2026 Cho-Jui Hsieh: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Computer Science	74	1457	1412	759	731	303	33250

Overview

Cho-Jui Hsieh is affiliated with the University of California, Los Angeles in the United States and has a research profile primarily focused on computer science with a strong emphasis on artificial intelligence.

Their recent research contributions include the following papers:

DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification (2021), published in arXiv (Cornell University)
Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with Adversarial Examples (2020), published in Proceedings of the AAAI Conference on Artificial Intelligence
Symbolic Discovery of Optimization Algorithms (2023), published in arXiv (Cornell University)
Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations (2020), published in arXiv (Cornell University)
When Vision Transformers Outperform ResNets without Pre-training or Strong Data Augmentations (2021), published in arXiv (Cornell University)

Frequent coauthors who have collaborated extensively with Cho-Jui Hsieh include:

Pin-Yu Chen
Minhao Cheng
Inderjit S. Dhillon
Zhouxing Shi
Ruochen Wang

The scientist's work appears prominently in several publication venues, including:

arXiv (Cornell University)
Proceedings of the AAAI Conference on Artificial Intelligence
2021 IEEE/CVF International Conference on Computer Vision (ICCV)
IEEE Transactions on Neural Networks and Learning Systems
Neural Networks

The main field of study is computer science, with key subfields including:

Artificial Intelligence
Computer Vision and Pattern Recognition
Signal Processing
Electrical and Electronic Engineering
Computational Mechanics

Cho-Jui Hsieh's research topics focus on several areas related to machine learning and artificial intelligence:

Adversarial Robustness in Machine Learning
Anomaly Detection Techniques and Applications
Advanced Neural Network Applications
Topic Modeling
Domain Adaptation and Few-Shot Learning
Machine Learning and Data Classification
Natural Language Processing Techniques

Best Publications

LIBLINEAR: A Library for Large Linear Classification

Rong-En Fan;Kai-Wei Chang;Cho-Jui Hsieh;Xiang-Rui Wang

9525
Citations
ZOO: Zeroth Order Optimization Based Black-box Attacks to Deep Neural Networks without Training Substitute Models

Pin-Yu Chen;Huan Zhang;Yash Sharma;Jinfeng Yi

1739
Citations
VisualBERT: A Simple and Performant Baseline for Vision and Language.

Liunian Harold Li;Mark Yatskar;Da Yin;Cho-Jui Hsieh

1462
Citations
A dual coordinate descent method for large-scale linear SVM

Cho-Jui Hsieh;Kai-Wei Chang;Chih-Jen Lin;S. Sathiya Keerthi

1187
Citations
Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks

Wei-Lin Chiang;Xuanqing Liu;Si Si;Yang Li

1122
Citations
Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent

Xiangru Lian;Ce Zhang;Huan Zhang;Cho-Jui Hsieh

776
Citations
Training and Testing Low-degree Polynomial Data Mappings via Linear SVM

Yin-Wen Chang;Cho-Jui Hsieh;Kai-Wei Chang;Michael Ringgaard

646
Citations
EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples

Pin-Yu Chen;Yash Sharma;Huan Zhang;Jinfeng Yi

645
Citations
Large Batch Optimization for Deep Learning: Training BERT in 76 minutes

Yang You;Jing Li;Sashank Reddi;Jonathan Hseu

518
Citations
Towards Fast Computation of Certified Robustness for ReLU Networks

Tsui-Wei Weng;Huan Zhang;Hongge Chen;Zhao Song

501
Citations
ImageNet Training in Minutes

Yang You;Zhao Zhang;Cho-Jui Hsieh;James Demmel

451
Citations
Efficient Neural Network Robustness Certification with General Activation Functions

Huan Zhang;Tsui-Wei Weng;Pin-Yu Chen;Cho-Jui Hsieh

444
Citations
Towards Robust Neural Networks via Random Self-ensemble

Xuanqing Liu;Minhao Cheng;Huan Zhang;Cho-Jui Hsieh

435
Citations
Large Linear Classification When Data Cannot Fit in Memory

Hsiang-Fu Yu;Cho-Jui Hsieh;Kai-Wei Chang;Chih-Jen Lin

428
Citations
Sparse Inverse Covariance Matrix Estimation Using Quadratic Approximation

Cho-jui Hsieh;Inderjit S. Dhillon;Pradeep K. Ravikumar;Mátyás A. Sustik

413
Citations
AutoZOOM: Autoencoder-Based Zeroth Order Optimization Method for Attacking Black-Box Neural Networks

Chun-Chen Tu;Paishun Ting;Pin-Yu Chen;Sijia Liu

380
Citations
Scalable Coordinate Descent Approaches to Parallel Matrix Factorization for Recommender Systems

Hsiang-Fu Yu;Cho-Jui Hsieh;Si Si;Inderjit Dhillon

347
Citations
Coordinate Descent Method for Large-scale L2-loss Linear Support Vector Machines

Kai-Wei Chang;Cho-Jui Hsieh;Chih-Jen Lin

326
Citations
Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach

Tsui-Wei Weng;Huan Zhang;Pin-Yu Chen;Jinfeng Yi

324
Citations
Memory efficient kernel approximation

Si Si;Cho-Jui Hsieh;Inderjit S. Dhillon

319
Citations
Towards Stable and Efficient Training of Verifiably Robust Neural Networks

Huan Zhang;Hongge Chen;Chaowei Xiao;Sven Gowal

178
Citations

Frequent Co-Authors

Huan Zhang University of California, Los Angeles

Inderjit S. Dhillon Google (United States)

Jinfeng Yi IBM (United States)

Pin-Yu Chen IBM (United States)

Kai-Wei Chang University of California, Los Angeles

Duane S. Boning MIT

James Demmel University of California, Berkeley

Sanjiv Kumar Google (United States)

Pradeep Ravikumar Carnegie Mellon University

Chih-Jen Lin National Taiwan University

External Links

Personal website of Cho-Jui Hsieh Google Scholar page

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