Jun-Ting (Tim) Hsieh
PhD student, Carnegie Mellon University
juntingh [at] cs [dot] cmu [dot] edu
Google scholar
PhD student, Carnegie Mellon University
juntingh [at] cs [dot] cmu [dot] edu
Google scholar
I am a PhD student in the Computer Science department at Carnegie Mellon University. I am fortunate to be advised by Pravesh Kothari. I am interested in theoretical computer science in general. Recently, I've been thinking about sum-of-squares certification and rounding algorithms for both worst-case and average-case problems, and also a bit of extremal graph theory. More broadly, I'm interested in the sum-of-squares hierarchy, hardness of approximation, and combinatorics.
In the past, I have worked on artificial intelligence and machine learning. I did my undergraduate and masters at Stanford University, where I worked with Fei-Fei Li and Stefano Ermon on machine learning projects. I'd also like to thank Li-Yang Tan at Stanford for sparking my interest in theoretical computer science.
Explicit two-sided unique-neighbor expanders
Jun-Ting Hsieh,
Theo McKenzie,
Sidhanth Mohanty,
Pedro Paredes
STOC, 2024
New SDP Roundings and Certifiable Approximation for Cubic Optimization
Jun-Ting Hsieh,
Pravesh K. Kothari,
Lucas Pesenti,
Luca Trevisan
SODA, 2024
Efficient Algorithms for Semirandom Planted CSPs at the Refutation Threshold
Venkatesan Guruswami,
Jun-Ting Hsieh,
Pravesh K. Kothari,
Peter Manohar
FOCS, 2023
Ellipsoid Fitting Up to a Constant
Jun-Ting Hsieh,
Pravesh K. Kothari,
Aaron Potechin,
Jeff Xu
ICALP, 2023
Approximating Max-Cut on Bounded Degree Graphs: Tighter Analysis of the FKL Algorithm
Jun-Ting Hsieh,
Pravesh K. Kothari
ICALP, 2023
A simple and sharper proof of the hypergraph Moore bound
Jun-Ting Hsieh,
Pravesh K. Kothari,
Sidhanth Mohanty
SODA, 2023
Polynomial-Time Power-Sum Decomposition of Polynomials
Mitali Bafna,
Jun-Ting Hsieh,
Pravesh K. Kothari,
Jeff Xu
FOCS, 2022
Certifying solution geometry in random CSPs: counts, clusters and balance
Jun-Ting Hsieh,
Sidhanth Mohanty,
Jeff Xu
CCC, 2022
Algorithmic Thresholds for Refuting Random Polynomial Systems
Jun-Ting Hsieh,
Pravesh K. Kothari
SODA, 2022
Learning Neural PDE Solvers with Convergence Guarantees
Jun-Ting Hsieh*,
Shengjia Zhao*,
Stephan Eismann,
Lucia Mirabella,
Stefano Ermon
ICLR, 2019
Learning to Decompose and Disentangle Representations for Video Prediction
Jun-Ting Hsieh,
Bingbin Liu,
De-An Huang,
Li Fei-Fei,
Juan Carlos Niebles
NeurIPS, 2018
Code
Graph Distillation for Action Detection with Privileged Modalities
Zelun Luo,
Jun-Ting Hsieh,
Lu Jiang,
Juan Carlos Niebles,
Li Fei-Fei
ECCV, 2018
Project
Code
Computer Vision-based Descriptive Analytics of Seniors' Daily Activities for Long-term Health Monitoring
Jun-Ting Hsieh*,
Zelun Luo*,
Niranjan Balachandar,
Serena Yeung,
Guido Pusiol,
Jay Luxenberg,
Grace Li,
Li-Jia Li,
N. Lance Downing,
Arnold Milstein,
Li Fei-Fei
Machine Learning for Healthcare (MLHC), 2018
Direct kinetic measurement of the reaction of the simplest Criegee intermediate with water vapor
Wen Chao,
Jun-Ting Hsieh,
Chun-Hung Chang,
Jim Jr-Min Lin
Science, 2015. Vol. 347, Issue 6223, pp. 751-754
Quantum Complexity Theory
TCS Toolkit Writing Project, 2020
Quantum Information Theory
Jun-Ting Hsieh,
Bingbin Liu
Course project, 2019