Scholar
Raghav Singhal
Google Scholar ID: Nc4_zNIAAAAJ
PhD Researcher, EPFL
Citations & Impact
Citations
201
H-index
4
i10-index
2
Publications
13
Co-authors
4
list available
Publications
Browse publications on Google Scholar (top-right) ↗
Resume (English only)
Academic Achievements
- FedEx-LoRA: Exact Aggregation for Federated and Efficient Fine-Tuning of Foundation Models, ACL 2025 - Oral (Top 2.2% of submitted papers)
- ABBA: Highly Expressive Hadamard Product Adaptation for Large Language Models, arXiv; ES-FOMO @ ICML 2025 - Spotlight (Top 9.5% of accepted papers)
- Safety Subspaces are Not Distinct: A Fine-Tuning Case Study, arXiv
- Initialization using Update Approximation is a Silver Bullet for Extremely Efficient Low-Rank Fine-Tuning, arXiv; SCOPE @ ICLR 2025
- Fed-SB: A Silver Bullet for Extreme Communication Efficiency and Performance in (Private) Federated LoRA Fine-Tuning, arXiv; ES-FOMO @ ICML 2025
- M3CoL: Harnessing Shared Relations via Multimodal Mixup Contrastive Learning for Multimodal Classification, TMLR
- Regularization-based Framework for Quantization-, Fault- and Variability-Aware Training, arXiv; MLNCP @ NeuRIPS 2024; Under Review at TMLR
- Translation and Scale Invariance for Event-Based Object Tracking, NICE 2023
Research Experience
- Worked as a researcher at Massachusetts Institute of Technology and Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) with Prof. Praneeth Vepakomma.
Education
- Graduated from IIT Bombay with a Bachelor's in EE and a Master's in AI/ML. Worked as a researcher at Massachusetts Institute of Technology and Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) under Prof. Praneeth Vepakomma.
Background
- Currently an AI PhD student at EPFL. Research interests include improving the usability of AI systems, efficient AI, enhancing the safety and reliability of AI systems, improving reasoning capabilities, and, more recently, developing useful agentic use-cases and evals.
