Dr. Yi He of Old Dominion University will present an on-campus Computer Science Colloquium lecture on November 4, 2022, at 3:00 p.m., in Rekhi Hall Room 214. The talk is presented by the Department of Computer Science.
The title of He’s talk is, “Open-World Machine Learning: Paradigm, Challenges, and Prospects.”
He’s research focus lies broadly in data mining and machine learning, and specifically in online learning, data stream analytics, graph learning, recommender systems, and explainable artificial intelligence.
Talk Abstract: Machine learning (ML) strives to build mathematical models that uncover and mimic the hidden decision-making process of human minds in a data-driven fashion. Despite its popularity and successes in many industrial and scientific endeavors, a fundamental difference lying between the learning environments of ML models and human-beings hinders the widespread application of ML in real tasks.
To wit, we humans live in an evolving world so must keep adjusting and reshaping our mental process to accommodate the world’s dynamics and be open to new concepts. While in ML, the models are prescribed to live in a closed world, where the environment into which the models will be deployed in future are prescribed to be identical as that the models were trained with before. As a result, the more wildly the environment evolves, the more rapidly the ML model performance decays.
How to enable ML to work in an open world, to generalize to a changeable data distribution, and to adapt to newly emerging concepts and decision options? The solutions to this question are of a great importance and interest in expanding the ML application domains, and eventually, paving the path to the development of general artificial intelligence. This talk shall focus on 1) summarizing the state of the art of machine learning in open world and 2) envisioning the opportunities by presenting the research gaps and the great potential for real applications.
Bio: Dr. Yi He is an assistant professor of Computer Science at ODU. He received a Ph.D. in computer science from the University of Louisiana at Lafayette, under the supervision of Dr. Xindong Wu, and a BE from the Harbin Institute of Technology (China), in 2020 and 2013, respectively. His research focus lies broadly in data mining and machine learning and specifically in online learning, data stream analytics, graph learning, recommender systems, and explainable artificial intelligence. His research outcomes have appeared in premier venues, e.g., AAAI, IJCAI, WWW, ICDM, SDM, ACM MM, T.KDE, T.NNLS, among many other AI, machine learning, and data mining outlets.