David Leake is Executive Associate Dean and Professor of Computer Science in the School of Informatics and Computing - Bloomington, at Indiana University. He is also an associate of the Indiana University Data to Insight Center and member of the faculty of the university's Cognitive Science and Human-Computer Interaction programs. He received his Ph.D. in Computer Science from Yale University in 1990. His research interests include case-based reasoning, context, explanation, human-centered computing, intelligent user interfaces, introspective reasoning, and knowledge capture and management. He has authored/edited over 175 publications in these areas, including winners of outstanding paper awards from the International Conference on Case-Based Reasoning. He has chaired the International Conference on Case-Based Reasoning and International Conference on Intelligent User Interfaces. He is Editor Emeritus of AI Magazine, the official magazine of the Association for the Advancement of Artificial Intelligence (AAAI), for which he was Editor in Chief for 17 years. He is a recipient of the AAAI Distinguished Service Award. He is a three-time recipient of the Indiana University Trustees' Teaching Award. His home page is http://www.cs.indiana.edu/~leake.

Shreyansh is a Machine Learning Scientist at Amazon.com. He works on developing domain knowledge driven machine learning solutions to facilitate decision making. Amazon sort center process millions of packages every day. In this talk, I will discuss how domain knowledge driven machine learning assist Amazon sort center site-leaders to make informed decision in order to meet millions of on time customer delivery promises.

Filip Ilievski is a Computer Scientist in the Center on Knowledge Graphs within the Information Sciences Institute (ISI) at the USC Viterbi School of Engineering. He holds a Ph.D. in Natural Language Processing from the Vrije Universiteit (VU) in Amsterdam. His principal research interest concerns the role of background knowledge for filling gaps in human communication. He is leading the development of the Commonsense Knowledge Graph (CSKG), a resource which consolidates existing sources into a harmonized representation. CSKG allows us to enhance language models for zero-shot Question Answering with more knowledge, precisely select relevant subsets, or perform explainable inference. He has been heavily involved in the development of the Knowledge Graph ToolKit (KGTK) - a comprehensive set of operations for working with modern, hyper-relational graphs like Wikidata. In the past year, he has worked together with around 10 Ph.D. and Master students, and collaborated with researchers at CMU, Bosch Research, RPI, etc. He has been publishing this work in top-tier venues like AAAI, EMNLP, and ISWC. His home page is https://usc-isi-i2.github.io/ilievski/