A General Framework for Analyzing Stochastic Dynamics in Learning Algorithms. - Chi-Ning Chou; Juspreet Singh Sandhu; Mien Brabeeba Wang; Tiancheng Yu (CoRR, 2020)

Limitations of Local Quantum Algorithms on Maximum Cuts of Sparse Hypergraphs and Beyond. - Chi-Ning Chou; Peter J. Love; Juspreet Singh Sandhu; Jonathan Shi (CoRR, 2021)

Efficient Quantum Voting with Information-Theoretic Security. - Emil T. Khabiboulline; Juspreet Singh Sandhu; Marco Ugo Gambetta; Mikhail D. Lukin; Johannes Borregaard (CoRR, 2021)

Limitations of Local Quantum Algorithms on Random MAX-k-XOR and Beyond. - Chi-Ning Chou; Peter J. Love; Juspreet Singh Sandhu; Jonathan Shi (ICALP, 2022)

Random Max-CSPs Inherit Algorithmic Hardness from Spin Glasses. - Chris Jones; Kunal Marwaha; Juspreet Singh Sandhu; Jonathan Shi (ITCS, 2023)

A Sum-of-Squares Hierarchy in the Absence of Pointwise Proofs I: Energy Certificates. - Juspreet Singh Sandhu; Jonathan Shi (CoRR, 2024)

Potential Hessian Ascent: The Sherrington-Kirkpatrick Model. - David Jekel; Juspreet Singh Sandhu; Jonathan Shi (SODA, 2025)

On expectations and variances in the hard-core model on bounded degree graphs. - Ewan S. D. Davies; Juspreet Singh Sandhu; Brian Tan (CoRR, 2025)