Research

I am interested in the area of representation learning. More recently, I have developed an interest in diffusion models (subjects include alignment, test-time scaling, controlled generation).

Please refer to my Google Scholar profile for details on the publications I have been a part of.

Conference tutorials

• Practical Adversarial Robustness in Deep Learning: Problems and Solutions (CVPR’21)
• Foundational Robustness of Foundation Models (NeurIPS’22)
• All Things ViTs: Understanding and Interpreting Attention in Vision (CVPR’23)

Invited talks, demos, etc.

• SoTA Diffusion Models with 🧨 diffusers (slides and recording)
  • IBM Research (October 17, 2023)
  • The Dyson Robotics Lab, Imperial College London
  • Department of Statistics, University of Oxford
• 🧨 diffusers for research at VAL, Indian Institute of Science (IISc), June 12, 2023. Slides are here.
• Controlling Text-to-Image Diffusion Models: Assorted Approaches
  • BigMAC ICCV Workshop in 2023 (slides, recording)
  • UCL in June 2024
  • Texas A&M University in September 2024 (slides)
  • UC Berkeley in October 2024 (slides, recording)
  • ICDMAI’25 (Slides)
• Demo of 🧨 diffusers at ICCV 2023 (Tweet).
• A talk on diffusion models, ETH Zurich (May 06, 2024). Slides are here.
• Transformers in Diffusion Models for Image Generation and Beyond, CS25 v5, Stanford (May 27, 2025) (slides and recording) (a little shoutout from Sander Dieleman).

For regular talks, refer here.

Teaching assistance

Served as a TA for Full Stack Deep Learning’s 2022 cohort.

Reviewing

• Conferences: NeurIPS’25, ICCV’25, ICML’25, CVPR’25, ICLR’25, NeurIPS’24, AAAI’23, ICASSP’21 (sub-reviewer).
• Workshops: UDL workshop (ICML’21).
• Journals: TMLR, Artificial Intelligence (Elsevier), IEEE Access.

Misc

• Released a dataset for large-scale multi-label text classification (joint work with Soumik Rakshit).
• Exploration on instruction-tuning Stable Diffusion.