- Generalizing Fair Clustering to Multiple Groups: Algorithms and Applications (AAAI 2026)
- Towards Fair Representation: Clustering and Consensus (COLT 2025)
- Improved Rank Aggregation under Fairness Constraint (IJCAI 2025)
- Equivalence Testing: The Power of Bounded Adaptivity (AISTATS 2024)
- Tight Lower Bound on Equivalence Testing in Conditional Sampling Model (SODA 2024)
- Approximate Maximum Rank Aggregation: Beyond the Worst-Case (FSTTCS 2023)
- Matrix Completion: Approximating the Minimum Diameter (ISAAC 2023)
- Support Size Estimation: The Power of Conditioning (MFCS 2023)
- Approximate Model Counting: Is SAT Oracle More Powerful than NP Oracle? (ICALP 2023, Track B)
- Clustering Permutations: New Techniques with Streaming Applications (ITCS 2023)
- Fair Rank Aggregation (NeurIPS 2022)
- Pairwise Reachability Oracles and Preservers under Failures (ICALP 2022, Track A)
- Approximating the Center Ranking under Ulam (FSTTCS 2021)
- Approximate Trace Reconstruction via Median String (in Average-Case) (FSTTCS 2021)
- Approximating the Median under the Ulam Metric (SODA 2021)
- Hardness of Approximation of (Multi-)LCS over Small Alphabet (APPROX 2020)
- New Extremal bounds for Reachability and Strong-Connectivity Preservers under failures (ICALP 2020, Track A, to appear in ACM Transactions on Algorithms (TALG) 2025)
- Approximate Online Pattern Matching in Sub-linear Time (FSTTCS 2019)
- Approximating Edit Distance Within Constant Factor in Truly Sub-Quadratic Time (FOCS 2018, best paper award, Journal of the ACM (JACM) 2020)
- Space-Optimal Quasi-Gray Codes with Logarithmic Read Complexity (ESA 2018, Track A)
- Sparse Weight Tolerant Subgraph for Single Source Shortest Path (SWAT 2018)
- On Resource-bounded versions of the van Lambalgen theorem (TAMC)
Research Experience
Post-doctoral fellow at Charles University, Prague, Czech Republic, hosted by Prof. Michal Koucky; Post-doctoral fellow at Weizmann Institute of Science, Israel, hosted by Prof. Robert Krauthgamer
Education
PhD from Indian Institute of Technology, Kanpur, supervised by Prof. Manindra Agrawal and Prof. Satyadev Nandakumar
Background
Research Interests: Theoretical computer science; more specifically, algorithms on large data sets, sublinear algorithms, approximation algorithms, graph algorithms, and data structures.