AI and Machine Learning

Chengzhong Ye is a BCBI Postdoctoral Fellow. His research focuses on developing AI/ML and statistical methods for analyzing large-scale genomic data. He is particularly interested in building deep learning models of genomic sequences that can interpret the functional and phenotypic consequences of genetic variation at scale, with the broader goal of advancing our understanding of human disease and complex traits. His recent work has centered on genomic language models that learn patterns of functional constraint and representations of genomic features through self-supervised learning from...

Aditi Krishnapriyan's research interests include machine learning methods for the natural sciences, generative modeling, understanding what ML models learn from scientific data, molecular and materials modeling, multi-scale dynamics, statistical mechanics, numerical methods, and optimization.

Her research group develops machine learning methods motivated by the distinct challenges and opportunities of the natural sciences. A central theme is understanding what machine learning models learn from scientific data, rather than assuming physical structure must be hand-engineered into them, and...

Sandrine Dudoit is Professor in the Department of Statistics and the Division of Biostatistics in the School of Public Health, as well as core faculty member of the Center for Computational Biology. She is also serving as Executive Associate Dean for the College of Computing, Data Science, and Society.

Dudoit's research and teaching activities broadly concern the development and application of statistical learning methods and software for the analysis of high-throughput -omic data in both basic biology and precision health and medicine.

Statistical methodology and theory. Her methodological...

Bin Yu is Chancellor’s Distinguished Professor in the Departments of Statistics and of Electrical Engineering & Computer Sciences, and Center for Computational Biology, and Senior Advisor at The Simons Institute for the Theory of Computing, at the University of California at Berkeley. Her current research interests focus on AI risk and evaluation, veridical data science, explainable and trustworthy AI, statistical machine learning algorithms and theory (e.g. deep learning and decision trees),

interdisciplinary data problems from precision medicine, genomics, and neuroscience. She pioneered the...

Sergey Levine received a BS and MS in Computer Science from Stanford University in 2009, and a Ph.D. in Computer Science from Stanford University in 2014. He joined the faculty of the Department of Electrical Engineering and Computer Sciences at UC Berkeley in fall 2016. His work focuses on machine learning for decision making and control, with an emphasis on deep learning and reinforcement learning algorithms. Applications of his work include autonomous robots and vehicles, as well as computer vision and graphics. His research includes developing algorithms for end-to-end training of deep...

Liberty Hamilton is an Assistant Professor in UC Berkeley’s Department of Neuroscience and the Department of Statistics, with an affiliation at UCSF Department of Neurosurgery. She is known for research on speech representations in auditory cortex and using natural stimuli to understand real-world perception and behavior. Her expertise is auditory neuroscience and computational models of speech perception, integrating human neurophysiology with data-driven approaches.

She investigates how the human brain represents speech and other natural sounds across development and in clinical contexts...

Gopala Anumanchipalli received a B.Tech and MS in Computer Science from IIIT Hyderabad in 2008, and a Ph.D in Language and Information Technologies from Carnegie Mellon University, and a Ph.D in Electrical and Computer Engineering from IST, Lisbon. After Postdoctoral training and being a Full Researcher at Dept. of Neurosurgery at UCSF, he joined the faculty of the Department of Electrical Engineering and Computer Sciences at UC Berkeley in Spring 2021 and continues to hold an adjunct position at Dept. of Neurosurgery at UC San Francisco. He works at the intersection of Speech Processing...

Pieter Abbeel is Director of the Berkeley Robot Learning Lab and Co-Director of the Berkeley Artificial Intelligence (BAIR) Lab. Abbeel’s research strives to build ever more intelligent systems, which has his lab push the frontiers of deep reinforcement learning, deep imitation learning, deep unsupervised learning, transfer learning, meta-learning, and learning to learn, as well as study the influence of AI on society. His lab also investigates how AI could advance other science and engineering disciplines. Abbeel's Intro to AI class has been taken by over 100K students through edX, and his...

Yun Song is a professor of Computer Science and Statistics. He received BS degrees in mathematics and physics from MIT, and a PhD in physics from Stanford University. His current research lies at the intersection of AI/ML, statistics, and biology. He is generally interested in developing robust and efficient computational tools and statistical methods to facilitate the research of the broad biomedical community, while also getting deeply involved in data analysis and interpretation.

He is currently serving as the Faculty Director of the Center for Computational Biology, and a co-Director of...

Ryan Tibshirani is CDSS Chancellor's Professor and Chair of the Department of Statistics at UC Berkeley. From 2011-2022, he was a faculty member in Statistics and Machine Learning at Carnegie Mellon University. He did his Ph.D. in Statistics at Stanford University (2011), with Jonathan Taylor as his thesis advisor.

His research interests lie broadly in statistics, machine learning, and optimization; and he likes to think about problems from different angles: applied, computational, theoretical. More specifically, his interests include high-dimensional statistics, nonparametric estimation...