I’m a full professor in the Department of Information Science at the University of Arkansas at Little Rock. My passion is to solve real-world problems through innovative transdisciplinary research, and my research interests are OmniTrustAI, Causal AI, Large Language Models, In-context Learning, Transfer Learning, Lifelong Metric Learning, Active Learning, Density-Based Clustering, and Network Clustering.

The burgeoning field of generative AI and Large Language Model (LLM) has outstripped human capabilities in numerous sectors. However, significant concerns persist regarding their societal impact. In response, I have dedicated myself to OmniTrustAI, an AI model designed to be universally applicable and trustworthy, thereby empowering businesses of all sizes. My notable contribution includes the development of the TOAA (Train Once, Apply Anywhere) framework. This innovative approach involves customizing foundation large language models with specific domain and organizational knowledge, facilitating a safe and robust application of generative AI for domain-specific tasks without the need for retraining. A prime example of TOAA's effectiveness is OmniMatch, a groundbreaking entity-matching tool. OmniMatch is designed to operate "out-of-the-box," capable of matching entities across a variety of domains without requiring additional training. Please watch a video on OmniMatch for more information.

My contributions to the field of clustering algorithms have been recognized with the prestigious ACM SIGKDD Test of Time Award for my seminal paper on the Density-Based Clustering Algorithm DBSCAN, which is one of the most popular clustering algorithms with many open-source implementations, including scikit-learn.

I am also committed to teaching and have taught courses on Deep Learning, Large Language Models (BERT, GPT, ChatGPT), Causal Inference, and Applications at the International School on Deep Learning and the International School on Big Data. In addition, I will teach the workshop on AI for Natural Language Processing in AR-BIC 2023, and recently have been invited to give a seminar on Large Language Models and Causal Inference in the Department of Linguistics at Seoul National University in Korea.

I serve as an editorial board member for various international journals, including  Trends in Artificial Intelligence and In silico Methods and Artificial Intelligence for Drug Discovery (specialty section of Frontiers in Drug Discovery). Additionally, I have served as a Program Committee member for several primary international conferences, including ACM SIGKDD.

You can find my publication in Google Scholar and DBLP.

I earned my Ph.D. degree in computer science from the Ludwig-Maximilian Universität München, and have an MS degree in computer science from the Chinese Academy of Sciences and a BS degree in Mathematics from Nankai University.

For more information about me, please visit my profiles on ResearchGate, LinkedIn, the Mathematics Genealogy Project, or ORCiD. I am also the founder of the Mid-south Computational Biology and Bioinformatics Society.

When I am not pursuing academic interests, I like to stay active by running and have completed several marathons, including the Chicago and San Francisco marathons. I also enjoy cycling, swimming, hiking, and spending time with my family and friends. I love traveling and learning about different cultures.

Please feel free to contact me via email at xwxu  @  ualr.edu.