Xujiang Zhao Xujiang Zhao, NEC Laboratories America
Short bio: Dr. Xujiang Zhao is a research staff member at NEC Laboratories America. He received his Ph.D. in Computer Science Department at The University of Texas at Dallas in 2022. Dr. Zhao has published his work in top-tier machine learning and data mining conferences, including NeurIPS, AAAI, ICDM, and EMNLP. He also served on technical program committees for several high-impact venues, such as ICML, NeurIPS, ICLR, KDD, and AAAI.
Chen Zhao Chen Zhao, Baylor University.
Short bio: Dr. Zhao is an Assistant Professor at the Department of Computer Science at Baylor University. He received his Ph.D. in Computer Science from the University of Texas at Dallas in 2021. Dr. Zhao’s research interests include large-scale data mining, machine learning and computer vision with a focus on fairness-aware machine learning and novelty detection. His research has been accepted and published in premier conferences, including KDD, CVPR, ICASSP, AAAI, WWW, ICDM, PAKDD, etc. Besides, Dr. Zhao served as PC members of top international conferences, such as KDD, NeurIPS, ICDM, AAAI, IJCAI, SDM, BigData, ECMLPKDD, AISTATS, WSDM, WACV, etc.
Feng Chen Feng Chen, University of Texas at Dallas
Short bio: Dr. Chen is an Associate Professor at the Department of Computer Science at the University of Texas at Dallas, where he directs the Pattern Discovery and Machine Learning Laboratory. He was previously an Assistant Professor at University at Albany – SUNY and a Postdoctoral Fellow at Carnegie Mellon University. He received his Ph.D. in Computer Science from Virginia Tech in 2012. Dr. Chen’s research interests include large-scale data mining, network mining, and machine learning, with a focus on event and pattern discovery in massive, complex networks. His research has been funded by NSF, NIH, ARO, IARPA, and the U.S. Department of Transportation, and published in more than 100 peer-reviewed in premier conferences, such as KDD, ICDM, WWW, CIKM, AAAI, IJCAI, ICML, and NeurIPS, and in top journals, such as TKDD, TKDE, TIST, KAIS, and Proceedings of the IEEE. He is a recipient of NSF CAREER award in 2018 and the NSF FAI award entitled “A Novel Paradigm for Fairness-Aware Deep Learning Models on Data Streams” in 2022 as the principal investigator (PI).
Jinhee Cho Jinhee Cho, Virginia Tech.
Short bio: Dr. Jin-Hee Cho is an Associate Professor in Computer Science at Virginia Tech. Dr. Cho published more than 170 peer-reviewed technical papers in cybersecurity, decision-making, and network security. She has received six best paper awards, including four best conference papers and two journal papers. She is a winner of the 2015 IEEE Communications Society William R. Bennett Prize in the Field of Communications Networking. Dr. Cho was selected for The 2013 Presidential Early Career Award for Scientists and Engineers (PECASE), the highest honor the US government has for outstanding scientists and engineers in the early stages of their independent research careers. She is a recipient of the 2022 Dean's Award for Excellence as Faculty Fellow by the College of Engineering at Virginia Tech.
Haifeng Chen Haifeng Chen, NEC Laboratories America.
Short bio: Dr. Haifeng Chen is heading the Data Science and Systems Security Department at NEC Laboratories America. Haifeng has served on the program committee for several top conferences, such as SigKDD and AAAI, and has been on the panel of NSF programs. He has co-authored more than a hundred conference/journal publications, including best papers from top conferences such as SigKDD, and has over 60 patents. Most research results have led to advanced solutions and products for various industrial domains, including IT & data centers, network security, power plants, petroleum, satellite, natural disaster, finance, and retail businesses. In recognition of his extraordinary research contribution, Haifeng has received many awards in the past years, including the 2014 “NEC Contributors of the Year.”

Program Committee

The following PC members have confirmed their participation in the workshop: