Siva Balakrishnan

Siva Balakrishnan is an Associate Professor at Carnegie Mellon University with a joint appointment in the Department of Statistics and Data Science and the Machine Learning Department. He holds an affiliation with the Dietrich College of Humanities and Social Sciences. Previously, he was a postdoctoral researcher at UC Berkeley's Department of Statistics, advised by Martin Wainwright and Bin Yu, and earned his Ph.D. in Computer Science from CMU's Language Technologies Institute under Jaime Carbonell. His research focuses on statistical machine learning, causal inference, and high-dimensional statistics, with notable contributions to domain adaptation, optimal transport, and robust statistics.

Education:

• Ph.D. in Computer Science, Carnegie Mellon University (Language Technologies Institute)
• Postdoctoral Researcher, University of California, Berkeley (Department of Statistics)

Research Interests:

His work bridges theoretical foundations and algorithmic development, emphasizing robust statistical methods and their applications in causal inference, public policy, and machine learning. Key areas include nonparametric methods, optimization, and high-dimensional data analysis. He has pioneered techniques in domain adaptation, such as the RLSbench framework for relaxed label shift scenarios.

Awards & Grants:

• Amazon Research Award (2021)
• Google Research Scholar Award (2021)
• NVIDIA Pioneer Award (2018)
• IMS Lawrence D. Brown Student Award (2020, 2022)
• National Science Foundation Grants (CCF-1763734, DMS-1713003, etc.)

Professional Activities:

He serves as an Associate Editor for JASA and on the editorial boards of Foundations and Trends in Statistics. His work has been featured in top venues like NeurIPS, ICML, and the Annals of Statistics. He currently holds a sabbatical at UC Berkeley's Department of Statistics (Spring 2025).

Labs & Collaborations:

He actively participates in the Statistics and Machine Learning Reading Group and the Causal Inference Working Group, fostering interdisciplinary research in CMU's vibrant academic community.