Subhro Das

Subhro Das is a Staff Research Scientist and Research Manager at the MIT-IBM AI Lab, IBM Research, Cambridge MA. As a Research Affiliate at MIT and Principal Investigator (PI), he works on developing novel deep learning algorithms in collaboration with MIT researchers. He is an IBM Master Inventor and has filed 25+ patents in priority areas of machine learning and dynamical systems. His research papers are published in top top AI/ML and signal processing venues, including ICML, NeurIPS, ICLR, AAAI, AISTATS, EACL, IEEE Transactions on Signal Processing and ICASSP.

His research interests are broadly in the areas of Representation Learning, Generative AI, Foundation Models, Trustworthy Machine Learning, Large Language Models, Reinforcement Learning, Dynamical Systems and ML Optimization methods. At the MIT-IBM AI Lab, his research work focuses on uncertainty quantification and human-centric system design for Large Language Models; deep learning for time-series; and, robust, accelerated & distributed optimization methods. 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 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.

For updated information, please see his personal webpage: subhrodas.github.io.

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), Subhro Das (IBM), Lam Nguyen (IBM)
• Principles and Methods for Exploiting Unlabeled Data in Supervised Learning
  PI: Greg Wornell (MIT), Subhro Das (IBM), Prasanna Sattigeri (IBM)
• Coarse Graining Using Machine Learning
  PI: Tommi Jaakkola (MIT), Nima Dehmamy (IBM), Subhro Das (IBM)

Selected Publications

• One step closer to unbiased aleatoric uncertainty estimation
  Wang Zhang, Martin Ma, Subhro Das, Lily Weng, Alexandre Megretski, Luca Daniel, and Lam Nguyen
  AAAI Conference on Artificial Intelligence (AAAI), 2024.
• Effective Human-AI Teams via Learned Natural Language Rules and Onboarding
  Hussein Mozannar, Jimin J Lee, Dennis Wei, Prasanna Sattigeri, Subhro Das, David Sontag
  Neural Information Processing Systems (NeurIPS), 2023.
• ConCerNet: A contrastive learning based framework for automated conservation law discovery
  Wang Zhang, Tsui-Wei Weng, Subhro Das, Alexandre Megretski, Luca Daniel, and Lam Nguyen
  International Conference on Machine Learning (ICML), 2023.
• Label-free Concept Bottleneck Models
  Tuomas Oikarinen, Subhro Das, Lam M. Nguyen, and Tsui-Wei Weng
  International Conference on Learning Representations (ICLR), 2023.
• Post-hoc uncertainty learning using a dirichlet meta-model
  Maohao Shen, Yuheng Bu, Prasanna Sattigeri, Soumya Ghosh, Subhro Das, and Gregory Wornell
  AAAI Conference on Artificial Intelligence (AAAI), 2023.
• Exact algorithms for learning to defer with halfspaces
  Hussein Mozannar, Hunter Lang, Dennis Wei, Prasanna Sattigeri, Subhro Das, and David Sontag
  International Conference on Artificial Intelligence and Statistics (AISTATS), 2023.
• Reliable gradient-free and likelihood-free prompt tuning
  Maohao Shen, Soumya Ghosh, Prasanna Sattigeri, Subhro Das, Yuheng Bu, and Gregory Wornell
  European Chapter of the Association for Computational Linguistics Conference (EACL), 2023.
• Attacking c-MARL more effectively: A data driven approach
  Nhan Pham, Lam Nguyen, Jie Chen, Hoang Thanh Lam, Subhro Das, and Tsui-Wei Weng
  IEEE International Conference on Data Mining (ICDM), 2023.
• Variance reduction for faster decentralized general convex optimization
  Ran Xin, Subhro Das, Soummya Kar, and Usman Khan
  IEEE Conference on Decision and Control (CDC), 2023.
• GAT-GMM: Generative Adversarial Training for Gaussian Mixture Models
  Farzan Farnia, William Wang, Subhro Das, Ali Jadbabaie
  SIAM Journal on Mathematics of Data Science (SIMODS), 2022.
• On Convergence of Gradient Descent Ascent: A Tight Local Analysis
  Haochuan Li, Farzan Farnia, Subhro Das, Ali Jadbabaie
  International Conference on Machine Learning (ICML), 2022.
• Beyond Worst-Case Analysis in Stochastic Approximation: Moment Estimation Improves Instance Complexity
  Jingzhao Zhang, Hongzhou Lin, Subhro Das, Suvrit Sra, Ali Jadbabaie
  International Conference on Machine Learning (ICML), 2022.
• Selective Regression under Fairness Criteria
  Abhin Shah, Yuheng Bu, Joshua Lee, Subhro Das, Rameswar Panda, Prasanna Sattigeri, Gregory Wornell
  International Conference on Machine Learning (ICML), 2022.
• Online Optimal Control with Affine Constraints
  Yingying Li, Subhro Das, Na Li
  AAAI Conference on Artificial Intelligence (AAAI), 2021.
• Fair selective classification via sufficiency
  Joshua K Lee, Yuheng Bu, Deepta Rajan, Prasanna Sattigeri, Rameswar Panda, Subhro Das, Gregory W Wornell
  International Conference on Machine Learning (ICML), 2021.
• Verifiably Safe Exploration for End-to-End Reinforcement Learning
  Nathan Hunt, Nathan Fulton, Sara Magliacane, Nghia Hoang, Subhro Das, Armando Solar-Lezama
  ACM International Conference on Hybrid Systems: Computation and Control (HSCC), 2021.
• Learning Occupational Task-Shares Dynamics for the Future of Work
  Subhro Das, Sebastian Steffen, Wyatt Clarke, Prabhat Reddy, Erik Brynjolfsson, Martin Fleming
  AAAI/ACM Conference on AI, Ethics, and Society (AIES), 2020.
• Efficient Goal Attainment and Engagement in a Care Manager System Using Unstructured Notes
  Sara Rosenthal, Subhro Das, Pei-Yun Hsueh, Ken Barker, Ching-Hua Chen
  Journal of the American Medical Informatics Association (JAMIA) Open, 2020.
• An adaptive, data-driven personalized advisor for increasing physical activity
  Zhiguo Li, Subhro Das, James Codella, Tian Hao, Kun Lin, Chandramouli Maduri, and Ching-Hua Chen
  IEEE Journal of Biomedical and Health Informatics (JBHI), 2018.
• Consensus+ innovations distributed Kalman filter with optimized gains
  Subhro Das and José MF Moura
  IEEE Transactions on Signal Processing (TSP), 2017.
• Distributed Kalman filtering with dynamic observations consensus
  Subhro Das and José M. F. Moura
  IEEE Transactions on Signal Processing (TSP), 2015.
• Distributed state estimation in multi-agent networks
  Subhro Das and José M. F. Moura
  IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2013

Full list of papers: Google Scholar

Students

Mentored/supervised some outstanding graduate students during their internship and scholar programs at IBM Research: Quang Nguyen (PhD, MIT), Tuomas Oikarinen (PhD, UC San Diego), Maohao Shen (PhD, MIT), Eli Lucherini (PhD, Princeton), Kadeem Noray (PhD, Harvard), Ran Xin (PhD, CMU), Nicholas Borge (MS, MIT), Yingying Li (PhD, Harvard), Joshua Lee (PhD, MIT), Renzhe Yu (PhD, UC Irvine), Orlando Romero (PhD, RPI), Nathan Hunt (PhD, MIT). Aside from them, collaborated with several students and postdocs from multiple universities.

Awards

• Two Outstanding Technical Achievement Awards, IBM Research, 2023.
• IBM Invention Achievement Awards: 2017; 2019 (First Plateau); 2020 (Second Plateau); 2021 (Third Plateau); 2022 (Fourth Plateau); 2022 (three High Value Patent awards); 2023 (Fifth Plateau).
• Elevated to IBM Master Inventor, Class of 2022.
• IBM Outstanding Innovation Award, 2022.
• Two IBM Research A-Level accomplishment awards recognizing contributions to two high-impact projects, 2021.
• Best Paper Award, ACM International Conference on Hybrid Systems: Computation and Control (HSCC), 2021. HSCC Awards
• IBM Outstanding Technical Achievement Award, 2021.
• Special Division Accomplishment Award, for Research Contributions to IBM COVID-19 Tech Taskforce, 2020.
• Distinguished Paper Award, American Medical Informatics Association, 2018. AMIA Web
• Best PhD Forum Award 2017, given by IEEE Signal Processing Society during ICASSP 2017 for best doctoral dissertation in Acoustics, Speech & Signal Processing areas. CMU ECE News
• A.G. Jordan Award, Carnegie Mellon University 2017, for combining outstanding Ph.D. thesis work with exceptional service to the ECE and CMU communities. ECE Awards, Ceremony Video
• Aditya Birla Scholarship, 2007-11, for academic excellence and humane leadership values. The Times of India, ABGS Web

Professional Service

• IEEE Senior Member. IEEE Leadership roles:
  • Board of Governors, IEEE Signal Processing Society, 2023 - present.
  • IEEE Transactions on Signal Processing: Senior Area Editor (2023-present); Associate Editor (2019-2023).
  • Associate Editor, IEEE Signal Processing Magazine eNewsletter.
  • Elected Member, IEEE Machine Learning for Signal Processing (MLSP) Technical Committee.
  • Associate Member, IEEE Signal Processing Theory and Methods (SPTM) Technical Committee.
  • Committee Chair, IEEE Young Professionals in Signal Processing.
• Panelist, VAIBHAV Summit, Government of India, Oct 2020.
• Co-organized conference workshops:
  • 2023 AAAI Workshop on When Machine Learning meets Dynamical Systems: Theory and Applications.
  • 2020 IEEE ICC Workshop on Advances in Network Localization and Navigation.
  • 2017 MedInfo Workshop on Cognitive Data Analysis and Decision Support in Healthcare.
• Conference program committee and reviewer:
  • Industry Program Chair, IEEE MLSP 2023.
  • Program Committee member of several workshops at NeurIPS, ICML, IJCAI, KDD and MLSP conferences.
  • Moderator & Panelist, Young Professionals Panel Discussion: ICASSP 2021; MLSP 2021; ICIP 2020.

News/Media

• MIT News: Automated system teaches users when to collaborate with an AI assistant, 2023.
• MIT News: Efficient technique improves machine-learning models’ reliability, 2023.
• MIT News: A technique to improve both fairness and accuracy in artificial intelligence, 2022.
• IEEE Signal Processing Society Newsletter: Series to Highlight Young Professionals in Signal Processing, 2020.
• IBM Research Blog: Characterizing the Evolution of Tasks Within Occupations, 2020.
• Our research on the Future of Work (IBM Press Release) was covered by The Wall Street Journal, Bloomberg TV, Forbes-1, Forbes-2, Wired, VentureBeat, TechRepublic, Business Insider, EnterpriseAI, Medium Blog, and many others, 2019.
• Medium Blog, Fighting the Opioid Epidemic with Interpretable Causal Estimation of Individual Treatment Effect, 2018.
• Charles Cooper: I Imagine: How Technology Will Shape Scientific Research in the Next Century, New York Academy of Sciences Magazine, Fall 2017.
• Carnegie Mellon University ECE Department News: Alum receives best Ph.D. Forum Award, 2017.
• Times of India, Aditya Birla Scholars: Tomorrow's leaders today, 2007.