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UID:0-7309@eng.ufl.edu
DTSTART;TZID=America/New_York:20250217T120000
DTEND;TZID=America/New_York:20250217T130000
DTSTAMP:20251201T210652Z
URL:https://www.eng.ufl.edu/news-events/events/cise-faculty-seminar-dr-jas
 on-hong/
SUMMARY:CISE Faculty Seminar: Dr. Jason Hong
DESCRIPTION:Zoom Link: https://ufl.zoom.us/j/95193220709\nBiography: Junyua
 n Hong is a postdoctoral fellow at UT Austin Institute for Foundations of 
 Machine Learning (IFML) and the Wireless Networking and Communications Gro
 up (WNCG). His research focuses on advancing Responsible AI for Healthcare
 . His recent work addressed pressing challenges in Responsible AI\, such a
 s data privacy\, fairness\, and security. In 2024\, he was recognized as a
 n ML Commons Rising Star and a finalist for the VLDB Best Paper Award. Add
 itionally\, his work on safeguarding data privacy in financial analysis wo
 n the third-place finish in the U.S. PETs (Privacy-Enhancing Technologies)
  Prize Challenge and was highlighted by the White House and MSU Research &
 amp\; Innovation Office in 2023. Beyond research\, he actively served as l
 ead 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.\nTitle of the 
 Talk: Harmonizing\, Understanding\, and Deploying Responsible AI\nAbstract
 : Artificial Intelligence (AI) has demonstrated remarkable\npotential for 
 tackling grand challenges in human society. Yet\,\nbuilding an integrative
  Responsible AI system that is comprehensively aligned with multifaceted h
 uman values— rather than a single one—remains a major challenge in\nea
 rning people’s trust\, particularly in high-stakes domains like\nhealthc
 are. To address the challenge\, my vision is to harmonize\, understand\, a
 nd deploy Responsible AI: optimizing AI systems that balance real-world co
 nstraints in computational accessibility\, data privacy\, security\, and e
 thical\nnorms through use-inspired threat analysis and integrative ethical
  learning algorithms. Pursuing this vision\, I developed privacy-preservin
 g algorithms harmonized with high\naccessibility to edge devices\, fairnes
 s to individuals\, and security of ML systems. My work also systematically
  analyzed the multifaceted trust risks associated with model compression a
 nd fine-tuning toward edge and personalized\nuse cases. Additionally\, I e
 xplored Responsible AI techniques for in-home dementia prevention and diag
 nosis\, expanding the time and space boundaries of dementia healthcare for
 \nsocially isolated older adults. My work lays the foundation for Responsi
 ble AI algorithm\, evaluation\, and deployment\, paving\nthe future path t
 oward reliable\, verifiable\, and effective AI in healthcare and beyond.
CATEGORIES:Seminars
LOCATION:Malachowsky Hall 5210\, 1889 Museum Road\, Gainesville\, Florida\,
  32611\, United States
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=1889 Museum Road\, Gainesvi
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