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Archives: Events

12 events found.

IDS Guest Seminar Series

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  • May 2024
  • Wed 22
    Featured May 22, 2024 @ 10:00 am - 11:00 am HKT

    HKU IDS Guest Seminar: A Bottom-Up Approach towards Generalizable Robot Learning

    P307 IDS Office, Graduate House, HKU , Hong Kong

    Xingyu LIN, Postdoctoral Scholar, University of California, Berkeley

  • Fri 24
    Featured May 24, 2024 @ 10:00 am - 11:00 am HKT

    HKU IDS Guest Seminar: Leveraging Generative Models to Understand the Visual Cortex

    P307 IDS Office, Graduate House, HKU , Hong Kong

    Prof. Andrew Luo, Ph.D. candidate, Carnegie Mellon University

  • September 2024
  • Mon 2
    Featured September 2, 2024 @ 10:30 am - 11:30 am HKT

    HKU IDS Guest Seminar: Discovering components, mechanism, and structure from data

    IDS Seminar Room, P603, Graduate House

    Dr Eddie Lee, Postdoc Researcher, Complexity Science Hub

  • Tue 3
    Featured September 3, 2024 @ 5:00 pm - 6:00 pm HKT

    HKU IDS Guest Seminar: Brain-network-inspired computing for next generation efficient and sustainable AI

    IDS Seminar Room, P603, Graduate House

    Professor Carlo Vittorio Cannistraci, Professor, Tsinghua Laboratory of Brain and Intelligence (THBI); Adjunct Professor, Department of Computer Science & School of Biomedical Engineering, Tsinghua University

  • December 2024
  • Mon 16
    Featured December 16, 2024 @ 2:00 pm - 3:00 pm HKT

    HKU IDS Guest Seminar: Neuro-Symbolic Static Analysis for Reliable Software Systems

    IDS Seminar Room, P603, Graduate House

    Speaker Dr. Chengpeng Wang Post-Doctoral Research Associate, Purdue University

  • February 2025
  • Wed 12
    Featured February 12, 2025 @ 10:00 am - 11:00 am HKT

    HKU IDS Guest Seminar: Towards Agile, Intelligent and Safe Legged Robots

    Zoom , Hong Kong

    Speaker Mr. Zhongyu Li Ph.D. candidate @ University of California, Berkeley

  • March 2025
  • Tue 11
    Featured March 11, 2025 @ 4:30 pm - 5:30 pm HKT

    HKU IDS Guest Seminar: Integrative Analysis and Statistical Inference for High-Dimensional and Heterogeneous Outcomes

    IDS Innovation Hub P302 , Hong Kong

    Speaker Mr. Jin-Hong Du Ph.D. candidate @ Carnegie Mellon University

  • Thu 27
    Featured March 27, 2025 @ 10:00 am - 11:00 am HKT

    HKU IDS Guest Seminar: Integrative Analysis and Statistical Inference for High-Dimensional and Heterogeneous Outcomes

    P307 IDS Office, Graduate House, HKU , Hong Kong

    Speaker Mr. Zu-ming JIANG Ph.D. candidate @ ETH Zurich

  • April 2025
  • Wed 9
    Featured April 9, 2025 @ 10:30 am - 11:30 am HKT

    HKU IDS Guest Seminar: A generalization of the de Finetti—Hewitt—Savage Theorem on exchangeable processes

    IDS Seminar Room, P603, Graduate House

    Speaker Professor David Schrittesser Professor, Harbin Institute of Technology

  • May 2025
  • Wed 14
    Featured May 14, 2025 @ 9:30 am - 10:30 am HKT

    HKU IDS Guest Seminar: Pathology and Integrative Genomics for Precision Medicine Research

    IDS Seminar Room, P603, Graduate House

    Speaker Prof Kun HUANG Chair, Department of Biostatistics & Health Data Science Chair, Department of Biostatistics, School of Public Health Precision Health Initiative Professor of Genomics Data Sciences Professor of Biostatistics & Health Data Science Professor of Medicine School of Medicine, Indiana University

  • Tue 20
    Featured May 20, 2025 @ 11:00 am - 12:00 pm HKT

    HKU IDS Guest Seminar: Perception Encoder Meets PerceptionLM: General Visual Understanding from Hidden Features to Open Data

    IDS Seminar Room, P603, Graduate House

    Speaker: Dr Andrea Madotto Research Scientist, Fundamental AI Research, Meta

  • June 2025
  • Tue 10
    Featured June 10, 2025 @ 2:00 pm - 3:00 pm HKT

    HKU IDS Guest Seminar: Multi-Scale Sparse Conv Learning for Point Cloud Super-Resolving and Compression

    P307 IDS Office, Graduate House, HKU , Hong Kong

    Speaker Professor Zhu LI Professor, Computer Science & Electrical Engineering (CSEE), University of Missouri, Kansas City; Director of the NSF Center for Big Learning at UMKC

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