Soumi Tribedi

Soumi Tribedi

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I am a AI in Science fellow at Michigan Institute for Data Science (MIDAS) from Feb 2023. I am also a postdoctoral researcher at the Department of Chemistry. I earned my PhD in theoretical and computational chemistry from IIT Bombay, India. I am interested in employing AI methods to solve electronic structure problems in chemistry.

Yutong Wang

Yutong Wang

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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 learning, ensemble methods, quantized neural networks, kernel methods, and optimization. During his PhD, Yutong was a trainee in the Michigan Center for Single-Cell Genomic Data Analytics research team and was a co-first author on a publication for his contribution in the application of machine learning for genomic data analysis.

Yossi Cohen

Joseph (Yossi) Cohen

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Originally from Tel Aviv, Israel, 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 AI in Science Fellow, Yossi is currently investigating the intersection of explainable artificial intelligence with sustainability practices applied for semiconductor fabrication. He aims to construct responsible and human-centered methodologies for intelligent optimization of systems and operations to improve upon the overall environmental impact of modern manufacturing.

Anastasia Visheratina

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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 was awarded the Innovation Fellowship at Biointerfaces Institute (University of Michigan) to explore and work on research translation and commercialization.

During the Schmidt AI in Science Fellowship, Anastasia will work on the integration of state-of-the-art AI methods for computer vision to streamline the automated analysis of complex objects using electron microscopy. The main research question of the Fellowship will be whether it is possible to utilize these methods in a generalizable and scalable way to accelerate the advancement of Materials Science in different domains.

Andreas Rauch

Andreas Rauch

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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, the tools utilized for model development have not harnessed the potential of this data. Rather than using data to augment and drive model development it is often only used for a-posteriori validation. I am interested in assimilating high-resolution data into computational models to increase simulation predictive capabilities and reduce computational cost.

Xin Xie

Xin Xie

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Educational background: Institute of Physics, Chinese Academy of Sciences, Ph.D., 2022
Zhejiang University, B.S., 2015
Research interests: Topological photonics, Deep learning, quantum electrodynamics

My research focuses on the design of new types of states that emerge from topological optical structures using deep learning, and the light-matter interaction between topological structures and two-dimensional semiconductor materials.

Nathan Fox

Nathan Fox

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I am an Environmental Scientist, interested in how we can harness big data to innovate solving the pressing environmental challenges we face today. I obtained my PhD in Ocean and Earth Sciences form the University of Southampton, UK, with a thesis entitled ‘Social media, geodiversity and the provision of cultural ecosystem services’.

My research utilizes crowdsourced data such as social media websites and mobile phone applications to understand human-nature interactions. I am interested in how machine learning algorithms can be implemented to informing nature conservation efforts. I have been a strong advocate for the inclusion of geodiversity in conservation sciences.

One of my favourite projects that I worked on was facilitating a hackathon for tackling challenges that could couple socio-demographic and environmental data. I lead a group that produced an AI validated plant observations from social media: Flickr images from central London 2011-2019. Click here to view the he dataset created during the hackathon.

Jennifer Li

Jennifer Li

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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, and time-domain Astronomy. She uses multi-wavelength ground-based and space telescopes to study the properties of AGN, their host galaxies, and their environments. As an AI in Science fellow, she will study AGN variability and anomaly detection with the Vera Rubin Observatory’s Legacy Survey of Space and Time. 

James Boyko

James Boyko

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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 life on Earth. I am excited to continue this work and contribute to our understanding of these fascinating processes.