Builders & open source
Cho-Jui Hsieh
Associate Professor of Computer Science, University of California, Los Angeles, UCLA
- Associate Professor of Computer Science, University of California, Los Angeles1
Cho-Jui Hsieh is an associate professor of computer science at UCLA; Hsieh's homepage says Hsieh was previously an assistant professor of computer science and statistics at UC Davis for three years and lists a visiting scholar role at Google beginning in summer 2018. Hsieh develops algorithms and optimization techniques for large-scale machine learning, including work on model size, training and prediction speed, and robustness of deep learning models. Hsieh is a co-author of Large Batch Optimization for Deep Learning: Training BERT in 76 minutes, which introduces the LAMB optimizer, and of Symbolic Discovery of Optimization Algorithms, which describes the Lion optimizer found through program search. With Pin-Yu Chen, Hsieh co-authored the book Adversarial Robustness for Machine Learning.123
At a glance
- Current role
- Associate Professor of Computer Science
- Company
- UCLA
- Sector
- University
- Education
- UT Austin, UCLA
- Work listed
- 2
- Honors
- 9
- Sources cited
- 7
- Profile reviewed
Career
Hsieh completed a PhD in computer science at the University of Texas at Austin from 2010 to 2015, advised by Inderjit Dhillon, after a master's degree at National Taiwan University supervised by Chih-Jen Lin. Hsieh then served as an assistant professor in the Departments of Computer Science and Statistics at UC Davis from 2015 to 2018 before joining the UCLA computer science faculty in November 2018, where Hsieh is now an associate professor.41
Research
Hsieh's research focuses on algorithms and optimization for large-scale machine learning, including adversarial robustness, fast and scalable training, model compression, recommender systems and automated machine learning. Hsieh is a co-author of the LAMB large-batch optimizer paper (ICLR 2020) and of Cluster-GCN (KDD 2019), an algorithm for training deep and large graph convolutional networks.425
On robustness, Hsieh co-authored CROWN, a method for certifying the robustness of neural networks with general activation functions presented at NIPS 2018, and the book Adversarial Robustness for Machine Learning with Pin-Yu Chen. Hsieh has also presented a tutorial on neural network verification.61
Open-source work
Hsieh's software page lists packages Hsieh has worked on, among them LIBLINEAR for large-scale linear classification, QUIC for sparse inverse covariance estimation, the large-scale kernel SVM solver DCSVM, and LIBPMF for parallel matrix factorization.7
Work and contributions
Lion optimizer (Symbolic Discovery of Optimization Algorithms) Published research
Co-author of the 2023 paper that formulates algorithm discovery as program search and reports the Lion (EvoLved Sign Momentum) optimizer, which tracks only momentum and is more memory-efficient than Adam.3
LAMB optimizer (Large Batch Optimization for Deep Learning) Published research
Co-author of the 2019 paper that develops LAMB, a layerwise adaptive large batch optimization technique, and provides convergence analysis for LAMB and LARS.2
Timeline
- Began PhD studies in computer science at UT Austin, advised by Inderjit Dhillon.4
- Joined UC Davis as an assistant professor of computer science and statistics.4
- Joined the UCLA Department of Computer Science.4
- Co-authored CROWN, on robustness certification for neural networks, accepted at NIPS 2018.6
- Co-authored the LAMB paper on large-batch optimization for training BERT.2
- Co-authored Cluster-GCN, published at KDD 2019.5
- Co-authored "Symbolic Discovery of Optimization Algorithms", which introduced the Lion optimizer.3
Other documented roles
Roles the sources document that are not confirmed as current.
Honors and recognition
- Best Research Paper Award, KDD 2010 · ACM SIGKDD, 201041
- Best Paper Award, ICDM 2012 · IEEE International Conference on Data Mining, 201241
- IBM PhD Fellowship · IBM, 201341
- Best Paper Award, ICPP 2018 · International Conference on Parallel Processing, 201841
- Samsung AI Researcher of the Year · Samsung, 202041
- NSF CAREER Award · National Science Foundation, 202141
- Outstanding Paper Award, ICLR 2021 · International Conference on Learning Representations, 202141
- Google Research Scholar Award · Google, 202141
- Okawa Research Award · Okawa Foundation, 20211
Where they work
- UCLA 5 people in the wiki
Related people
- Aditya GroverAlso at UCLA
- Nanyun (Violet) PengAlso at UCLA
- Saadia GabrielAlso at UCLA
- Stefano SoattoAlso at UCLA
- Martín AbadiTraining systems · Machine-learning theory
- Beren MillidgeTraining systems · Machine-learning theory
- John DuchiMachine-learning theory · Training systems
- Michael I. JordanMachine-learning theory · Training systems
- Robert ShawTraining systems
- Rajeev RastogiMachine-learning theory
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References
7 sources · profile reviewed Oct 21, 2026 · data updated Oct 21, 2026
- Cho-Jui Hsieh — homepage (UCLA Computer Science)
- Large Batch Optimization for Deep Learning: Training BERT in 76 minutes (arXiv 1904.00962)
- Symbolic Discovery of Optimization Algorithms (arXiv 2302.06675)
- Cho-Jui Hsieh — curriculum vitae (PDF)
- Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks (arXiv 1905.07953)
- Efficient Neural Network Robustness Certification with General Activation Functions (arXiv 1811.00866)
- Cho-Jui Hsieh — software