🎉 Incoming Research Intern at Microsoft Research Cambridge, UK 🇬🇧.
Will be spending my summer 2025 working with the Azure AI Research Group at MSR!
About Me
I am a Ph.D. candidate at the Scalable Computing Systems Lab at EPFL, working under the supervision of Prof. Anne-Marie Kermarrec. My research focusses on designing efficient and privacy-preserving systems for Decentralized and Federated Learning, with particular emphasis on addressing the challenges of communication efficiency, data heterogeneity, and privacy in collaborative AI settings.
Presently, I am a visiting doctoral researcher at the Camera Culture Group of the MIT Media Lab, where I work under the supervision of Prof. Ramesh Raskar working on enabling collaborative AI in open decentralized networks.
Previously: I completed my Bachelor’s Degree in Computer Science and Engineering from the Indian Institute of Technology Mandi where I received the President of India Gold Medal. As part of my Bachelor Thesis, I worked with Dr. Manas Thakur in the Compilers and Programming Languages Group at IIT Mandi on Automating Loop Parallelization for TornadoVM.
Interests
- Distributed Systems
- Artificial Intelligence
- Machine Learning
- Decentralized Learning
- Privacy and Security
Education
Ph.D. in Computer Science
École Polytechnique Fédérale de Lausanne (EPFL)
Visiting Ph.D. in Computer Science
Massachusetts Institute of Technology (MIT)
B.Tech. in Computer Science and Engineering
Indian Institute of Technology (IIT) Mandi
Exchange Student in Computer Science
RWTH Aachen University
Updates
🎉 Our paper "Low-Cost Privacy-Aware Decentralized Learning" has been accepted for publication in the Proceedings on Privacy Enhancing Technologies 2025.
Accepted in Issue 2025.3. See you in Washington D.C.! 🇺🇸
🤖 We hosted the first MIT Decentralized AI Hackathon. 🤖
Collaboration 🤝🏽 vs. ⚔️ competition!
🎉 Our paper "Boosting Asynchronous Decentralized Learning with Model Fragmentation" has been accepted for publication in the ACM Web Conference 2025.
Accepted for Oral Presentation. See you in Sydney! 🇦🇺
🎉 Our paper "Fair Decentralized Learning" has been accepted for publication in the IEEE SaTML 2025.
Fairness through clustering!
✈️ Heading to Vancouver to attend NeurIPS 2024.
Along with Akash, I will be presenting our work “Revisiting Ensembling in One-Shot Federated Learning” at NeurIPS 2024. 🇨🇦
✈️ Starting my Research Visit at the MIT Media Lab.
Next 6 months will be in Cambridge, MA.
🎉 Our paper "Revisiting Ensembling in One-Shot Federated Learning" has been accepted for publication at NeurIPS 2024.
Accepted as a poster. See you in Vancouver! 🇨🇦
🎉 Our paper "Noiseless Privacy-Preserving Decentralized Learning" has been accepted for publication in the Proceedings on Privacy Enhancing Technologies 2025.
Accepted in Issue 2025.1. See you in Washington D.C.! 🇺🇸
🎉 I received the Doc.Mobility Grant 2024 at EPFL.
The grant will support my research visit to MIT this year.
Featured Publications
This is a selected list of my publications. For a complete list, please refer to .
(2025).
Noiseless Privacy-Preserving Decentralized Learning.
Proceedings on Privacy Enhancing Technologies (PoPETS) 2025.
(2025).
Boosting Asynchronous Decentralized Learning with Model Fragmentation.
Proceedings of the ACM Web Conference (WWW) 2025.
(2024).
Epidemic Learning: Boosting Decentralized Learning with Randomized Communication.
37th Annual Conference on Neural Information Processing Systems (NeurIPS).
(2023).
Get More for Less in Decentralized Learning Systems.
2023 IEEE 43rd International Conference on Distributed Computing Systems (ICDCS).
Recent Publications
(2025).
Low-Cost Privacy-Aware Decentralized Learning.
Proceedings on Privacy Enhancing Technologies (PoPETS) 2025.
(2025).
Fair Decentralized Learning.
2025 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML).
(2025).
Practical Federated Learning without a Server.
Proceedings of the 5th Workshop on Machine Learning and Systems (EuroMLSys).
(2024).
Revisiting Ensembling in One-Shot Federated Learning.
38th Annual Conference on Neural Information Processing Systems (NeurIPS).
(2024).
Energy-Aware Decentralized Learning with Intermittent Model Training.
2024 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW).
Invited Talks
Decentralized Systems for Machine Learning
Invited talk @ MIT Media Lab.
Decentralized Systems for Machine Learning
Invited talk @ McGill University.
Scalability of Prolog: Real-world logic applications
Lecture @ IIT Mandi.
Adversarial training for blind-spot removal
HiWi talk @ RWTH Aachen University.
Services
- 2025: PC@TMLR; PC@NeurIPS'25;
- 2024: PC@NeurIPS'24(Top Reviewer);
- Assistant Reviewer: OSDI'24; NETYS'24; SRDS'23;