Dr. Gim Hee Lee is currently an Associate Professor in the Department of Computer Science at the National University of Singapore (NUS), where he leads the Computer Vision and Robotic Perception Laboratory. Prior to joining NUS, he was a researcher at Mitsubishi Electric Research Laboratories (MERL), USA. He obtained his PhD in Computer Science from ETH Zurich. He has served or will serve as an Area Chair for major computer vision and machine learning conferences such as CVPR, ICCV, ECCV, ICLR, NeurIPS, etc. He was part of the organizing committee as the Program Chair for 3DV 2022 and Demo Chair for CVPR 2023, and he is organizing 3DV 2025 in Singapore as the General Chair. He is a recipient the Singapore NRF Investigatorship, Class of 2024. His research interests include 3D computer vision, machine learning and robotics.

Youngsok Kim is currently an associate professor of the Department of Computer Science and Engineering at Yonsei University. His research interests span computer architecture and system software with an emphasis on architecture-conscious database management systems and processor performance modeling. He received his BSc and PhD in Computer Science and Engineering from POSTECH. He was a post-doc researcher at Seoul National University before joining Yonsei University. During his PhD studies, he was an intern at Consumer Hardware, Google Inc. and S.LSI Business, Samsung Electronics.

열린 튜링 강연회는 전산학 분야 최고의 권위를 지닌 튜링상 수상자들의 연구 업적을 널리 알리고자 마련된 자리로, 일반인도 이해할 수 있도록 쉽고 친절하게 해설하는 공개 강연입니다.

올해 강연에서는 2024년 수상 주제인 강화학습(Reinforcement Learning) 을 중심으로, 다음과 같은 내용이 소개됩니다:

· 2024년 튜링상(강화학습) 해설: 옥정슬ST 전산학부는 서울대학교 컴퓨터공학부, 포항공과대학교 컴퓨터공학과와 함께 "제3회 열린 튜링 강연회(Open Turing Lecture)"를 개최합니다.

열린 튜링 강연회는 전산학 분야 최고의 권위를 지닌 튜링상 수상자들의 연구 업적을 널리 알리고자 마련된 자리로, 일반인도 이해할 수 있도록 쉽고 친절하게 해설하는 공개 강연입니다.

올해 강연에서는 2024년 수상 주제인 강화학습(Reinforcement Learning) 을 중심으로, 다음과 같은 내용이 소개됩니다:

· 2024년 튜링상(강화학습) 해설: 옥정슬 교수 (POSTECH)

· 2017년 튜링상(컴퓨터구조) 해설: 허재혁 교수 (KAIST)

· 2004년 튜링상(인터넷) 해설: 박경수 교수 (서울대학교)

전산학의 과거 업적을 되새기고, 현재의 의미를 돌아보며, 미래를 함께 조망할 수 있는 뜻깊은 시간이 될 것입니다.

행사는 POSTECH에서 진행되며 유튜브 생중계로도 참여하실 수 있으니 많은 관심과 시청 바랍니다.

행사 개요

· 행사명: 제3회 열린 튜링 강연회 (Open Turing Lecture)

· 일시: 2025년 5월 15일(목) 16:00 ~ 17:30

· 장소: POSTECH 포스코 국제관 1층 국제회의장

· 유튜브 생중계: https://www.youtube.com/live/RNIKN2YUTj4

· 공식 홈페이지: https://open-turing.github.io

※ 공식 홈페이지를 통해 지난 강연 영상도 다시 시청하실 수 있습니다.

많은 참여와 관심 부탁드립니다.

Bio:

1996년 9월 - 1998년 2월 미국 하버드 대학 엔칭연구소 Visiting Scholar
1998년 3월 - 현재 서강대학교 철학과 교수
2004년 3월 - 2004년 2월 캐나다 University of Toronto, Department East asian Studies, Visiting Scholar

Dr. DK Lee is the Vice President of the AI DC Lab at SK Telecom’s SK AI R&D Center. He leads the development of full-stack solutions for AI datacenter business initiatives, focusing on predictive management of AI DC power, cooling, and space facilities; virtualization of GPUs, NPUs, and xPUs; efficient AI job scheduling on AI Cloud resources; implementation of AIOps; AI Cloud FinOps (financial operations) management; and the modernization and acceleration of AI applications on hybrid cloud computing platforms. Dr. Lee earned his Ph.D. in Computer Science from the Korea Advanced Institute of Science and Technology (KAIST) in 2011. His research focused on future Internet architecture, Internet data measurement, and the design of large-scale networked systems. Leveraging his research background, he joined SK Telecom in 2011 and has since dedicated his career to advancing mobile network technologies.

한선화 박사는 KAIST 전산학과에서 석사(1989) 및 박사(1997) 학위를 취득했다. 1997년부터 한국과학기술정보연구원(KISTI)에서 근무하며 원장(2014~2017)을 비롯해 지식정보센터장, 정보기술개발단장, 정책연구실장, 선임연구부장, 첨단정보연구소장 등 주요 직책을 역임했다. 2023년부터 ㈜페블러스 데이터커뮤니케이터에서 활동하고 있다. 또한, 국가과학기술연구회 정책본부장(2018~2020), 공공데이터전략위원회 민간위원(2018~2021), 국가과학기술자문회의 자문위원(2013~2014), 국가과학기술심의회 심의위원(2011~2015) 등 국가 과학기술 정책 수립 및 연구 발전에 기여해왔다. 과학 커뮤니케이터로서도 활발히 활동하며 ＊KTV 과학톡＊ (2018~2020) 진행을 맡았고, TJB ＊생방송투데이＊ (2020~), ＊곽마더＊ (2022), ＊미래설계소＊ (2023) 등에 출연했다.

Dr. Youngwoo Seo is a field-roboticist of building mobile robots including self-driving cars, drones, a high-speed transport – hyperloop, unmanned ground vehicles, etc. for more than two decades, and a seasoned executive with experience of managing diverse teams to deliver what matters. He currently serves as an Executive Vice President at the Land Systems Business Group, Hanwha Aerospace, where he oversees R&D efforts, among other responsibilities, for developing robotics and autonomous systems. Prior to joining Hanwha, Dr. Seo ran Atlas Robotics, Inc. to deliver a technology stack for autonomous mobility by shared autonomy, and led a team of engineers to develop perception stacks and mission-critical systems for Hyperloop One, to develop an autonomous flight stack for Autel Robotics, to deliver parts of the next-generation product at the Special Project Group of Apple, Inc., to deliver public demonstration of autonomous driving with GM-CMU Autonomous Driving Collaborative Research Lab. During his doctoral study, he was a member of the Tartan Racing team, the winning entry of the 2007 DARPA Urban Challenge, and worked on developing computational ways of augmenting cartographic resources and of assessing roadway status for reliable autonomous driving. While working as a research staff at the Robotics Institute of Carnegie Mellon University, he developed many machine learning algorithms and multi-agents systems to solve real-world problems. He earned a Ph.D. and a master’s degree in robotics from Carnegie Mellon University, and a master’s degree in computer science from Seoul National University.

Jun Han is an Associate Professor at KAIST of Computer Science, School of Computing at KAIST. He founded and directs the Cyber-Physical Systems and Security (CyPhy) Lab at KAIST. Prior to joining KAIST, he was at the National University of Singapore with an appointment in the Department of Computer Science, School of Computing, and Yonsei University with an appointment in the School of Electrical and Electronic Engineering. His research interest lies at the intersection of security and mobile/sensing systems and focuses on utilizing contextual information to solve security problems in the Internet-of-Things and Cyber-Physical Systems. He publishes at top-tier venues across various research communities spanning mobile computing, sensing systems, and security (including IEEE S&P, USENIX Security, ACM CCS, MobiSys, MobiCom, SenSys, Ubicomp, IPSN).

Abstract:
Modern planet-scale online services require high-performance computing infrastructures, but the end of Dennard scaling and excessive coordination overhead make it challenging to augment computing resources efficiently. In this talk, I will introduce the concept of network programmability that provides new opportunities to transform the network into a computation-facilitating infrastructure. To show its potential impacts on the performance of computing systems, I will present examples of switch-based in-network acceleration, including in-network caching and in-network request cloning. Finally, I will briefly discuss the future directions of in-network acceleration, which include next-generation SmartNICs and eBPF/XDP.

Bio:
Gyuyeong Kim is an Assistant Professor in the Department of Computer Engineering at Sungshin Women's University. He received his Ph.D. and B.S. in Computer Science from Korea University in 2020 and 2012, respectively. Before joining Sungshin Women's University, he was a Research Professor at Korea University. He works on broad topics in computer networking and systems. During undergraduate, he developed KLUE, a lecture evaluation service for Korea University.

o Location: Offline (Room 201, N1 Building)

o Speaker : Yizheng Chen(University of Maryland)

■ Title: Benchmarking LLMs for Secure Code Generation

■ Abstract

Models (LLMs) have demonstrated promising capabilities in discovering and patching real-world security vulnerabilities. But how do we determine which LLM-based system performs best?

In this talk, I will explore the challenges of benchmarking LLMs for cyberdefense.

I will begin by presenting our work on evaluating LLMs’ ability to generate secure code. Notably, we find that results from prior code-generation benchmarks do not translate to LLMs’ secure coding performance in real-world software projects.

Next, I will discuss a key issue: memorization. LLMs may not be solving security problems from first principles but rather recalling secure solutions they have already seen. Finally, I will discuss future research directions in effectively evaluating

and improving LLMs for cybersecurity applications.

Models (LLM

■ Bio

Yizheng Chen is an Assistant Professor of Computer Science at the University of Maryland. Her research focuses on Large Language Models for Code Generation and AI for Security.

Her recent work PrimeVul has been used by Gemini 1.5 Pro for vulnerability detection evaluation. Previously, she received her Ph.D. in Computer Science from the Georgia Institute of Technology,

and was a postdoc at University of California, Berkeley and Columbia University. Her work has received an ACM CCS Best Paper Award Runner-up, a Google ASPIRE Award, and Top 10 Finalist of the CSAW Applied Research Competition.

She is a recipient of the Anita Borg Memorial Scholarship.