Upcoming Events
CISE Faculty Seminar: Dr. Jason Hong
02/17/2025, 12:00 pm - 1:00 pm Malachowsky Hall 5210, Gainesville, FL Zoom Link: https://ufl.zoom.us/j/95193220709 Biography: Junyuan Hong is a postdoctoral fellow at UT Austin Institute for Foundations of Machine Learning (IFML) and the Wireless Networking and Communications Group (WNCG). His research focuses on advancing Responsible AI for Healthcare. His recent work addressed pressing challenges in Responsible AI, such as data privacy, fairness, and security. In 2024, he was recognized as an ML Commons Rising Star and a finalist for the VLDB Best Paper Award. Additionally, his work on safeguarding data privacy in financial analysis won the third-place finish in the U.S. PETs (Privacy-Enhancing Technologies) Prize Challenge and was highlighted by the White House and MSU Research & Innovation Office in 2023. Beyond research, he actively served as lead chair organizer for Federated-Learning and Gen AI-for-Health workshops at top-tier data mining and machine learning conferences (KDD and NeurIPS), and a mentor in the Responsible AI for Ukraine program. Title of the Talk: Harmonizing, Understanding, and Deploying Responsible AI Abstract: Artificial Intelligence (AI) has demonstrated remarkable |
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Colloquium Talk Series: Dr. Heng Ji
03/13/2025, 12:00 pm - 1:00 pm Malachowsky Hall 5210, Gainesville, FL Biography: Heng Ji is a Tenured Full Professor and Associate Head of Research at Siebel School of Computing and Data Science, and an affiliated faculty member at Electrical and Computer Engineering Department, Coordinated Science Laboratory, and Carl R. Woese Institute for Genomic Biology 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 and Vision-Language Models, and AI for Science. The awards she received include Outstanding Paper Award at ACL2024, two Outstanding Paper Awards at NAACL2024, “Young Scientist” by the World Laureates Association in 2023 and 2024, “Young Scientist” and a member of the Global Future Council on the Future of Computing by the World Economic Forum in 2016 and 2017, “Women Leaders of Conversational AI” (Class of 2023) by Project Voice, “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 selected to participate in DARPA AI Forward 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 2010-2020. She served as the associate editor for IEEE/ACM Transaction on Audio, Speech, and Language Processing, and the Program Committee Co-Chair of many conferences including NAACL-HLT2018 and AACL-IJCNLP2022. She was 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, Bosch, IBM, Disney). Title of the Talk: Towards Knowledgeable Foundation Models Abstract: Large language models (LLMs) and vision-language models (VLMs) have demonstrated remarkable performance on knowledge reasoning tasks, owing to their implicit knowledge derived from extensive pretraining data. However, their inherent knowledge bases often suffer from disorganization and illusion, bias towards common entities, and rapid obsolescence. Consequently, LLMs frequently make up untruthful information, exhibit resistance to updating outdated knowledge, or struggle with generalizing across multiple languages. In this talk I will discuss several research directions that aim to make foundation models’ knowledge more accurate, organized, up-to-date and fair: (1) Where and How is Knowledge Stored in LLM? (2)How to Control LLM’s Knowledge? (3)How to Acquire and Update LLM’s DynamicKnowledge? (4) How to Enable LLM’s System2 Thinking, Critical Thinking and CreativeIntelligence? (5) How to Bridge theKnowledge Gap between Natural Languageand Unnatural Language? I will also show the promising results on two very different real-world applications – complex situation report and forecasting, as well as drug and material discovery. |