Yang (Arvin) Shi is an Assistant Professor of Computer Science at Utah State University. He has been working towards building data-driven methods for representing program code to enhance the ability of Intelligent Tutoring Systems and benefit student modeling processes for computing education. With a focus on DM/ML approaches applied to CS education, his research interests also include Programming Language Processing, Software Analysis, and Deep Learning. He has served as a (Senior) program committee member in conferences across EdTech (EDM, LAK, AIED, SIGCSE, ICER) and AI (KDD, AAAI, NeurIPS) disciplines, and co-organized the Educational Data Mining in Computer Science Education (CSEDM) workshop since 2020.

Qingsong Wen is currently the Head of AI & Chief Scientist at Squirrel Ai Learning (a top EdTech unicorn), and PhD Supervisor at University of Oxford. Before that, he worked at Alibaba, Qualcomm, Marvell, etc., and received his M.S. and Ph.D. degrees in Electrical and Computer Engineering from Georgia Institute of Technology, USA. His research interests include machine learning, data mining, and signal processing, especially AI for Time Series, AI for Education, LLM & AI Agent. He has published over 150 top-ranked AI conference and journal papers, had multiple Oral/Spotlight Papers at NeurIPS, ICML, ICLR, ACL, AAAI, had multiple Most Influential Papers at IJCAI, received multiple IAAI Innovative Application Awards at AAAI, and won First Place of SP Grand Challenge at ICASSP. Currently, he serves as Chair of IEEE CIS Task Force on AI for Time Series and Spatio-Temporal Data, and Vice Chair of INNS AI for Education Section. He also regularly serves as Area Chair of the top conferences including NeurIPS, ICML, ICLR, KDD, IJCAI, ICASSP, etc, and Associate Editor for IEEE TPAMI (IF=18.6).

Xiangliang Zhang is a Leonard C. Bettex Collegiate Professor of Computer Science at University of Notre Dame, where she is leading a Machine Intelligence and kNowledge Engineering (MINE) group. Her research broadly addresses ways that enable ​computer machines to​ learn by the use of diverse types of data. Specifically, she is interested in designing machine learning algorithms for learning from complex and large-scale streaming data and graph data, with applications to recommendation systems, knowledge discovery, and natural language understanding. Her recent research notably advances the application of AI in scientific disciplines such as Chemistry, Biology and Physics. More information can be found from her over 300 peer-reviewed publications. She was invited to deliver an Early Career Spotlight talk at IJCAI-ECAI 2018. In 2009, she was awarded the European Research Consortium for Informatics and Mathematics (ERCIM) Alain Bensoussan Fellowship. She regularly serve on the Program Committee for premier conferences like SIGKDD (Area Chair, Senior PC), AAAI (Area Chair, Senior PC), IJCAI (Area Chair, Senior PC), etc. She also serves as Editor-in-Chief of ACM SIGKDD Explorations, associated editor for ACM transactions on Knowledge Discovery from Data (TKDD) and Information Sciences. She recently serves as the PC Co-chair of KDD 2026 (research track). Prior to joining the University of Notre Dame, she was an Associate Professor in Computer Science at KAUST, Saudi Arabia. She completed her Ph.D. degree in computer science from INRIA-University Paris-Sud, France, in July 2010. She received her master and bachelor degrees from Xi’an Jiaotong University, China. 

Alison Cheng is a Professor of Psychology at University of Notre Dame, is a world-renowned expert in psychological and educational measurement and learning analytics. Her recent work on predicting student learning in STEM courses may be very useful to shed light on how to predict the effectiveness of our lower division math courses. Dr. Cheng received her bachelor’s degree in 2003 from University of Science and Technology of China, one of the most prestigious universities in China. She received her master’s and PhD degrees from UIUC in 2007 and 2008, respectively. Since then, she has been an assistant, associate, and full professor at University of Notre Dame. Since 2021 she has been the Associate Director of the Lucy Family Institute for Data and Society at University of Notre Dame. Dr. Cheng is an elected fellow of Association for Psychological Science, and elected fellow of American Psychological Association. She has won numerous awards, including the NSF CAREER award, and First place in 2023 EDM Cup hosted by International Educational Data Mining Society (IEDMS) on Kaggle. Dr. Cheng has served many leadership roles in the Psychometric Society, and as editors for many prestigious journals, including being the Chief Editor of one of the most prominent journals in her area.

John Stamper earned his Ph.D. in computer science at the University of North Carolina at Charlotte. His main area of research is focused on using big data from educational systems to improve learning. He generally publishes in the areas of intelligent tutoring systems and educational data mining. He is also the lead researcher behind DataShop, which is the largest open repository of log data from learning systems.  Prior to starting his Ph.D., Stamper spent over ten years in the business world. Although he is still consulting, his most recent major position was Vice President of Development for VSI Technolgies, Inc. During this role, he earned his MCSE (Microsoft Certified Systems Engineer) and MCDBA (Microsoft Certified Database Administrator) certifications. Recently, he has been involved in creating a new startup called TutorGen, which is looking to help build intelligent tutoring capabilities for existing educational technologies with the use of big data. 

Dragan Gašević is Distinguished Professor of Learning Analytics in the Faculty of Information Technology and the Director of the Centre for Learning Analytics (CoLAM) at Monash University. His research interests center around data analytic, AI, and design methods that can advance understanding of self-regulated and collaborative learning. Previously, he was a Professor and the Sir Tim O’Shea Chair in Learning Analytics and Informatics (Feb 2015–Feb 2018) in the Moray House School of Education and the School of Informatics and Co-Director of Centre for Research in Digital Education at the University of Edinburgh. He was the Canada Research Chair in Semantic and Learning Technologies and Professor in the School of Computing and Information Systems at Athabasca University (Jan 2007–Jan 2015). He is a founder and served as the President (2015–2017) of the Society for Learning Analytics Research (SoLAR), the world’s leading research and professional organization in learning analytics. He has held several honorary professorships and industry fellowships in Asia, Australia, Europe and North America. He served as a founding program chair of the International Conference on Learning Analytics & Knowledge (LAK) in 2011 and 2012, the general chair in 2016, a founding program co-chair of the Learning Analytics Summer Institute (LASI) in 2013 and 2014, and a founding editor of the Journal of Learning Analytics (2012–2017) and Computers & Education: Artificial Intelligence (2020–present). In 2019–2025, he was recognized as the national field leader in educational technology in The Australian – the only Australian daily newspaper distributed nationally. He led the EU-funded SHEILA project that received the Best Research Project of the Year Award (2019) from the Association for Learning Technology. In 2022, he received the Lifetime Member Award, the highest distinction of the Society for Learning Analytics Research (SoLAR) and named a Distinguished Member of the Association for Computing Machinery (ACM), the world’s largest computing society. 

Steven Moore is an assistant professor in the Department of Information Sciences and Technology at George Mason University. He studies how to design educational technologies that improve student learning and how people use AI to learn. Drawing on learning science, human-computer interaction, and applied natural language processing, he builds and evaluates AI-enhanced courseware and assessment tools. His work advances learner sourcing, crowdsourcing, and human-AI collaboration for content creation and feedback at scale. Recently, he has focused on leveraging large language models to support instructional design by applying structured rubrics consistently across varied content types. His academic research is informed by extensive industry experience and consulting with universities and school districts. 

Ritu Chaturvedi received her PhD from the University of Windsor. Between 2016 and 2017, she held a position as an Assistant Professor (Teaching Stream) at the University of Toronto, Mississauga. Chaturvedi joined the School of Computer Science at the University of Guelph in 2017 where she is now an Assistant Professor. 

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