Short Bio

Dongxiao Zhu is currently an Associate Professor at Department of Computer Science, Wayne State University. He received his Ph.D. from University of Michigan (2006). Dongxiao Zhu's recent research interests are in Trustworthy Machine Learning and Applications in health, urban, and social computing with emphasis on S & P, adversarial robustness, explainability and fairness. Dr. Zhu is the Founding Director of Wayne AI Research Initiative, Director of Trustworthy AI Lab, and Directors of Computer Science Graduate Program and Master Program in AI (Algorithms and Systems Track) at Wayne State University. He has published over 80 peer-reviewed publications and served on program committees (Senior PC or PC) of flagship AI conferences (NuerIPS, ICML, ICLR, CVPR, AAAI, IJCAI, ACL, EMNLP, AMIA, MICCAI) and of premier biomedical informatics journals (Bioinformatics, Nucleic Acids Research, TCBB, Scientific Reports, BMC Genomics, and Frontiers in Genetics). Dr. Zhu's research has been supported by NIH, NSF and private agencies. Dr. Zhu's teaching interest lies in programming language, data structures and algorithms, machine learning and data science.

In addition to foundational AI research, Dr. Zhu develops tailor-made AI algorithms for promoting research in life, physical and social science domains. He is passionate about leveraging AI for social good. He develops robust, fair and explainable AI algorithms and efficient systems to optimize public service delivery via learning geo-social features from geo-tagged big data, in efforts to achieve the sustainable development goals such as zero hunger, better cyber-social behaviors, good health and well-being, and reduced inequalities in socially vulnerable regions/groups.

News

March 2023: I am co-organizing 2nd Workshop on New Frontiers in Adversarial Machine Learning (AdvML) at ICML-2023!
Feb 2023: I served as a topic editor for Evolution in Statistical Genetics and Methodology at Frontiers in Genetics journal.
Feb 2023: I served as a meta-reviewer for AI ethics track at International Joint Conference on Artificial Intelligence (IJCAI-23).
Jan 2023: I gave an invited talk on Advancing Algorithmic Fairness in Deep Learning at Department of Statistics and Probability of Michigan State University.
Jan 2023: I created the new online MS Program in Computer Science with concentration on AI.
Dec 2022: I received a NIH R33 grant (MPI) in developing a chatbot to acquire social, clinical and molecular determinants for personalized on-device MIS-C risk assessment, total amount is $1,449,684.
Dec 2022: I received a NSF Convergence Accelerator grant (co-PI) in learning geospatial features and developing human-centered AI for enhancing mobility equity for persons with disabilities, total amount is $613,621.
Nov 2022: The paper entitled "Learning Compact Features via In-Training Representation Alignment" first authored by my student Xin Li has been accepted by AAAI-23, accept rate is 1,721/8,777=19.6%.
Nov 2022: I gave an invited talk on Empowering Explainable Machine Learning through the Lens of Adversarial Robustness and Fairness at Computer Science and Engineering Department of Michigan State University.
Sept 2022: Trustworthy AI paper titled "AttCAT: Explaining Transformers via Attentive Class Activation Tokens." first authored by my student Mr. Yao Qiang has been accepted by NuerIPS-22, accept rate is 2,665/10,411 = 25.6%.
Aug 2022: I received a NSF HCC grant (PI) in leveraging AI for better cyber-social behavior via enhancing trust in data donation from users, total amount is $600,000.
Aug 2022: I created the new Master program in AI (Algorithms and Systems Track) hosted by Computer Science department. Interested students please contact me for further info.
June 2022: Congratulations to Dr. Xin Li for completing his successful PhD journey! Dissertation: Adversarial Machine Learning for Advanced Medical Imaging Systems.
June 2022: My student Mr. Chengyin Li's first first-author machine learning paper on privacy-aware and resource-constraint EV charging recommendation algorithm titled "Coupling User Preference with External Rewards to Enable Driver-centered and Resource-aware EV Charging Recommendation" accepted by ECML-PKDD 2022, accept rate is 242/932 = 26%.
May 2022: I am giving a Keynote Speech at Adversarial Machine Learning towards Advanced Vision Systems (AMLAVS) workshop @ACCV-2022.
April 2022: Trustworthy AI paper titled "Counterfactual Interpolation Augmentation (CIA): A Unified Approach to Enhance Fairness and Explainability of DNN." first authored by my student Mr. Yao Qiang has been accepted by 31st International Joint Conference on Artificial Intelligence (IJCAI-22), accept rate is 680/4535 = 15%.
April 2022: I am honored and pleased to be selected to receive the "College of Engineering Excellence in Research" Award. Thanks to the department of computer science and college of engineering for your support!
March 2022: One of the collaborative work on "Deep learning protocol for improved photoacoustic brain imaging" has been recognized as a top cited article in Journal of Biophotonics (Certificate).
March 2022: I am co-organizing 1st Workshop on New Frontiers in Adversarial Machine Learning (AdvML) at ICML-2022! Program focuses on new theory/algorithm/application stack of AdvML and Trustworthy ML (Poster). Submission deadline: May 23 2022.
March 2022: Congratulations to Dr. Deng Pan for completing his successful PhD journey! Dissertation: Interpretable Machine Learning and Applications.
Jan 2022: Announcements - two fully funded PhD student positions in machine learning research available in Fall 2022! Strong background in mathematics and programming are desirable. Contact me with CV, transcripts, and/or publications.
Dec 2021: New tenure-track faculty position available @ Wayne State CS - preferred qualification in machine learning and AI.
Nov 2021: I am giving an invited talk titled "Robust and Explainable Machine Learning Algorithms for Social Good" at Department of Computer and Data Sciences at Case Western Reserve University.