2023 Cohort

Weichi earned her Ph.D. in Statistics from New York University, where her dissertation focused on adapting machine-learning models to address challenging problems in medical and physical sciences. As a Schmidt AI in Science Fellow, she is currently developing generative models for molecular design under data-scarce conditions to advance scientific discovery in chemistry and materials science. ...

ML with chemist-in-the-loop to find reaction conditions for plastic recycling. Plastic pollution is a huge environmental problem. Without viable, cost-effective, and environmentally friendly pathways for recycling and upcycling, there is little incentive to change the way we think about and handle plastic waste today. I have always been passionate about finding solutions to this problem. ...

I received my Ph.D. in Environmental Studies in 2023 from the University of California, Santa Cruz, with a Designated Emphasis in Statistics. My PhD dissertation focused on the interactions between climate change, plant phenology, and human society. Before that, I completed my undergraduate studies in Environmental Biology at the National University of Singapore. I am ...

Elena is a Postdoctoral Fellow with the Robotics Department at U-M since August 2022. Her research interest is in developing autonomous unmanned vehicles that are capable of intelligent planning and decision-making. As a Schmidt AI in Science Fellow, she will continue working on developing model-based reinforcement learning algorithms that enable autonomous agents to generalize and ...

Kevin is developing heliostack, a novel software-based approach for detecting solar system objects in astronomical images. The software will be able combine images taken on multiple days and from multiple telescopes, which will fundamentally change to how we search for and study solar system minor planet populations. This technique will allow scientists to study smaller ...

As a Schmidt AI in Science Fellow, Jake will investigate applications of AI and machine learning approaches to basic questions in ecology and evolutionary biology. Initially, Jake will study a neural network system trained to generate high-throughput measurements from museum specimens in order to understand the evolution of the avian skeleton. Longer term, Jake aspires ...

At the University of Michigan, I specialize in the intersection of astrophysical spectroscopy and machine learning. My current research focuses on analyzing spectroscopic data from the earliest galaxies in the universe, observed by the James Webb Space Telescope. I joined MIDAS to enhance my scientific software packages with machine learning techniques. These upgrades include the ...

Alyssa is interested in drinking water research at the intersection of science and policy, including equitable access to quality drinking water and science communication. While at MIDAS, she will use sensors and machine learning techniques to generate data-driven models designed to advance real-time decision-making in drinking water and further the protection of public health. In ...