Biosketch

Ragesh Jaiswal is an Associate Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Delhi. He received his Ph.D. in Computer Science from the University of California, San Diego, under the supervision of Russell Impagliazzo. His research lies in theoretical computer science, with a focus on algorithms, complexity theory, clustering, and cryptography.

Jaiswal has made fundamental contributions to the theory of clustering, including efficient approximation algorithms for constrained and socially fair clustering, as well as sampling-based techniques for large-scale data analysis. His earlier work on direct product theorems and hardness amplification, carried out with Impagliazzo and collaborators, has had a lasting influence on computational complexity and cryptography. His research has appeared in leading journals such as SIAM Journal on Computing, Algorithmica, Theoretical Computer Science, Journal of Cryptology, and at premier conferences including FOCS, STOC, ICALP, ICLR, and NeurIPS.

He has been the recipient of multiple research grants from national and international agencies, including Google, Microsoft Research, and SERB. He received the best paper award at ISAAC'23. He also received IIT Delhi’s Teaching Excellence Award and the Outstanding Young Faculty Fellowship. Jaiswal regularly serves on reviewing committees of top conferences such as ICML, NeurIPS, ICLR, and AAAI, and has delivered invited talks at international workshops and schools.

Recent Publications

• Ragesh Jaiswal and Amit Kumar.: Clustering What Matters in Constrained Settings. Algorithmica, Volume 87, pages 1178–1198, 2025.
• Ragesh Jaiswal, Amit Kumar, and Jatin Yadav.: Robust-Sorting and Applications to Ulam-Median. In the 52nd International Colloquium on Automata, Languages, and Programming (ICALP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 334, pp. 100:1-100:19, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2025).
• Poojan Chetan Shah and Ragesh Jaiswal.: Quantum (Inspired) D^2-sampling with Applications. The 13th International Conference on Learning Representations (ICLR’25), 2025.
• Ragesh Jaiswal and Amit Kumar. Universal weak coreset. In Proceedings of the 38th AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence (AAAI’24/IAAI’24/EAAI’24), Vol. 38. AAAI Press, Article 1426, 12782–12789, 2024.
• Ragesh Jaiswal, Amit Kumar, and Jatin Yadav.: FPT Approximation for Capacitated Sum of Radii. In the 15th Innovations in Theoretical Computer Science Conference (ITCS 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 287, pp. 65:1-65:21, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2024)