Subhro Das is a Research Staff Member in AI Algorithms at the MIT-IBM Watson AI Lab, IBM Research, Cambridge MA. He is a Research Affiliate at MIT, co-leading IBM’s engagement in the Bridge pillar of MIT Quest for Intelligence. He serve as the Co-Chair of the AI Learning Professional Interest Community (PIC) at IBM Research.

His research interests are in distributed learning over multi-agent networks, dynamical systems, multi-agent reinforcement learning, accelerated & adaptive optimization methods, and online learning in non-stationary environments – broadly in the areas of AI, machine learning, and statistical signal processing with applications in healthcare and social good. Before moving to Cambridge, he was a Research Scientist at IBM T.J. Watson Research Center, New York. Therein, he worked on developing signal processing and machine learning based predictive algorithms for a broad variety of biomedical and healthcare applications.

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 was an intern at Ulm University (Germany), Gwangju Institute of Science & Technology (South Korea), and, Bosch Research (Palo Alto, CA), respectively.

Top Work

Publications with the MIT-IBM Watson AI Lab

ConCerNet: A Contrastive Learning Based Framework for Automated Conservation Law Discovery and Trustworthy Dynamical System Prediction
ConCerNet: A Contrastive Learning Based Framework for Automated Conservation Law Discovery and Trustworthy Dynamical System Prediction
 
Label-free Concept Bottleneck Models
Label-free Concept Bottleneck Models
 
Who Should Predict? Exact Algorithms For Learning to Defer to Humans
Who Should Predict? Exact Algorithms For Learning to Defer to Humans
 
Post-hoc Uncertainty Learning using a Dirichlet Meta-Model
Post-hoc Uncertainty Learning using a Dirichlet Meta-Model
 
Beyond Worst-Case Analysis in Stochastic Approximation: Moment Estimation Improves Instance Complexity
Beyond Worst-Case Analysis in Stochastic Approximation: Moment Estimation Improves Instance Complexity
 
Selective Regression under Fairness Criteria
Selective Regression under Fairness Criteria
 
On Convergence of Gradient Descent Ascent: A Tight Local Analysis
On Convergence of Gradient Descent Ascent: A Tight Local Analysis
 
Fast Convergence for Unstable Reinforcement Learning Problems by Logarithmic Mapping
Fast Convergence for Unstable Reinforcement Learning Problems by Logarithmic Mapping
 
Online Optimal Control with Affine Constraints
Online Optimal Control with Affine Constraints
 
Fair Selective Classification Via Sufficiency
Fair Selective Classification Via Sufficiency
 
The Future of Work: How New Technologies Are Transforming Tasks
The Future of Work: How New Technologies Are Transforming Tasks