Regular biography
Cho-Jui Hsieh is an Associate Professor in the Department of Computer Science at the University of California, Los Angeles. His research interests include machine learning, data mining, optimization, and adversarial deep learning. He is affiliated with the UCLA Samueli School of Engineering. Hsieh's work has been recognized with several awards, including the Okawa Foundation Research Award, VNN-COMP Award, ICLR Outstanding Paper Award, Google Research Scholar Award, NSF CAREER Award, Facebook Research Award on Computationally Efficient NLP, and Best paper awards at ICPP, ICDM, and KDD. His contact information is available at chohsieh@cs.ucla.edu and his profile page can be found at https://samueli.ucla.edu/people/cho-jui-hsieh/.
Scholar-generated biography
Cho-Jui Hsieh is a researcher at the University of California, Los Angeles, specializing in Machine Learning and Optimization. His work focuses on developing efficient algorithms for large-scale machine learning tasks, including linear classification, deep neural networks, and graph convolutional networks. He has contributed to advancements in optimization techniques for training models like BERT and Vision Transformers, emphasizing robustness and efficiency. His research also explores black-box attacks on neural networks using zeroth-order optimization methods. Hsieh's publications highlight the intersection of optimization and machine learning, with applications in natural language processing, computer vision, and robust model training.