Faculty
Feng Chen, is an Associate Professor with tenure in the Department of Computer Science at the University of Texas at Dallas (UTD) from July 2019 to the present. He was an assistant professor at the State University of New York at Albany from January 2014 to July 2019 and a Postdoctoral Fellow at Carnegie Mellon University in 2013. Dr. Chen's research is focused on safe and trust worthy AI. Dr. Chen was awarded an NSF CAREER award in 2018 for his research on ``A Theoretical Framework for Discovering Complex Patterns in Big Attributed Networks.'' He is a member of the IEEE and the ACM. (webpage) |
Students
Changbin Li is a Ph.D. student in Computer Science at The University of Texas at Dallas, where he is supervised by Prof. Feng Chen. He completed his MS at the University of Science and Technology of China (USTC) and his bachelor's degree at Chongqing University. Changbin's research focuses on uncertainty prediction in Graph Neural Networks (GNN), classification uncertainty quantification, uncertainty-aware active learning, deep reinforcement learning, and uncertainty opinion inference using GNN. He also gained practical experience during a summer internship at Alibaba Damo Academy in Seattle, WA, in 2019. | |
Linlin Yu is a Ph.D. student in Computer Science at The University of Texas at Dallas, where her doctoral research primarily focuses on uncertainty quantification and reasoning in graphs using deterministic deep learning models. She is also working on some projects related to uncertainty quantification on domain-specific applications, such as semantic segmentation in Bird's Eye view, named entity recognition in NLP, classification for hyperspectral imaging data, and drug discovery. She got her B.A degree at Shanghai Jiao Tong University. | |
Haoliang Wang is pursuing his Ph.D. in computer science at The University of Texas at Dallas. He holds a master's degree in computer applied technology and a bachelor's degree in computer science from Shanghai Normal University. Haoliang is dedicated to applying machine learning to address real-world problems, with a special interest in developing innovative data augmentation methods to tackle the challenges posed by limited data in machine learning. | |
Yuzhe Ou is a Ph.D. student in Computer Science at The University of Texas at Dallas. He earned his BS in Statistics from Zhejiang University and completed a master's program in Statistics, specializing in Data Science, at UTD. Yuzhe's research interests are centered around Deep Learning, particularly focusing on uncertainty quantification within this domain. | |
Tianhao Wang is a Ph.D. student in Computer Science at The University of Texas at Dallas, where his doctoral research primarily focuses on Autonomous Driving and Deep Reinforcement Learning. He is working on some projects related to autonomous driving, such as BEV based perception models, deep learning based trajectory prediction and controlling. He also worked on projects related to market price forecasting and learning based code vulnerabilities detection. He got his B.A degree at Shandong University. | |
Kai Jiang is in his second year of Ph.D. studies in Computer Science at The University of Texas at Dallas. He got an MS in Computer Science from the University of Florida and a bachelor in Software Engineering from Harbin Engineering University in China. Kai's research interests lie in Deep Learning, specifically in algorithmic fairness and anomaly and out-of-distribution detection. | |
Kangshuo Li is a first-year Ph.D. student in Computer Science at University of Texas at Dallas. His research interests mainly distributed in uncertainty quantification in deep learning models, especially for set prediction models, calibration, and conformalized approaches. Currently, he is actively exploring topics fairness-aware trustworthy AI. Before joining the AISL lab, he earned his M.A. in Statistics degree at Columbia University. |
Alumni
Xujiang Zhao is a researcher at NEC Laboratories America. He earned his Ph.D. degree in Computer Science at UTD in 2022. He is interested in machine learning and data mining, especially in Uncertainty Estimation, Large Language Models, . | |
Chen Zhao is an Assistant Professor in the Department of Computer Science at Baylor University. Prior to joining Baylor, he was a senior R&D computer vision engineer at Kitware Inc. He earned his Ph.D. degree in Computer Science at UTD in 2021. His research focuses on machine learning, data mining, and artificial intelligence, particularly fairness-aware machine learning, novelty detection, and domain generalization. His publications have appeared in prestigious conferences and journals, including KDD, CVPR, WWW, AAAI, ICDM, TKDD, ICASSP, etc. He has served as a PC member for several international conferences and workshops, such as NeurIPS, KDD, AAAI, IJCAI, ICML, ICLR, etc. | |
Kevin Buchan Jr. is Assistant Vice President, Analytics at ApolloMed. He received a Ph.D. in Information Science from the State University of New York at Albany in 2021, co-advised by Prof. Luis Luna-Reyes and Prof. Feng Chen. His dissertation was titled, "Using Machine Learning to Predict Super-Utilizers of Healthcare Services." . | |
Baojian Zhou is an assistant professor of the School of Data Science at Fudan University. He received a Ph.D. in Computer Science from the State University of New York at Albany in 2020. My research interests are data mining and machine learning, especially mining and learning from large-scale graph data. I am also interested in mathematical optimization such as stochastic online optimization and online decision making. My research goal is to find algorithms for solving different kinds of graph problems. | |
Adil Alim is Sr Data Scientist at Lowe's Companies, Inc.. He received a Ph.D. in Computer Science from the State University of New York at Albany in 2019. His research interests are data mining, machine learning, data science, graph theory, network science, uncertainty learning on machine learning models, and anomaly/ interesting pattern/event detection on the attributed/time envolving data. . | |
Yan Hu is Associate Director-Data Science, Advanced Analytics at Novo Nordisk. She was a part-time Ph.D. student and received a Ph.D. in Computer Science from the State University of New York at Albany in 2019. His research interests during Ph.D. were detection of emerging events, deep learning and machine leaning applications in healthcare data. . |