Subhro Das           

Subhro Das is a Staff Research Scientist and Research Manager at the MIT-IBM AI Lab, IBM Research, Cambridge MA. As a Principal Investigator (PI), he works on developing novel AI algorithms in collaboration with MIT. He is a Research Affiliate at MIT, co-leading IBM's engagement in the MIT Quest for Intelligence. His research interests are broadly in the areas of ML Optimization, Reinforcement Learning and Trustworthy ML. Currently, he works on developing novel AI algorithms for robust, accelerated, online & distributed optimization; safe, unstable & multi-agent reinforcement learning; uncertainty quantification and human-centric AI systems. He led the Future of Work initiative within IBM Research, studying the impact of AI on labor market and developing AI-driven recommendation frameworks for skills and talent management. Previously, at the IBM T.J. Watson Research Center in New York, he worked on developing signal processing and machine learning based predictive algorithms for a broad variety of biomedical and healthcare applications. He is an IBM Master Inventor and has filed 20+ patents in high priority areas of machine learning and dynamical systems.

He received MS and PhD degrees in Electrical and Computer Engineering from Carnegie Mellon University in 2014 and 2016, respectively. His dissertation research was in distributed filtering and prediction of time-varying random fields and he was advised by Prof. José M. F. Moura. He completed his Bachelors (B.Tech.) degree in Electronics & Electrical Communication Engineering from Indian Institute of Technology Kharagpur in 2011. During the summers of 2009, 2010 and 2015, he interned at Ulm University (Germany), Gwangju Institute of Science & Technology (South Korea), and, Bosch Research (Palo Alto, CA), respectively.

Current MIT-IBM Research Grants

• Human-Centric AI: Novel Algorithms for Shared Decision Making
  PI: David Sontag (MIT), Arvind Satyanarayan(MIT), Subhro Das (IBM), Dennis Wei (IBM), Prasanna Sattigeri (IBM)
• Adaptive, Robust, and Collaborative Optimization
  PI: Ali Jadbabaie (MIT), Asu Ozdaglar(MIT), Subhro Das (IBM), Nima Dehnamy (IBM), Songtao Lu (IBM)
• Safe Learning for Time Series Problems: Data, Structure and Optimization
  PI: Luca Daniel (MIT), Alexandre Mcgretski(MIT), Lam Nguyen(IBM), Subhro Das (IBM)
• Principles and Methods for Exploiting Unlabeled Data in Supervised Learning
  PI: Greg Wornell (MIT), Prasanna Sattigeri (IBM), Subhro Das (IBM)
• Coarse Graining Using Machine Learning
  PI: Tommi Jaakkola (MIT), Nima Dehmamy (IBM), Subhro Das (IBM)