檢送113年05月29日(星期三) 數據科學系列演講資訊如下，歡迎蒞臨聽講！

時間：2024.05.29(Wed.) 15:30-17:20

地點：工程三館EC115教室

講者姓名 職稱/任職單位：李濬屹教授 清華大學資工系 

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Challenges of Digital Twin Learning for Deep Learning Based Intelligent Robotics
Abstract
Collecting data on a large scale is vital for the development of cutting-edge artificial intelligence (AI) technologies, especially those involving machine learning (ML) models, such as deep neural networks, which require training with relevant data. On one hand, the collection of real-world data, using devices such as cameras and microphones, would enable AI systems to better understand everyday life and ultimately behave or assist in a manner akin to human interaction. On the other hand, growing concerns about security and privacy make it increasingly difficult to collect such real-world data. As a result, the emergence of digital twins offers a promising direction for intelligent robots that employ deep learning models for tasks such as perception, planning, localization, and control.
Biography
Chun-Yi Lee is a Professor of Computer Science at National Tsing Hua University (NTHU), Hsinchu, Taiwan
and the supervisor of Elsa Lab. He received his B.S. and M.S. degrees from National Taiwan University,
Taipei, Taiwan, in 2003 and 2005, respectively, and the M.A. and Ph.D. degrees from Princeton University,
Princeton, NJ, USA, in 2009 and 2013, respectively, all in Electrical Engineering. Prof. Lee joined the
Department of Computer Science at NTHU as an Assistant Professor in 2015. He was promoted to Associate
Professor in 2019 and to full Professor in 2023. Before his tenure at NTHU, he was a senior engineer at
Oracle America, Inc. in Santa Clara, CA, USA, from 2012 to 2015. Prof. Lee founded Elsa Lab at National Tsing
Hua University in 2015. Under his leadership, Elsa Lab has garnered several prestigious awards from global
robotics and AI challenges. These include the first place at the NVIDIA Embedded Intelligent Robotics
Challenge in 2016, first place at the NVIDIA Jetson Robotics Challenge in 2018, second place in the Person-
In-Context (PIC) Challenge at ECCV 2018, second place in the NVIDIA AI at the Edge Challenge in 2020, and
the Best Solution Award (1st Place) in the Small Object Detection Challenge for Spotting Birds at MVA 2023.