Postdoctoral Fellow

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 ...

Haotian received his D.Phil. from University of Oxford in 2022, and BSc from University of California, San Diego in 2019. With a background in chemistry, his research interest lies at the intersection of AI and Electrochemistry. Specifically, he pioneered Physics-Informed Neural Network (PINN) for electrochemistry simulation and has 16 first authored publication to date. At ...

Xinyu (Cindy) Liu holds a Ph.D. in Operations Research from Georgia Tech, with a doctoral minor in Transportation Systems and Engineering. Xinyu’s research interest is to develop stochastic optimization and models, data-driven methods, and computational tools to inform the design and operations of emerging mobility services, systems, infrastructures, and facilities. With the Schmidt AI in ...

Biocatalysis bears great potential to accelerate the synthesis of functional organic molecules by leveraging the ability of enzymes to catalyze reactions remarkably selectively. To facilitate its application, we aim to predict suitable enzymes for new substrates by viewing biocatalysis as a heterogeneous enzyme-substrate network. The models will be prospectively applied toward preparing pharmaceutically relevant molecules ...

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 ...