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Alumni

Photo of James Boyko

James Boyko

I am a researcher with a strong interest in phylogenetic comparative methods, and I am always seeking ways to improve and advance these techniques. During my MSc studies at the University of Toronto and my PhD studies at the University of Arkansas, I focused on the long-term trends that shape the evolution and history of ...

Jennifer Li

Jennifer Li received her B.Sc. in Atmospheric Science from National Taiwan University and M.S. and Ph.D. degrees in Astronomy from the University of Illinois at Urbana-Champaign. After graduating, she moved to the University of Michigan to begin her postdoctoral research in 2021. Her research interests include active galactic nuclei (AGN), black hole and galaxy evolution, ...

Andreas Rauch

The development of the next generation of propulsion and power-generation devices requires enhanced understanding of the multiphysics processes governing them. I am focused on developing data-driven physics constrained models to provide high fidelity computational tools for flow prediction and analysis. Advances in both computational and experimental methods have generated significant high-resolution fluid dynamics data. However, ...

Anastasia Visheratina

Anastasia received her Ph.D. in Physics (2018) at ITMO University, St. Petersburg, Russia, focusing on developing chiral nanoparticles for biomedical applications. In 2019, she joined the Department of Chemical Engineering (laboratory of Professor Nicholas Kotov) at the University of Michigan as a Postdoctoral Research Fellow to deepen her expertise in nanoscale chirality. In 2022, Anastasia ...

Joseph (Yossi) Cohen

Yossi Cohen received his Bachelor’s, Master’s, and Ph.D. degrees in Mechanical Engineering from the University of Michigan. His thesis explored industrial artificial intelligence concepts for fault diagnosis, advancing prognostics and health management research for complex manufacturing systems. His research interests include human-centered augmented intelligence, responsible artificial intelligence in industry, and sustainable manufacturing. As a Schmidt ...

Yutong Wang

Yutong Wang’s primary research interest is in developing theory for modern machine learning methods with motivation for providing a rigorous foundation for its use in science and engineering. He is currently working on applying deep learning to solving inverse problems in reconstructive spectroscopy motivated by applications in wearable devices. Areas he has worked included over-parametrized ...