Artificial Intelligence at the College of Science

The College of Science is at the forefront of AI research, developing the tools and methods that are redefining what’s possible across every scientific discipline. From decoding the building blocks of disease to modeling the systems that shape our planet, our researchers are using AI to answer questions that were once beyond our reach. Just as importantly, we’re teaching our students not only how to use these technologies, but how to wield them responsibly and ethically.

Educating students for what’s possible

As AI redefines what’s possible, it doesn’t diminish what makes us human. It makes those qualities essential. Our students pair genuine AI fluency with critical thinking, ethical reasoning, and creative collaboration. From combined majors in undergraduate studies to graduate programs in fields like bioinformatics and nanomedicine, they learn to work with AI, not to be replaced by it. 

Undergraduate majors
ms programs
PHD programs

COS faculty at the leading edge

College of Science faculty are developing new machine learning methods and applying them in drug discovery, climate science, adaptive robotics, and personalized health. They translate that research into impact and into the classroom, pioneering experiential models that let students apply AI to real challenges while shaping the responsible standards institutions worldwide follow.

Faculty perspectives on AI

Highlighted Publication

Hidden patterns of urban mixing across five global cities

Drawing on large-scale travel surveys of over 200,000 residents across Boston, Chicago, Hong Kong, London, and São Paulo, this research published in Nature Cities reveals patterns in social mixing invisible in mobility data alone. It finds that individual mobility shapes who we encounter more than demographics, home environment, or transit access, yet activity spaces remain stratified by income, producing structurally different social mixing experiences across groups.

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Highlighted Publication

A real-time early warning system to anticipate respiratory disease outbreaks using transfer learning

Respiratory outbreaks drive over a million U.S. hospitalizations yearly, yet surveillance lags behind real-time conditions. This machine learning system, published in Nature Communications, pairs Google search trends with epidemiological data to predict outbreak timing across all 50 states. Deployed live in 2024–2025 alongside the CDC, it caught 98% of onsets and 97% of peaks with weeks of lead time, turning reactive responses into proactive preparedness.

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Highlighted Publication

Accurate prediction of tensorial spectra using equivariant graph neural network

Designing optoelectronic devices depends on modeling how materials interact with light, but the calculations are slow and demanding. Published in Nature Communications, this research introduces TSENN, a symmetry-aware neural network that predicts a crystal’s full optical response directly from its structure. Trained on 1,432 semiconductors, it turns a half-hour computation into about one second, accelerating the discovery of new materials.

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    Related Institutes and Centers

    • Network Science Institute
      The Network Science Institute works to discvoer and inspire fundamentally new ways to measure, model, predict, and visualize meaningful interactions and interconnectivity of social, physical, and technological systems.
    • Quantum Materials and Sensing Institute
      The Quantum Materials and Sensing Institute seeks to accelerate practical quantum materials and technologies from concept to commercialization.
    • Center for Innovation in Mathematics, Physics, and Computational Theory
      IMPACT develops novel analytical frameworks and leverages modern computational methods, including artificial intelligence and quantum information, to address fundamental questions in mathematics and physics.
    • Center for Marine and Ecological Genomics
      By developing an open science ecosystem for marine genomics research and leveraging advancements in AI, the Center for Marine and Ecological Genomics will accelerate genomic discoveries from non-model marine organisms.

    Select Faculty in AI and Machine Learning

    • Bara 800

      Albert-László Barabási

      Robert Gray Dodge Professor of Network Science
      University Distinguished Professor

      Professor Barabási’s research focuses on biological networks, science of success, applications of control theory to networks, and the development of network models of resiliency in systems.

    • Arun Bansil

      Arun Bansil

      University Distinguished Professor, Physics

      Professor Bansil’s research focuses on first principles studies, oftopological materials, and optical spectroscopy.

    • Ning Bao

      Assistant Professor, Mathematics and Physics

      Professor Bao’s expertise includes holography and quantum information theory.

    • James Halverson

      Associate Professor, Physics

      Professor Halverson’s expertise lies in AI, string theory, mathematics, and string phenomenology.

    • paulhand.jpg

      Paul Hand

      Assistant Professor, Mathematics (jointly appointed with Khoury College of Computer Sciences)

      Professor Hand researches theory and algorithms for AI and machine learning in the context of vision and imaging.

    • Sarah Harrison

      Sarah Harrison

      Assistant Professor, Physics and Mathematics

      Professor Harrison’s research is focused on quantum field theory, string theory, and mathematics.

    • Katie Lotterhos standing outdoors in front of a rocky cliff

      Katie Lotterhos

      Associate Professor, Marine and Ecological Genomics

      Professor Lotterhos conducts cross-disciplinary research in ecological and evolutionary genomics, marine biology, and biostatistics.

    • Stacy Marsella

      Professor, Psychology (jointly appointed with Khoury College of Computer Sciences)

      Dr. Marsella’s work, which is grounded in the computational model of human cognition, emotion, and social behavior, has applications for human behavior, health interventions, social skills training, and planning operations.

    • Pran Nath

      Matthews Distinguished University Professor, Physics

      Professor Nath’s expertise includes supergravity, particle physics, and cosmology.

    • Brent Nelson headshot

      Brent Nelson

      Professor, Physics

      Professor Nelson’s expertise includes AI, cosmology, and string phenomenology.

    • Mary Jo Ondrechen

      Professor, Chemistry and Chemical Biology

      The Ondrechen Research Group develops methods for genomics, studies how enzymes work, and collaborates in drug discovery using computation.

    • Lace Padilla

      Assistant Professor, Psychology (jointly appointed with Khoury College of Computer Sciences)

      Professor Padilla combines cognitive, neural, and computer sciences to study how people process data visualizations and how those visualizations can communicate more accurately and effectively.

    • jose perea

      Jose Perea

      Associate Professor, Mathematics (jointly appointed with Khoury College of Computer Sciences)

      Professor Perea is passionate about using nonstandard mathematical ideas to solve problems in data science and machine learning.

    • Fabian

      Fabian Ruehle

      Assistant Professor, Physics and Mathematics

      Professor Ruehle’s research incorporates AI, string theory, mathematics, and string phenomenology.

    • Matthias Schlichting

      Associate Teaching Professor, Biology

      Dr. Schlichting’s teaching spans Biology Project Lab, Genetics and Molecular Biology, and the innovative Biological Clocks course he developed to engage students in circadian rhythms through active learning.

    • Marcus Sherman

      Assistant Teaching Professor, Biology

      Dr. Sherman’s is a multidisciplinary educator in the biological and computational sciences, who advances bioinformatics education by developing new pedagogical methods across programs.

    • Srivinas Sridhar

      University Distinguished Professor of Physics

      Professor Sridhar’s research is focused in quantitative MRI, drug discovery, and neurovisual science.

    • Tomasz Taylor

      Professor, Physics

      Professor Taylor’s expertise lies in holography and scattering amplitudes.

    • Anne van de Ven

      Teaching Professor, Physics
      Program Director, MS Nanomedicine

      Dr. van de Ven’s interdisciplinary work in nanomedicine, AI, and graduate education prepares scientists to evaluate emerging technologies, lead innovation, and translate research into real-world patient impact.

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