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2023 Cohort

Elena Shrestha

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 ...

Matthew Andres Moreno

Matthew works on “digital evolution. He will use ML to create low-dimensional representations of viable “digital organisms” and study how evolution happens in this reduced space. His research will shed light on the evolution of evolvability – when populations evolve to improve their ability to generate further adaptive variation. AI Mentor: Kevin Wood, Biophysics Science ...

Kevin Napier

Kevin will develop a groundbreaking ML-based astronomical image stacker to combine images taken on multiple days and from multiple telescopes. This will bring fundamental changes to how we search for and study solar system minor planet populations, with the immediate application of discovering some of the faintest objects in the solar system – objects in ...

Jacob Berv

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 ...

Vital Fernández

My research involves the analysis of emission line spectra. The captivating and colourful images obtained from the cosmos are primarily composed of photons emitted by particles heated by stars. A spectrum is a plot that depicts the number of photons observed as a function of their wavelength/colour. This tool provides a reliable fingerprint to quantify ...

Christin Salley

Dr. Christin Salley’s dissertation revolved around mitigating natural hazards through crisis detection and communications, enhancing accessibility to emergency management response systems. Her research aimed to ultimately assess social systems related to disasters and develop proactive measures to enhance community resilience. She earned her PhD in Civil Engineering (with a concentration in Construction and Infrastructure Systems) ...

Alyssa Schubert

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 ...

Weichi Yao

Recent years have seen significant advancements in artificial intelligence (AI) and machine learning (ML), evidenced by their empirical success. However, many scientific applications still rely on traditional statistical methods for several reasons. One issue is the data inefficiency of ML models, which is a universal challenge across scientific domains due to the high costs to ...

Nanta Sophonrat

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. ...