Bang Liu is an Assistant Professor in the Department of Computer Science and Operations Research (DIRO) at the University of Montreal (UdeM). He is a member of the RALI laboratory (Applied Research in Computer Linguistics) of DIRO, the Institut Courtois of UdeM, Mila – Quebec Artificial Intelligence Institute, and holds a Canada CIFAR AI Chair. He received his B.Engr. degree in 2013 from University of Science and Technology of China (USTC), as well as his M.S. degree and Ph.D. degree from University of Alberta in 2015 and 2020, respectively. His research interests primarily lie in the areas of natural language processing, multimodal and embodied learning, theory and techniques for AGI, and AI for science (e.g., health, material science). Bang is keen to understand the essence of intelligence and develop intelligent techniques for accelerating scientific discovery. He has published 50+ papers in top-tier conferences and journals such as ACL, EMNLP, NAACL, NeurIPS, ICLR, AAAI, KDD, The Web Conference, ICDM, CIKM, CVPR, ACM Transactions on Knowledge Discovery from Data (TKDD), IEEE/ACM Transactions on Networking (TON), and ACM Transactions on the Web (TWEB). He received the Faculty of Arts and Science Medals for Research Excellence 2022 in the University of Montreal. He has served as an area chair for ACL, NAACL, EACL, and EMNLP. He is also a program committee member or reviewer for many conferences and journals, including KDD, ACL, The Web Conference, SIGIR, AAAI, NeurIPS, ICLR, TOIS, JAIR, TPAMI, TNNLS, PR, and so on. He has given several tutorials in WWW 2022, AAAI 2022, IJCAI 2021, SIGIR 2021, and KDD 2021. He has also co-organized several workshops in ICLR 2022 and NAACL 2022.

Dr. Yu (Hugo) Chen is a distinguished engineer and scientist known for his remarkable contributions in Artificial Intelligence. As the Co-founder and Head of Machine Learning at Anytime.AI, he has pioneered generative AI solutions for the legal domain. As a proud alumnus of Rensselaer Polytechnic Institute, he earned his PhD in Computer Science and has since established himself as an authority in the realms of Machine Learning (Deep Learning) and Natural Language Processing. His groundbreaking research has garnered attention and acclaim, with publications in esteemed conferences and journals like NeurIPS, ICML, ICLR, AAAI, IJCAI, ACL, EMNLP, NAACL, KDD, WSDM, TheWebConf, ISWC, and TNNLS. In recognition of his exceptional work, he received the Best Student Paper Award of AAAI DLGMA’20. Further extending his knowledge, Dr. Chen contributed to the pivotal book, "Graph Neural Networks: Foundations, Frontiers, and Applications". As a respected expert in his field, he has shared his expertise through DLG4NLP tutorials at renowned conferences such as NAACL'21, SIGIR'21, KDD'21, IJCAI'21, AAAI’22, and TheWebConf’22. Dr. Chen's pioneering work has not only left a mark in the academic world but also in the technology and marketing spheres, with mentions in prominent publications including the World Economic Forum, TechXplore, TechCrunch, Ad Age, and Adweek. As a testament to his innovative spirit, he holds co-inventorship for 4 US patents.

Xiaojie Guo currently serves as a Research Staff Member at IBM Thomas J. Watson Research Center. She got her Ph.D. degree from the department of Information Science and Technology at George Mason University. Her research topics include data mining, artificial intelligence, and machine learning, with special interests in deep learning on graphs, graph transformation and generation, and interpretable representation learning. She has published over 30 papers in top-tier conferences and journals such as KDD, ICDM, ICLR, NeurIPS, AAAI, CIKM, Knowledge and Information System (KAIS), IEEE Transactions on Neural Networks and Learning Systems (TNNLS) and KAIS. She won the Best Paper Award in ICDM 2019 and has one paper awarded as an ESI Hot and Highly Cited Paper as the first author. She also won the AAAI/IAAI 2022 Award. Xiaojie has also served as an independent peer reviewer for multiple top academic journals, such as the IEEE Transactions on Neural Networks and Learning Systems (TNNLS), IEEE Transactions on Knowledge Discovery from Data (TKDD), ICLR, and NeurIPS.

Dr. Lingfei Wu is Cofounder and CEO of Anytime.AI, a new generative AI startup where they empower lawyers with unparalleled effectiveness & efficiency while connecting them to top-tier clients. He earned his Ph.D. degree in computer science from the College of William and Mary in 2016. Previously, he was an engineering Leader in Content Understanding at Pinterest, leading and overseeing the content understanding team consisted of talented applied scientists and software engineers and product managers to leverage Large Language Models (LLMs) and Generative AI technologies for building various interest-based and engagement-based content understanding signals. Before that, he was a Principal Scientist at JD.COM Silicon Valley Research Center, leading a team of 30+ machine learning/natural language processing scientists and software engineers to build next generation Large Language Models (LLMs)-powered Ecommerce systems. He was a research staff member at IBM Thomas J. Watson Research Center and led a 10+ research scientist team for developing novel Graph Neural Networks methods and systems, which leads to three-time Outstanding Technical Achievement Award at IBM Research. He has published one book (in GNNs) and more than 100 top-ranked conference and journal papers, and is a co-inventor of more than 60 filed US patents. Because of the high commercial value of his patents, he received eight invention achievement awards and was appointed as IBM Master Inventors, class of 2020. He was the recipients of the Best Paper Award and Best Student Paper Award of several conferences such as IEEE ICC’19, AAAI workshop on DLGMA’20 and KDD workshop on DLG’19. His research has been featured in numerous media outlets, including NatureNews, YahooNews, AP News, PR Newswire, The Time Weekly, Venturebeat, MIT News, IBM Research News, and SIAM News. He has served as Industry and Government Program Co-Chairs of IEEE BigData'22, Sponsorship Co-Chairs of KDD'22 and Associate Conference Co-Chairs of AAAI'21 and is the founding co-chairs for several workshops such as Deep Learning on Graphs (with AAAI’20-22 and KDD’19-22). He has also served as Associate Editor for IEEE Transactions on Neural Networks and Learning Systems and ACM Transactions on Knowledge Discovery from Data.