all powered by MATLAB tools that he has utilized and/or created. These projects explore: protection and control of large-scale quantum devices and the qubits that comprise them; simulation of novel phases and symmetries and the development of periodically driven quantum systems; and creation of improved quantum sensors for the nanoscale detection of vector AC fields—a valuable capability in fundamental physics research, microwave engineering, material science, and biological applications. Guoqing’s wide-ranging and innovative work is already contributing to multiple projects within and beyond MIT and has potential to impact future research in the field. As his work progresses, he looks to create a full MATLAB-based toolkit for the quantum research community.

Tsun-Hsuan (Johnson) Wang is a PhD candidate whose research integrates computer science with artificial intelligence and machine learning to create real-world autonomous systems, such as robot cars, and to explore foundational questions in robot control. With the support of a MathWorks Fellowship, Johnson will expand his innovative work to create high-fidelity environments and sim-to- real approaches for control learning that will safely expose agents to challenging and dangerous edge cases. The goal of this research is to develop robot systems that can learn from prior experiences yet are robust enough to guarantee safety under real-world conditions and untrained scenarios. Johnson recently developed and implemented VISTA, a system based on this line of research that is now freely available to the research community. MATLAB supports vital aspects of Johnson’s work, chiefly developing algorithms in simulated environments and generating data for policy learning and robot deployment. His work has already yielded valuable contributions to autonomous driving and has the potential to broadly advance machine learning for safety-critical applications in many industries.

Wei-Chen (Eric) Wang is a PhD candidate who applies innovative computational modeling to solve problems in biology and medicine. Specifically, Eric is building novel predictive models to understand the computational principles underlying movements of biological systems, with the goal of transforming neuromotor rehabilitation, humanoid robotics, and animation. As a MathWorks Fellow, Eric will infer the reward functions of naturalistic movements across subjects and species and examine how they cluster in the reward space using autoencoders and inverse reinforcement learning. His current research is yielding key insights into how rewards affect the motor plans of individuals, how these plans are affected in subjects with neuromotor disorders, and how reward dependent behavior changes through development. MATLAB is indispensable to Eric’s work, and he has contributed numerous open-source tools to empower other researchers. Through his research, Eric is helping to build a more complete understanding of human movement and creating powerful new computational tools to address a broad range of scientific and medical challenges.

Reimar Weissbach is a PhD candidate, as well as an MBA candidate through MIT Leaders for Global Operations, whose research applies innovative computation approaches in combination with experimental techniques to advance the field of additive manufacturing (AM). Specifically, Reimar investigates the role of particle adhesion in the spreading of thin powder layers used in metal 3-D printing. As a MathWorks Fellow, he will pursue an ambitious project to apply numerical modeling to all stages of the widely used process of laser powder bed fusion (LPBF), with the goal of understanding governing LPBF mechanisms, such as the dynamics of powder spreading and the rapid melting and solidification that determine component density and microstructure. MATLAB is a critical tool in Reimar’s research, enabling him to develop custom numerical models and efficiently analyze large amounts of experimental data. His work advances the fundamental understanding of process physics, defect creation mechanisms (quality), and rate limits (throughput). It could help to accelerate product development by predicting LPBF process parameters for existing materials and inform the development of new materials with tailored properties for the growing AM industry.

Elizabeth Whittier is a PhD candidate whose research in magnetic imaging and bioinstrumentation is speeding the development of next-generation imaging systems and nanomaterials. Specifically, Elizabeth’s work is focused on the creation of a novel magnetic particle imaging (MPI) system to visualize magnetic nanomaterials in live animals and to enable functional imaging and spectroscopy at unprecedented speeds. She has already reached critical milestones in the project, including the successful design of a high-frequency, high-power resonant tank, precisely tuned detection coils, and a system of Helmholtz coils; and the establishment of parameters and geometries of all the electromagnets of the instrument, through MATLAB modeling. With the support of a MathWorks Fellowship, Elizabeth will complete the fabrication of her instrumentation system, which promises to be the most powerful, highest-resolution MPI system in the world. Her work draws extensively on MathWorks tools, and it has the potential to add transformational new capabilities to magnetic imaging and the field of bioelectronics.

Hanshen Xiao is a PhD candidate whose research explores the area of privacy-preserving machine learning. Specifically, he investigates information-theoretical security, robust optimization, and signal processing, from theoretical and practical perspectives. MATLAB is an indispensable tool in Hanshen’s work, supporting such tasks as large-scale spectrum analysis, testing learning algorithms, and estimating higher-order cumulants. His recent accomplishments include studies in high dimensional differentially private stochastic optimization with heavy-tailed data and task augmentation for private (collaborative) learning on transformed data. With the support of his MathWorks Fellowship, Hanshen will pursue a cutting-edge approach in private transformation- based machine learning with the goals of improving security properties and demonstrating that transformed data learning maintains utility. This work has already yielded a promising result in collaborative learning that does not require collusion assumptions unlike established multiparty computation schemes. This project, and Hanshen’s future research, have the potential to advance privacy-preserving machine learning, offering both new theoretical knowledge and practical applications.

Oliver Xie is a PhD candidate whose work combines chemical, biochemical, and genetic engineering with cutting-edge computational approaches to explore protein materials. Specifically, Oliver is studying block copolymers and the unique properties arising from their self-assembled morphologies. As a MathWorks Fellow, he is developing a novel self-consistent field theory (SCFT), which predicts the self-assembly and phase behavior of sequence-defined polymers, and he is using MATLAB tools to uncover the relationship between polymer sequence and structure. His SCFT enables the representation of a polymer sequence as a continuous function, which dramatically simplifies simulations of complex polymers, including tapered polymers, polymers with 20 or more blocks, and even disordered proteins. By demonstrating that the useful dimensionality of sequence-controlled design is far lower than the actual experimental dimensionality explored in traditional high-throughput and ML-driven approaches, Oliver’s work enables smarter experiments for the discovery of new materials. Next steps in his work include building a design-test-build cycle for sequence-controlled polymers to discover unique 3-D nanostructures. Through his work, Oliver is addressing core challenges in polymer science and offering powerful new insights into the fundamentals of self- assembly.

Haowei Xu is a PhD candidate whose research applies computational physics and materials science to innovative studies of light-matter interaction in solid-state systems. Haowei’s work has already yielded numerous innovative theoretical results and predictions related to the non-linear responses of nuclear, electronic, and ionic systems, such as the optical control over both electronic and nuclear spins, light-induced phase transitions in materials, as well as new insights into light- matter interactions in topographical materials. Many of these predictions have been verified in experiments. As a MathWorks Fellow, Haowei will expand his work in several areas, including an efficient new interface between optical photons and nuclear spins and several novel nonlinear optical effects, potentially useful for energy harvesting, light detection, and the characterization of materials. MATLAB tools are foundational to his work, particularly for the creation of robust algorithms to work with large-scale matrices, and he is proud to share new MATLAB tools, such as codes for calculating nonlinear optical responses, to enhance and boost the work of other researchers.

Haiqian Yang is a PhD candidate whose research seeks to broaden understanding of the basic principles governing multicellular system dynamics in space and time. This knowledge is essential to advancing a wide variety of health and bioengineering applications, including tissue regeneration, suppression of tumor invasion, and control of embryo morphogenesis. Haiqian’s work utilizes cutting-edge optical tools to study the spatial and temporal organization of multicellular systems and to explore cellular regulation of mechanical properties in a 3-D developing living system. With the support of his second MathWorks Fellowship, Haiqian will extend this research through two projects. The first project explores the anisotropic nonlinear mechanics of extracellular matrices using “optical tweezers” that he helped to design in MATLAB. The second project, which also draws extensively on MATLAB, builds on Haiqian’s promising studies of the principles controlling the spatial order and dynamics in multicellular systems to better understand how this framework relates to mechanical stresses and to elucidate more abstract physical quantities, such as entropy in a multicellular living system.

Jin (Harvey) Yang is a PhD candidate whose research aims to decipher the mechanisms underlying 3-D genome organization and enhancer-promoter interaction. As a MathWorks Fellow, Harvey seeks to develop new methods in synthetic 3-D genome biology. In past and present work, he has made innovative use of MATLAB and created new open-source tools for the MathWorks community, including modeling synthetic gene circuits through SimBiology to guide experimental testing. In his recent project on the repair of DNA double-strand breaks, he developed models and analytical theory to elucidate how loop extrusion could mediate synapsis of two DNA double-strand break ends. In his current project, Harvey aims to create a new bottom-up synthetic biology strategy for understanding how 3-D genome organization regulates genome function. As his work advances, he plans to make extensive use of MATLAB for image segmentation and analysis and to build new tools that will enhance the impact of his own work and may have broad applications for other researchers.

So-Yoon Yang is a PhD candidate who is applying her expertise in semiconductors to the creation of novel, functional devices for biomedical applications. So-Yoon’s highly interdisciplinary research builds upon her prior work developing flexible, wearable sensor patches and transistor-based biosensors. Her MathWorks Fellowship will support So-Yoon’s ongoing work developing a long- term energy harvesting system for ingestible electronics for diagnostic and therapeutic devices. By harvesting chemical energy from the gastrointestinal tract, this new technology could sustain electrical power for over a month. Her current work also includes the development of a modular circuit system and a MATLAB-based modular software system for next-generation ingestible electronics, as well as contributions to a new electronically triggered drug delivery system for ingestible electronics. Through her cutting-edge research, So-Yoon is paving the way for major advances in bioelectronic devices with the potential to greatly impact health and medicine.

Yuzhe Yang is a PhD candidate working at the intersection of machine learning (ML), artificial intelligence (AI), and digital health. He is building innovative neural learning pipelines that combine MATLAB’s capacity to manipulate signals and matrices with contemporary deep learning approaches with the goal of developing new modalities and applications for digital health. His recent work includes a project that tackled the problem of imbalance in real-world data and yielded new tools and insights for self-supervision and feature-level smoothing in deep learning. In another project, he developed a novel approach—now in use—for contactless, in-home monitoring of Covid-19. As a MathWorks Fellow, he has developed (and will soon begin testing) the first ML-based solution that uses a person’s nocturnal breathing signals to detect Parkinson’s disease, estimate disease severity, and track disease progression. Making innovative use of MATLAB to address core challenges in deep learning, Yuzhe is making valuable contributions to smart health sensing solutions and advancing the field of digital health.