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 … Read more
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 … Read more
Nicholas A. Kotov is Irving Langmuir Distinguished University Professor in Chemical Sciences at the University of Michigan. He is a pioneer of theoretical foundations and practical implementations of complex systems from ‘imperfect’ nanoparticles that offer a vast field for the application of data science and machine learning. Chiral nanostructures, biomimetic nanocomposites, and graph theoretical representations … Read more
My lab studies how information from one sensory system influences processing in other sensory systems, as well as how this information is integrated in the brain. Specifically, we investigate the mechanisms underlying basic auditory, visual, and tactile interactions, synesthesia, multisensory body image perception, and visual facilitation of speech perception. Our current research examines multisensory processes … Read more
Michael is an Assistant Professor of Energy Systems at the University of Michigan’s School for Environment and Sustainability and PI of the ASSET Lab. He researches how to equitably reduce global and local environmental impacts of energy systems while making those systems robust to future climate change. His research advances energy system models to address … Read more
My research focuses on developing and using methods in machine learning and natural language processing to learn about society from text, promoting better and more reproducible data science, and studying the societal impacts of these technologies. I collaborate with colleagues in statistics, linguistics, political science, and other areas of computational social science to investigate how … Read more
His research interest lies in the intersection of signal processing, data science, machine learning, and numerical optimization. He is particularly interested in computational methods for learning low-complexity models from high-dimensional data, leveraging tools from machine learning, numerical optimization, and high dimensional geometry, with applications in imaging sciences, scientific discovery, and healthcare. Recently, he is also … Read more
Dr. Gen Li is an Associate Professor in the Department of Biostatistics. He is devoted to developing new statistical methods for analyzing complex biomedical data, including multi-way tensor array data, multi-view data, and compositional data. His methodological research interests include dimension reduction, predictive modeling, association analysis, and functional data analysis. He also has research interests … Read more
Lu’s research is focused on natural language processing, computational social science, and machine learning. More specifically, Lu works on algorithms for text summarization, language generation, argument mining, information extraction, and discourse analysis, as well as novel applications that apply such techniques to understand media bias and polarization and other interdisciplinary subjects. Awards and Accomplishments