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AIS Former Mentor

Derek Van Berkel

Dr. Van Berkel is an assistant professor at The University of Michigan, School for Environment and Sustainability. His research focuses on understanding land change at diverse scales; the physical and psychological benefit of exposure to natural environments; and how digital visualization of data can add new place-based knowledge in science and community decision-making. He has ...

Sabine Loos

My research focuses on natural hazards and disaster information, everything from understanding where disaster data comes from, how it’s used, and its implications to design improved disaster information systems that prioritize the human experience and lead to more effective and equitable outcomes. My lab takes a user-centered and data-driven approach. We aim to understand user ...

P.C. Ku

I work on photonic devices, mainly semiconductor based. Currently, my group are working on chip-scale spectrometers, quantum light sources, and tactile sensors. Accomplishments and Awards 2022 Propelling Original Data Science (PODS) Grant Award: Machine Learning Guided Co-design for Reconstructive Spectroscopy

Sabina Tomkins

My research utilizes computational social science and artificial intelligence to derive contextually informed algorithmic frameworks for understanding individuals and the social systems which influence their behavior, and for supporting positive behavior change within these systems. In particular, I analyze policy and behavior, and apply interactive behavior support and predictive algorithms, within the areas of higher ...

Sean Johnson

Sean Johnson is an observational astronomer and primarily studies galaxies, supermassive black holes, and the surrounding gas supplies that fuel their growth. By combining datasets from space-based and large ground-based telescopes, he studies the physical conditions of the gas supplies that enable galaxies to continue forming stars, and identifies the chemical signatures of heavy elements ...

Dan Rabosky

The Rabosky lab seeks to understand how and why life on Earth became so diverse. We focus primarily on large-scale patterns of species diversification (speciation and extinction) and on the tempo and mode of phenotypic evolution, to better understand what regulates the “amount” of biodiversity through Deep Time. To this end, we develop theoretical frameworks ...

Nicholas Kotov

Nicholas A. Kotov is Irving Langmuir Distinguished University Professor in Chemical Sciences at the University of Michigan. He is a pioneer of theoretical foundations and practical implementations of complex systems from ‘imperfect’ nanoparticles that offer a vast field for the application of data science and machine learning. Chiral nanostructures, biomimetic nanocomposites, and graph theoretical representations ...

Qing Qu

His research interest lies in the intersection of signal processing, data science, machine learning, and numerical optimization. He is particularly interested in computational methods for learning low-complexity models from high-dimensional data, leveraging tools from machine learning, numerical optimization, and high dimensional geometry, with applications in imaging sciences, scientific discovery, and healthcare. Recently, he is also ...

Lu Wang

Lu’s research is focused on natural language processing, computational social science, and machine learning. More specifically, Lu works on algorithms for text summarization, language generation, argument mining, information extraction, and discourse analysis, as well as novel applications that apply such techniques to understand media bias and polarization and other interdisciplinary subjects.