Y Z (Yang Zhang)

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YZ’s research can be summarized into two words: Matter and Machine. On the Matter side, his group studies far-from-equilibrium physics. They synergistically combine and push the boundaries of statistical and stochastic thermodynamic theories, accelerated molecular simulations, understandable AI/ML/DS methods, and neutron scattering experiments, with the goal of significantly extending our understanding of a wide range of long timescale phenomena and rare events. Particular emphasis is given to the physics and chemistry of liquids and complex fluids, especially at interfaces, driven away from equilibrium, or under extreme conditions. On the Machine side, leveraging their expertise in materials and modeling, his group advances the development of soft robots and human-compatible machines, swarm robots and collective intelligence, and robots in extreme environments, which can lead to immediate societal impact.
• Matter
o Far-from-equilibrium physics, long timescale phenomena, and rare events (statistical and stochastic thermodynamic theories, accelerated molecular simulations, understandable AI/ML/DS methods)
o Neutron scattering, sources, and instrumentation
o Physics and chemistry of liquids and complex fluids, especially under interfacial/non-equilibrium/extreme conditions (water, metallic liquids, molten salts, ionic liquids, electrolyte solutions)
• Machine
o Soft robots and human-compatible machines
o Swarm robots and collective intelligence
o Robots in extreme environments

Ram Vasudevan

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The ROAHM (Robotics and Optimization for the Analysis of Human Motion) Lab seeks to understand and improve human and robot interaction with one another and with the environment. We devise techniques to diagnose unsafe behavior and construct controllers that can then safely intervene or aid in retraining. Our work seeks to develop practical solutions to these problems while providing mathematical guarantees regarding performance. We illustrate the utility of our work on a variety of robotic systems (some of which are depicted in the image below.

Maani Ghaffari

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The Computational Autonomy and Robotics Laboratory (CURLY) at the University of Michigan develops novel algorithms for mobile robots concerning multimodal perception, learning, autonomous navigation, exploration, and environmental monitoring.

Present-day robotic algorithms and systems lack sufficient robustness to operate reliably in environments that are unknown a priori. We develop autonomous robotic exploration in unknown and extreme environments such as forests, deserts, and underwater. Our research enables a robot to navigate and traverse a disaster site, collaborate in teams with other robots, or with humans for search and rescue or scientific exploration and discovery. The outcome of our research is an enabling scientific technology for directly sampling and analyzing surface and subsurface compositions.

Liang Qi

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My research fields are investigations of the mechanical and chemical properties of materials by applying theoretical and computational tools, including first-principles calculations, atomistic simulations, multiscale modeling, and how to apply machine learning to overcome the size/time scale limitations that widely exist in these computational studies. As shown in the following picture, a recent example of my research is to construct the physics-informed machine learning surrogate model to quickly predict material parameters related to their strength and ductility, otherwise, these parameters can only obtain from costly first-principles calculations and/or atomistic simulations.

Surrogate models based on physical descriptors to predict the planar fault energies of multicomponent alloys in order to quickly estimate the strength and ductility of these alloys in a large compositional space (Acta Materialia, 210 (2021) 116800).

Bruce Maxim

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I use genetics algorithms and data mining to assist with the task of refactoring large software products to improve the quality their quality.

Software Engineering and Game Design

Jane E. Huggins

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The UM-DBI Laboratory’s current work focuses on the development of electroencephalogram (EEG)-based brain-computer interfaces (BCIs) into practical clinical tools for use by people with physical impairments. Barriers to clinical use include signal processing challenges, selection of tasks for BCI operation, interactions between BCIs and different conditions causing impairments, and technical support issues to troubleshoot in-home BCI use. We are developing algorithms for real-time analysis of EEG to determine which key a user wants to activate or to identify whether the user currently wants to activate any key at all. We also analyze survey data to guide BCI design decisions.

Typing with a brain-computer interface.

Sabina Tomkins

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My research utilizes computational social science and artificial intelligence to derive contextually informed algorithmic frameworks for understanding individuals and the social systems which influence their behavior, and for supporting positive behavior change within these systems. In particular, I analyze policy and behavior, and apply interactive behavior support and predictive algorithms, within the areas of higher education, environmental sustainability, and political participation.

Individuals are increasing spend their time in quasi-digital environments generating a wealth of data traces with which to understand their behavior. However, this data can produce its own set of challenges (sparsity, heterogeneity, connectivity) and generating new understandings of human behavior necessitates novel statistical and computational approaches. In my work I address these challenges by developing probabilistic frameworks which can capitalize on structure between data instances, producing state-of-the-art performance in tasks from predicting household appliance energy consumption with an energy disaggregation framework to predicting the movements of human traffickers.

Ruiwei Jiang

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Ruiwei works on discrete optimization under uncertainty. Many practical engineering problems search for good discrete decisions under uncertain or even incomplete inputs. Ruiwei’s research aims to develop data-enabled stochastic optimization (DESO) models and solution methodology that bring together data analytics, integer programming, stochastic programming, and robust optimization. Together with his collaborators, Ruiwei applies DESO approaches to various engineering problems, including power and water system operations, transportation systems, and healthcare resource scheduling.

The recognitions of Ruiwei’s work include a NSF CAREER award, two INFORMS Junior Faculty Interest Group paper awards, and an Outstanding Teaching award from the IISE Operations Research Division.

Sean Johnson

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Sean Johnson is an observational astronomer and primarily studies galaxies, supermassive black holes, and the surrounding gas supplies that fuel their growth. By combining datasets from space-based and large ground-based telescopes, he studies the physical conditions of the gas supplies that enable galaxies to continue forming stars, and identifies the chemical signatures of heavy elements that are produced in supernova explosions and deposited into intergalactic space by galactic-scale winds and galaxy interactions. He is co-PI of the Cosmic Ultraviolet Baryon Survey (CUBS) and leading wide-field ground-based follow-up for the MUSE Quasar Blind Emitter Survey (MUSEQuBES) which are increasing samples for CGM studies at z<1 by more than an order-of-magnitude by combining high quality HST UV spectra, deep integral field observations with MUSE, and wider field galaxy surveys with Magellan. Before joining U. Michigan, Sean was a Hubble and Carnegie-Princeton Postdoctoral Fellow at Princeton University where he volunteered regularly with the Prison Teaching Initiative. Prior to that, he was a graduate student at The University of Chicago.

Deepak Nagrath

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Our lab is focused on answering the question-What is the role of tumor microenvironment in modulating cancer cell metabolism? We have developed several metabolic isotope tracing and 13C-based metabolic flux analysis developed in our lab. cells. Notably, we are focusing on personalized metabolic therapy and circulating tumor cell organoids and tumor tissue slices in pancreatic, lung, and breast cancers. We integrate high dimensional imaging, tissue engineering, metabolic engineering, bioinformatics, machine learning, and systems biology tools developed in our labs to understand metabolic interactions between cancer and stromal cells. Using our recently developed platform, collateral lethal identification of metabolic targets (CLIM)-a machine learning and genome-scale metabolic flux analysis-based framework, we elucidate the broad efficacy of targeting MTHFD2 despite distinct cancer genetic profiles co-occurring with UQCR11 deletion and irrespective of stromal compositions of tumors.” Our CLIM method can be used to identify metabolic vulnerabilities in other cancers and could serve as a precision treatment plans for a host of malignancies.