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DATE：2026/4/23 (Thursday)
TIME：15：30
HOST：Dr. Chun-Chia Chen
VENUE：Dr. Poe‘s Lecture Hall
SPEAKER：Dr. Anatoli Fedynitch
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中研院原分所演講公告
日期：2026/4/22 (星期三)
時間：上午10時30分
主持人：江正天 教授
演講地點：原分所浦大邦紀念講堂
演講者: Jürgen Popp 博士
演講者單位：Leibniz Institute of Photonic Technology, Jena, Germany
演講題目：AI-Enhanced Raman Spectroscopy: Translating Photonic Innovations into Clinical Diagnostics and Therapy
Abstract:
The emergence of artificial intelligence is transforming how researchers interact with machines at a remarkable pace. Globally, research teams and entire communities are already reinventing their workflows to leverage the productivity gains that AI offers. The most impacted disciplines are those that rely on computational methods, supercomputing, and daily software development — all of which define my team's work, from data analysis and simulations to training machine learning algorithms. Beyond coding, AI proves remarkably powerful for literature research and interdisciplinary thinking, since by design it does not associate methods with narrow research fields. In this talk, I will report on the use cases we have adopted in our main research workflows and the lessons we have learned so far.
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日期：2026/4/23 (星期四)
時間：15時30分
主持人：陳俊嘉 博士
演講地點：原分所浦大邦紀念講堂
演講者: 安納托里 博士
演講者單位：中央研究院物理研究所
演講題目：AI Application in Research
Abstract:
The emergence of artificial intelligence is transforming how researchers interact with machines at a remarkable pace. Globally, research teams and entire communities are already reinventing their workflows to leverage the productivity gains that AI offers. The most impacted disciplines are those that rely on computational methods, supercomputing, and daily software development — all of which define my team's work, from data analysis and simulations to training machine learning algorithms. Beyond coding, AI proves remarkably powerful for literature research and interdisciplinary thinking, since by design it does not associate methods with narrow research fields. In this talk, I will report on the use cases we have adopted in our main research workflows and the lessons we have learned so far.
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