Faculty Members

  • Ganapathy Krishnamurthi
Prof. Ganapathy Krishnamurthi is faculty in the Department of Engineering Design and associate faculty at the Robert Bosch Center for Data Science and Artificial Intelligence at IIT Madras. He earned his PhD from Purdue University, and MSc in Physics from IIT Madras. He worked as a post-doctoral research fellow at Case Western Reserve University, USA and at Mayo Clinic, USA.

His research and work experience focuses on applying Machine Learning and Artificial Intelligence techniques to problems in medical image analysis, computer vision, interpretability/explainability of Deep Learning models across various applications  and using deep learning to solve inverse problems in medical imaging and computer vision. His current research involves developing deep learning solutions for time series data in business, engineering and imaging applications. He has published numerous research papers pertaining to Deep Learning and Machine Learning applied to many areas in science, engineering and technology.
  • Prof. Balaji Srinivasan
Prof. Balaji Srinivasan is faculty in the Department of Mechanical Engineering and an associate faculty at the Robert Bosch Center for Data Science and Artificial Intelligence at IIT Madras, pursuing research in the areas of fundamental Machine Learning and Deep Learning with focus on applications to science and engineering disciplines. He earned his B.Tech from IIT Madras, MS from Purdue University, and PhD from Stanford University, where he was the William K. Bowes, Stanford Graduate Fellow. Prior to his current role at IIT-Madras, he was a faculty member at IIT- Delhi and a post-doctoral fellow at University of Michigan, Ann Arbor.

His current research involves developing computational algorithms and models for a range of practical engineering problems that use a combination of probabilistic models, PDE based approaches as well as data-driven approaches. He has published research papers across multiple domains including Machine Learning, Partial Differential Equations, Computational algorithms, and High performance computing.

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