Bang Liu is an Assistant Professor in the Department of Computer Science and Operations Research (DIRO) at the University of Montreal. He is also a member of Mila – Quebec Artificial Intelligence Institute and a Canada CIFAR AI (CCAI) Chair. He received his B.Engr. degree in 2013 from the University of Science and Technology of China (USTC), as well as his M.S. degree and Ph.D. degree from the University of Alberta in 2015 and 2020, respectively. He researches natural language processing and understanding, text mining, multimodal & embodied learning, and AI applications in different fields (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 over 60 papers in top-tier conferences and journals such as KDD, The Web Conference, ICDM, CIKM, NeurIPS, ICLR, AAAI, ACL, EMNLP, NAACL, 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 EACL, ACL, 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 AAAI 2024, 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 and AIGC tutorials at renowned conferences such as NAACL’21, SIGIR’21, KDD’21, IJCAI’21, AAAI’22, TheWebConf’22, and AAAI'24. 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.

Manling Li is an assistant professor at the Computer Science department of Northwestern University (full-time starting in Fall 2024) and a postdoc at Stanford University. She obtained Ph.D. degree in Computer Science at University of Illinois Urbana-Champaign in 2023. At the core of her research in Knowledge Foundation Models, she aim to equip machines with factual knowledge extraction and reasoning from multimodal data (Language + X, where X can be images, videos, robotics, audio, etc). Her work on multimodal knowledge extraction won the ACL'20 Best Demo Paper Award, and the work on scientific information extraction from COVID literature won NAACL'21 Best Demo Paper Award. She was a recipient of Microsoft Research PhD Fellowship in 2021. She was selected as a DARPA Riser in 2022, and a EE CS Rising Star in 2022. She was awarded C.L. Dave and Jane W.S. Liu Award, and has been selected as a Mavis Future Faculty Fellow. She has more than 40 publications on multimodal knowledge extraction and reasoning, and gave tutorials about event-centric multimodal knowledge at AAAI'21, ACL'21, NAACL'22, CVPR'23, AAAI'23, etc. She organized the ACL'23 Knowledgeable Language Models workshop, ACL'23 Language and Molecular workshop, and co-organized the SIGIR'23 KDF (Knowledge Discovery from Unstructured Data in Financial Services) Workshop. She serves as area chairs in ACL and EMNLP, senior PC members at IJCAI and PC members for AAAI, ARR, ACL, EMNLP, NAACL, COLING, etc. 

Heng Ji is a professor at Computer Science Department, and an affiliated faculty member at Electrical and Computer Engineering Department and Coordinated Science Laboratory of University of Illinois Urbana-Champaign. She is an Amazon Scholar. She is the Founding Director of Amazon-Illinois Center on AI for Interactive Conversational Experiences (AICE). She received her B.A. and M. A. in Computational Linguistics from Tsinghua University, and her M.S. and Ph.D. in Computer Science from New York University. Her research interests focus on Natural Language Processing, especially on Multimedia Multilingual Information Extraction, Knowledge-enhanced Large Language Models, Knowledge-driven Generation and Conversational AI. She was selected as a Young Scientist to attend the 6th World Laureates Association Forum, and selected to participate in DARPA AI Forward in 2023. She was selected as "Young Scientist" and a member of the Global Future Council on the Future of Computing by the World Economic Forum in 2016 and 2017. She was named as part of Women Leaders of Conversational AI (Class of 2023) by Project Voice. The awards she received include "AI's 10 to Watch" Award by IEEE Intelligent Systems in 2013, NSF CAREER award in 2009, PACLIC2012 Best paper runner-up, "Best of ICDM2013" paper award, "Best of SDM2013" paper award, ACL2018 Best Demo paper nomination, ACL2020 Best Demo Paper Award, NAACL2021 Best Demo Paper Award, Google Research Award in 2009 and 2014, IBM Watson Faculty Award in 2012 and 2014 and Bosch Research Award in 2014-2018. She was invited to testify to the U.S. House Cybersecurity, Data Analytics, & IT Committee as an AI expert in 2023. She was invited by the Secretary of the U.S. Air Force and AFRL to join Air Force Data Analytics Expert Panel to inform the Air Force Strategy 2030, and invited to speak at the Federal Information Integrity R&D Interagency Working Group (IIRD IWG) briefing in 2023. She is the lead of many multi-institution projects and tasks, including the U.S. ARL projects on information fusion and knowledge networks construction, DARPA ECOLE MIRACLE team, DARPA KAIROS RESIN team and DARPA DEFT Tinker Bell team. She has coordinated the NIST TAC Knowledge Base Population task since 2010. She was the associate editor for IEEE/ACM Transaction on Audio, Speech, and Language Processing, and served as the Program Committee Co-Chair of many conferences including NAACL-HLT2018 and AACL-IJCNLP2022. She is elected as the North American Chapter of the Association for Computational Linguistics (NAACL) secretary 2020-2023. Her research has been widely supported by the U.S. government agencies (DARPA, NSF, DoE, ARL, IARPA, AFRL, DHS) and industry (Amazon, Google, Facebook, Bosch, IBM, Disney).

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.