Subhro Das

Subhro Das is a Principal Applied Scientist in the Applied Science team at Microsoft. His research interests are broadly in the areas of Representation Learning, Generative AI, Foundation Models, Trustworthy Machine Learning, Large Language Models, Reinforcement Learning and ML Optimization methods. His work focuses on long-context understanding, uncertainty quantification and human-centric system design with large language and multimodal models.

Previously, he was a Staff Research Scientist / Research Manager at the MIT-IBM AI Lab in IBM Research and a Research Affiliate at MIT. His research papers are published in top machine learning and signal processing venues, including ICML, NeurIPS, ICLR, AAAI, AISTATS, EACL, TMLR, IEEE TSP and ICASSP. He was an IBM Master Inventor and have filed 25+ patents in machine learning.

He has deep expertise on post-training techniques and alignment methods for LLMs, especially in fine-tuning, instruction tuning, reinforcement learning and synthetic data generation. He developed a pipeline for large-scale long-context instruction following data generation, with variety of reasoning tasks, for training Granite (IBM’s LLM) models. Contributed to the post-training of Granite 3.1, and multimodal model Granite Vision.

Education

PhD, Electrical & Computer Engineering, Carnegie Mellon University, 2016. Advised by: Prof. José Moura.

MS, Electrical & Computer Engineering, Carnegie Mellon University, 2013.

B.Tech., Electronics & Communication Engineering, Indian Institute of Technology Kharagpur, 2011.

Appointments

Microsoft
Jun 2025 - present: Principal Applied Scientist, ODSP Applied Science, Cambridge, MA.

IBM Research
Nov 2018 - Jun 2025: Staff Research Scientist / Research Manager, AI Models. MIT-IBM AI Lab, Cambridge, MA.
Jul 2016 - Nov 2018: Research Scientist, ML for Health. IBM T.J. Watson Research Center, Yorktown Heights, NY.

Massachusetts Institute of Technology
May 2019 - Jun 2025: Research Affiliate, Electrical Engineering & Computer Science. Cambridge, MA.

Past MIT-IBM Research Grants

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

Students

Mentored/supervised some outstanding graduate students during their internship and scholar programs at IBM Research: Abinitha Gourabathina (PhD, MIT), Quang Nguyen (PhD, MIT), Tuomas Oikarinen (PhD, UC San Diego), Maohao Shen (PhD, MIT), Wang Zhang (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.