2024 Cohort

In data from telescopes, exoplanet signals are masked by complex instrumental and astrophysical noise sources of apriori unknown form. Conventional exoplanet-detection pipelines perform sequential data processing to mitigate noise, relying on incomplete signal models at each step, which can lower overall detection performance. My research analyzes the statistical properties of exoplanet data-processing architectures to design ...

Freshwater ecosystems are experiencing increasing water quality degradation globally due to climate, environmental factors, and human activities. My research focuses on developing cutting-edge, interdisciplinary tools to better understand, predict, and mitigate these impacts on freshwater quality and ecosystem health. By integrating diverse data sources and methods, including in situ measurements, satellite observations, process-based models, statistical ...

I pioneered multiple statistical methods and theory to study the effects of natural selection and other evolutionary processes on observed DNA sequences. Using geometric deep learning and/or other machine learning techniques, I now plan to improve genealogy and haplotype inference for low quality, low coverage, and/or small sample size data. I am also studying a ...

This project aims to advance the detection of lethal, non-trackable space debris (10 μm-10 mm) by analyzing non-thermal electromagnetic (NTEM) radiation emitted during hypervelocity collisions. We will leverage AI/ML models to automatically identify and classify these collision signatures using observational data from ground-based radio telescopes, thereby improving the efficiency of debris monitoring.

Madeline is a proud Pittsburgher and Torontonian with training in mathematical and computational biology, particularly the ecology and evolution of malaria. Madeline’s current research interests focus on developing our quantitative understanding of basic biological processes that underly within-host infection dynamics. She is working towards a new approach for developing compact, predictive models of within-host infection ...

Xin’s research interests include risk, reliability, and resilience analysis of natural hazards under climate change, leveraging interdisciplinary approaches such as remote sensing, geospatial modeling, data-driven and physics-based models. Before joining the University of Michigan, he earned his PhD in Civil Engineering from Shanghai Jiao Tong University under the supervision of Prof. Lulu Zhang. He was ...

Zheng is interested in applying AI-based program synthesis tools to automate the craft of engineering simulation programs and redesign data structures and algorithms to optimize performance. This will not solely significantly expedite the advancement of engineering simulation software but also redefine the approach of developers within these systems, fostering increased efficiency. The aspiration is for ...