Atal Narayan Sahu

Atal Narayan Sahu AI and Quantum Computing Engineer

About

I’m a graduate student in Quantum Science and Technology at UCLA. My current research interests lie in computational methods for scientific discovery. I aim to develop generative and physics-informed ML models and explore hybrid quantum-classical algorithms to tackle inverse design, property prediction, and synthesis planning in drug discovery, materials design, and quantum chemistry.

Previously, at Regology, I led the development of Reggi, an LLM-based legal assistant. Earlier, at KAUST, I researched communication-efficient methods for distributed deep learning.

If you’re interested in learning more about my professional background, feel free to check out my CV.

Research

Publications

1. REFL: Resource-Efficient Federated Learning
   A. M. Abdelmoniem, A. N. Sahu, M. Canini, S. Fahmy
   In ACM EuroSys’23
   [Link]

2. On the Convergence Analysis of Asynchronous SGD for Solving Consistent Linear Systems
   A. N. Sahu, A. Dutta, A. Tiwari, & P. Richtárik
   In Linear Algebra and Applications
   [Link]

3. Rethinking gradient sparsification as total error minimization
   A. N. Sahu, A. Dutta, A. M. Abdelmoniem, T. Banerjee, M. Canini, & P. Kalnis
   In NeuRIPS’21
   [Link]

4. Efficient Sparse Collective Communication and its application to accelerate Distributed Deep Learning
   J. Fei, Chen-Yu Ho, A. N. Sahu, M. Canini, & A. Sapio
   In ACM SIGCOMM 2021
   [Link]

5. On the Discrepancy between the Theoretical Analysis and Practical Implementations of Compressed Communication for Distributed Deep Learning
   A. Dutta, E. H. Bergou, A. M. Abdelmoniem, Chen-Yu Ho, A. N. Sahu, M. Canini, & P. Kalnis
   In Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI) 2020
   [arXiv]

6. Natural Compression for Distributed Deep Learning
   S. Horváth, Chen-Yu Ho, L. Horváth, A. N. Sahu, M. Canini, & P. Richtárik
   3rd Annual Conference on Mathematical and Scientific Machine Learning, PMLR
   [Link]

Contact