Vladimir Braverman

Vladimir Braverman is a Professor of Computer Science at Johns Hopkins University and an Adjunct Professor at Rice University. He holds secondary appointments in the Department of Applied Mathematics and Statistics at Johns Hopkins. His research focuses on sublinear algorithms, machine learning applications in healthcare and genomics, and data science. Braverman leads projects at the Johns Hopkins Mathematical Institute for Data Science (MINDS) and the Institute for Data Intensive Engineering and Science (IDIES). He has held visiting roles at Google Research and previously led a research group at HyperRoll (acquired by Oracle in 2009).

Education:

• PhD in Computer Science, University of California, Los Angeles (UCLA), 2011
• MSc in Computer Science, UCLA, 2009
• MSc in Computer Science, Ben-Gurion University of the Negev, Israel, 2004
• BSc in Computer Science (cum laude), Ben-Gurion University of the Negev, Israel, 1998

Research Interests: Braverman’s work bridges algorithm design and real-world applications. His lab develops efficient algorithms for large-scale data (e.g., sketches, coresets) with applications in radiomics, genomics, and systems. Notable areas include adversarial machine learning, continual learning for medical imaging, and federated learning systems.

Awards:

• NSF CAREER Award (2017)
• Best Paper Award, FAST 2019
• Google Faculty Award (2014)
• Cisco Faculty Award

Grants and Labs: Active grants include NSF and industry partnerships. Braverman collaborates across disciplines, including physics, oncology, and radiology, through MINDS and IDIES. His work emphasizes translational AI for healthcare and scalable systems.

Key Projects:

• Development of coreset-based algorithms for efficient LLM training
• Medical AI fairness and adversarial robustness in radiology
• Federated learning frameworks for cross-domain medical data