Short Bio

Dongxiao Zhu is currently a 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 AI and Applications in health, urban, and social computing with emphasis on 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 100 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 use-inspired AI algorithms for promoting research in life, physical and social science domains. He is passionate about leveraging AI for social good. And 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

Oct 2024: Two papers accepted by WACV 2025, first authored by my student Chengyin Li.
Sept 2024: I am part of Geriatrics Workforce Enhancement Program project supported by $5M Health Resources and Services Administration grant!
August 2024: I am co-organizing the 3rd Workshop on Adversarial Machine Learning Frontiers @ NeurIPS-24.
July 2024: Congratulations to Dr. Qiang Yao for completing his successful PhD journey and start his career as Tenure-Track Assistant Professor at Oakland University! Dissertation: Designing for Reliability: Theoretical and Applied Perspectives on Trustworthy Artificial Intelligence.
July 2024: Congratulations to Dr. Chengyin Li for completing his successful PhD journey and start his career as a staff scientist at Henry Ford Health! Dissertation: Novel Transformer Architectures for 3D Multi-modal and Multi-organ Medical Image Segmentation.
July 2024: The paper entitled "Fairness-aware Vision Transformer via Debiased Self-Attention" first authored by my student Qiang Yao has been accepted by ECCV-24.
June 2024: I am serving on a NIH study section again.
May 2024: I am excited to participate in NSF Workshop on Envisioning Open Research Resources for AI.
May 2024: I am serving on a NSF CISE panel again.
April 2024: Congratulations to Chengyin Li on winning the prestigious Michael E. Conrad Award!
April 2024: Our AI for mobility and future of work research was featured on Detroit PBS.
March 2024: The MS AI program I created in collaboration with ECE and ISE departments is ranked #20 in the nation by TechGuide.
March 2024: I am serving on a NIH study section.
Feb 2024: Our recent work learns to poison LLMs during instruction tuning!
Feb 2024: I am serving on a NSF CISE panel.
Jan 2024: Our paper titled: Benchmark and Neural Architecture for Conversational Entity Retrieval from a Knowledge Graph is accepted by ACM TheWebConf-24, accept rate is 806/4,028 = 20.2%.
Jan 2024: Our paper titled: Traumatic Data: Applying Trauma-Informed Design to Data Donation of Sexual Violence Experiences to Improve Sexual Risk Detection AI is accepted by ACM CHI-24, accept rate is 1,060/4,028 = 26.3%.
Dec 2023: I gave an invited talk at National Science Foundation, titled "Towards Trustworthy AGI in the Era of Foundation Models – Opportunities, Risks, and Vision”.
Dec 2023: Our paper titled: MFABA: A More Faithful and Accelerated Boundary-based Attribution Method for Deep Neural Networks is accepted by AAAI-24, accept rate is 2,342/12,100 = 23.75%.
Dec 2023: I serve as a meta-reviewer for AI ethics track at International Joint Conference on Artificial Intelligence (IJCAI-24).
Nov 2023: Our recent work on fine tuning Segment Anything Model (SAM) for automated mobility infrastructure segmentation is available on arxiv.
Oct 2023: Our recent work uncovers the vulnerabilities of Large Language Model (LLM) from a novel lens of Adversarial In-Context Learning!
Aug 2023: I serve as a meta-reviewer for Robustness & Trustworthiness track at the 38th Annual AAAI Conference on Artificial Intelligence (AAAI-24).
June 2023: The paper titled "FocalUNETR: A Focal Transformer for Boundary-aware Prostate Segmentation using CT Images" first authored by my student Chengyin Li has been accepted by MICCAI-23, accept rate is 740/2,250 = 32%.
May 2023: Congratulations to Yao Qiang on winning the prestigious Michael E. Conrad Award!
April 2023: I was recommended for the promotion to full professor, effective Aug. 17 2023!
April 2023: The paper entitled "Negative Flux Aggregation to Estimate Feature Attributions" first authored by my student Xin Li has been accepted by IJCAI-23, accept rate is 685/4,566=15%.
April 2023: Our position paper titled "ChatGPT- a promising generative AI tool and its implications on cancer care" published in Cancer.
April 2023: I am giving an invited talk on Enhancing Algorithmic Fairness in Deep Learning at Department of Computer Science and Engineering of University at Buffalo.
April 2023: I am served as a panelist on the workshop on Fairness in Machine Intelligence for Global Health 2023.
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 Enhancing 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.