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AIS 2023 AI Mentor

Katie Skinner

My research spans robotics, computer vision, and machine learning with a focus on enabling autonomy in dynamic, unstructured, or remote environments across field robotics applications (air, land, sea, and space). In particular, my group focuses on problems that rely on limited labeled data.

Majdi Radaideh

Prof. Majdi Radaideh leads the Artificial Intelligence and Multiphysics Simulations lab (AIMS), which focuses on the intersection between nuclear reactor design, nuclear multiphysics modeling and simulation, advanced computational methods, and machine learning algorithms to drive advanced reactor research and improve the sustainability of the current reactor fleet. AIMS extensively employs data science and machine learning ...

Yixin Wang

Yixin Wang works in the fields of Bayesian statistics, machine learning, and causal inference, with applications to recommender systems, text data, and genetics. She also works on algorithmic fairness and reinforcement learning, often via connections to causality. Her research centers around developing practical and trustworthy machine learning algorithms for large datasets that can enhance scientific ...

Camille Avestruz

Prof. Avestruz is a computational cosmologist leading the ALCCA (Avestruz Lab for Computational Cosmology and Astrophysics) research group. Her research group uses simulations to model, predict, and interpret observed large-scale cosmic structures. Her primary focus is to understand the evolution of galaxy clusters. These are the most massive gravitationally collapsed structures in our universe, comprised ...

David Fouhey

David works on computer vision and machine learning with the end goal of developing autonomous systems that can learn to build representations of the underlying state and dynamics of the world through observation (and potentially interaction). Towards this end, he is particularly interested in understanding physical and functional properties from images. His research interest in ...

Xun Huan

Prof. Huan’s research broadly revolves around uncertainty quantification, data-driven modeling, and numerical optimization. He focuses on methods to bridge together models and data: e.g., optimal experimental design, Bayesian statistical inference, uncertainty propagation in high-dimensional settings, and algorithms that are robust to model misspecification. He seeks to develop efficient numerical methods that integrate computationally-intensive models with ...

Yang Chen

Yang Chen received her Ph.D. (2017) in Statistics from Harvard University and then joined the University of Michigan as an Assistant Professor of Statistics and Research Assistant Professor at the Michigan Institute of Data Science (MIDAS). She received her B.A. in Mathematics and Applied Mathematics from the University of Science and Technology of China. Research ...

Bryan R. Goldsmith

Bryan R. Goldsmith, PhD, is Assistant Professor in the department of Chemical Engineering within the College of Engineering at the University of Michigan, Ann Arbor. Prof. Goldsmith’s research group utilizes first-principles modeling (e.g., density-functional theory and wave function based methods), molecular simulation, and data analytics tools (e.g., compressed sensing, kernel ridge regression, and subgroup discovery) to ...

Ambuj Tewari

My research group is engaged in fundamental research in the following areas: Statistical learning theory: We are developing theory and algorithms for predictions problems (e.g., learning to rank and multilabel learning) with complex label spaces and where the available human supervision is often weak. Sequential prediction in a game theoretic framework: We are trying to ...