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Current members
Current members
Professor: Dongxiao Zhu
Professor: Dongxiao Zhu
Dongxiao Zhu is currently a Professor at Department of Computer Science, Wayne State University. He received his Ph.D. from University of Michigan, Ann Arbor. Dr. 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, Inaugural Co-Director of Institute of AI and Data Science, and Director of Trustworthy AI Lab at Wayne State University. He has published 100+ peer-reviewed publications and served on program committees of flagship AI conferences (NuerIPS, ICML, ICLR, CVPR, AAAI, IJCAI, ACL, EMNLP, AMIA, MICCAI) and of premier scientific journals (Bioinformatics, Nucleic Acids Research, Journal of Medical Internet Research, Medical Physics, Transportation Research, Small, and BMC Genomics). Dr. Zhu's research has been supported by NSF, NIH 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 also pursues use-inspired AI research in life, physical and social science domains. He is passionate about leveraging AI for science and social good. And his recent work in developing robust, fair and explainable AI algorithms and systems to ameliorate spatiotemporal mismatch between housing and employment, future of work, and reducing health disparities have been featured in Detroit PBS, WJR radio and Hour Detroit Magazine, just to name a few, manifesting his continuous efforts in achieving 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. Dr. Zhu is also deeply engaged in AI education. As the program’s Founder and Inaugural Director, he led the efforts to launch Wayne State’s M.S. in AI (Algorithm & Software track), regularly teaches foundational AI courses, and actively leads initiatives to increase AI literacy among faculty and students, ensuring that the benefits of AI scholarship and practice extend across disciplines. (more...) (Google Scholar)
Ujunwa Mgboh, PhD candidate
Ujunwa Mgboh, PhD candidate
Ujunwa is currently a Ph.D. student in the Department of Computer Science at Wayne State University. She obtained her M.Sc. in Computer Science from Eastern Michigan University and her B.Sc. in Computer Science from the University of Nigeria. Her research lies at the intersection of artificial intelligence and radiation oncology, with a focus on developing deep learning frameworks for medical image analysis and treatment plan automation. She has pioneered novel approaches in fluence map prediction (FMP) for intensity-modulated radiation therapy (IMRT), introducing transformer-based architectures that enhance spatial consistency and dosimetric accuracy in automated treatment planning. Her work aims to bridge the gap between machine learning innovations and clinical implementation, contributing toward faster, more reliable, and fully automated radiotherapy workflows. She is deeply motivated by her aspiration to advance both research and education in medical AI and hopes to pursue a professorship at a leading university, where she can continue to develop clinically meaningful AI-driven technologies and mentor the next generation of scientists.
Rafi Ibn Sultan, PhD candidate
Rafi Ibn Sultan, PhD candidate
Rafi is a Ph.D. candidate and Graduate Research Assistant in Computer Science at Wayne State University. Since joining the lab in 2022, his research has focused on computer vision and image segmentation across multiple modalities and domains. He explores the intersection of foundation models and vision–language models (VLMs) to enhance segmentation-based scene understanding. His recent works span AI for social good, including mobility infrastructure segmentation with vision foundation models (link) and text-assisted medical image segmentation using VLMs (link). Ongoing projects involve coronary artery segmentation with novel encoder–decoder architectures (link) and grounded conversational models for pedestrian navigation, integrating segmentation, depth, and language reasoning for accessibility. His long-term goal is to advance trustworthy, multimodal AI systems that bridge visual understanding and real-world societal impact. Outside of research, Rafi enjoys collaborating with peers on new ideas and is always eager to take on exciting, challenging projects.
Useful links: LinkedIn, Website, Google Scholar, GitHub
Xiangyu Zhou, PhD candidate
Xiangyu Zhou, PhD candidate
Xiangyu Zhou is currently advancing as a Ph.D. candidate in the Department of Computer Science at Wayne State University, specializing in the realm of Trustworthy AI. Xiangyu embarked on his academic journey by obtaining his MS degree from the Stevens Institute of Technology. His research ambitiously spans areas like safety and adversarial robustness in the Large Language Models or Large Reasoning Models and the innovative domain of In Context Learning (ICL). Prior to this, Xiangyu laid a strong foundation by studying computer science and software engineering, which further led him to develop practical applications such as chat platforms, data donation tools, and movie forums.
Mohammad Amin Roshani, PhD candidate
Mohammad Amin Roshani, PhD candidate
Mohammad Amin Roshani is a Ph.D. student in Computer Science at Wayne State University whose research focuses on applying artificial intelligence to medicine and bioinformatics. He develops large language models and AI agent systems for clinical and biomedical tasks, emphasizing trustworthiness, interpretability, and real-world deployment. His current work involves extracting Social Determinants of Health (SDOH) from the MIMIC dataset and leading a newly funded collaboration with the School of Nursing to build an AI-assisted health application for older adults that streamlines data sharing, mobility tracking, and clinical communication. Previously, he contributed to an NIH-funded project on COVID-19 severity risk assessment. His goal is to create reliable and human-centered AI systems that enhance decision-making and promote equitable, transparent healthcare.
Hui Zhu, PhD candidate
Hui Zhu, PhD candidate
Hui Zhu is a Ph.D. candidate in Computer Science at Wayne State University with a B.S. degree from Dalian University of Technology. His academic focus lies at the intersection of computer vision and machine learning. He has developed a strong interest in understanding and explaining the behavior of foundation models, especially large-scale vision models. His work centers on interpretation methods that reveal how these models process information and make predictions, improving transparency and trust in modern AI systems. In addition, his current research also explores hallucination in vision-language models, aiming to better understand and reduce incorrect or misleading outputs in multimodal AI systems.
Saleh Zare Zade, PhD candidate
Saleh Zare Zade, PhD candidate
Saleh Zare Zade is a Ph.D. candidate in Computer Science at Wayne State University specializing in Trustworthy Artificial Intelligence. His research examines the safety and reliability of large language models (LLMs), focusing on adversarial in-context learning (link), data poisoning (link), membership inference attack (link), and LLM unlearning (link, link). He is driven by the question of how large language models can remain powerful and useful while preserving privacy and resisting manipulation. His work combines empirical analysis with theoretical insights, aiming to uncover hidden vulnerabilities in model training and inference pipelines. He studies how these models can be manipulated or attacked and designs methods that improve their defense, calibration, and interpretability. He aims to connect cutting-edge research with real-world challenges and collaborate with others who share the goal of advancing trustworthy and responsible AI.
Yiannos Demetriou, MD/PhD candidate
Yiannos Demetriou, MD/PhD candidate
Yiannos is a second year MD/Ph.D. student planning to pursue a Ph.D. in Computer Science. He received his B.Sc. from the University of Michigan. His research interests lie in advancing and developing machine learning methods to improve the practice of medicine. Outside of that, he enjoys hiking, reading, and video games.
Hao Liu, PhD pre-candidate
Hao Liu, PhD pre-candidate
Hao Liu graduated in December 2019 with my undergraduate degree from Kent State University, where I studied computer science for about 4 years. After that time, I have been working as an IT systems engineer in Michigan for over 2.5 years. As a result, I have extensive experience in computer systems engineering, programming and data science.
Supriya TS. Kumar, MS candidate
Supriya TS. Kumar, MS candidate
I am a Data scientist/Analyst with 5 years of industry and research experience. Currently a Computer Science graduate student at Wayne State University with specialization in Machine Learning and its application in different sectors. I also hold a master’s in Electrical and Computer Engineering from Oklahoma State University. With my varied experience as research assistant and data scientist I got to work with large data in healthcare, natural language processing, social media etc., to develop Machine Learning/Deep Learning models and systems with focus on adversarial robustness, explainability and fairness in AI.
Harrison Larvins, MS candidate
Harrison Larvins, MS candidate
Harrison is currently a Robotics M.S. student at Wayne State University and software engineer based in Detroit, MI. He received the B.S. in Computer Science and a B.M. in Music at Oakland University in 2021. He is a certified ScrumMaster and Scrum Developer, and has experience in a variety of projects related to machine learning, mobile development, full-stack web development, autonomous robotic systems, and algorithmic music composition. His research interests are in the design of wearable embedded systems, applying deep learning and natural language processing methods within the fields of music theory and musicology, and explainable recommender and computer vision systems. His hobbies include playing the piano and traveling
Md. Sajid Alam Chowdhury, PhD student
Md. Sajid Alam Chowdhury, PhD student
Sajid will be joining Wayne State University’s Computer Science program as a Ph.D. student in Winter 2026. His research interests include Computer Vision and Natural Language Processing, with a particular focus on model security and robustness. He has prior experience in adversarial machine learning, working with a range of adversarial attacks and developing adversarial defense methods. Sajid completed his B.Sc. in Computer Science and Engineering from Chittagong University of Engineering and Technology (CUET). After completing his undergraduate degree, he worked as a Software Engineer at Samsung R&D Institute Bangladesh.
Recent graduates
Recent graduates
Xiangrui Li, PhD 2020
Xiangrui Li, PhD 2020
Xiangrui Li is a 4th year PhD student in Computer Science. Before entering Wayne State University, he received BS and MS in math from University of Science and Technology of China. His research interests are machine learning and its applications in healthcare and imbalanced learning in deep learning.
Current position: senior algorithm engineer @ ByteDance Ltd
Xin Li, PhD 2022
Xin Li, PhD 2022
Xin Li is currently a Ph.D. candidate at the Department of Computer Science, Wayne State University. He received the B.S. degree in Electrical and Computer Engineering from Shanghai Jiao Tong University (2013), the B.S. and M.S. degrees in Atmospheric and Space Sciences from University of Michigan (2016) and the M.S. degree in Industrial and Operation Engineering degree from University of Michigan (2016). Xin’s research interest is about theories and applications of deep learning in visual recognition and natural language processing for healthcare. He also works as a graduate teaching assistant and enjoys sharing knowledge, communicating concepts and inspiring deep thinking with students.
Current position: research scientist II @ Bosch AI
Deng Pan, PhD 2022
Deng Pan, PhD 2022
Deng is currently a PhD candidate at Department of Computer Science, Wayne State University. He received B.S. degree of Financial Mathematics from South University of Science and Technology. Deng’s research interests include deep learning based recommender systems, semi-supervised learning techniques, and graph neural networks. Personal hobbies include math, physics, JRPG, anime, electronics.
Current position: algorithm engineer @ Alibaba Group
Yao Qiang, PhD 2024
Yao Qiang, PhD 2024
Yao Qiang is currently a tenure-track assistant professor at the Department of Computer Science and Engineering at Oakland University, working in the Trustworthy AI. He received his bachelor's degree from Xidian University. His research mainly focuses on Trustworthy AI, Natural Language Processing, Large Language Models, Foundational Machine Learning, Medical Image Segmentation, etc. His dedication to these areas has culminated in the publication of numerous research papers at the most competitive AI conferences, including NeurIPS, IJCAI, AAAI, ICML, MICCAI, IJCNN, etc. In addition to his academic accomplishments, Yao has acquired valuable practical experience through a three-month internship as an Applied Scientist at Amazon, focusing on improving the robustness of LLMs against prompt perturbations. Yao's passion for research not only drives him to delve deeper into the frontiers of science but also encourages him to transform theoretical discoveries into practical innovations that make a meaningful impact on society.
Chengyin Li, PhD 2024
Chengyin Li, PhD 2024
Chengyin Li is currently a staff scientist at Henry Ford Health Sciences. He received his bachelor's degree from Nanjing University of Science and Technology, and a master's degree from University of Chinese Academy of Sciences. His research mainly focuses on Medical Image Applications, Trustworthy AI, and Visual Foundation Models (VFMs). He has successfully contributed to multiple AI research papers accepted for publication in major conference venues and journals, including NeurIPS, IJCAI, MICCAI, ECML, Medical Physics, and Pediatric Research. Additionally, he has been actively engaged in research at Henry Ford Health System, with a primary emphasis on enhancing the performance and robustness of medical image segmentation tasks.
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