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Ansh Nagda
Ansh Nagda
anshnagda (at) berkeley (dot) edu
I am a PhD student in the Theoretical Computer Science group at UC Berkeley and a Research Scientist at Google DeepMind, where I am advised by Prasad Raghavendra. Before starting graduate school, I was an undergrad at the University of Washinton.
My primary research interests lie in theoretical computer science, with a broad focus on complexity theory, approximation algorithms, and quantum computing.
Papers
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Reinforced Generation of Combinatorial Structures: Applications to Complexity Theory
Ansh Nagda, Prabhakar Raghavan, Abhradeep Thakurta
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On optimal distinguishers for planted clique
Ansh Nagda, Prasad Raghavendra
FOCS 2025
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Improved approximation algorithms for the EPR Hamiltonian
Nathan Ju, Ansh Nagda
APPROX 2025
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On approximability of the Permanent of PSD matrices
Farzam Ebrahimnejad, Shayan Oveis Gharan, Ansh Nagda
STOC 2025
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Counting and Sampling Perfect Matchings in Regular Expanding Non-Bipartite Graphs
Farzam Ebrahimnejad, Shayan Oveis Gharan, Ansh Nagda
ITCS 2022
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Sublinear Time Hypergraph Sparsification via Cut and Edge Sampling Queries
Yu Chen, Sanjeev Khanna, Ansh Nagda
ICALP 2021
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Near-linear Size Hypergraph Cut Sparsifiers
Yu Chen, Sanjeev Khanna, Ansh Nagda
FOCS 2020