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.

[Main research seminar] Professor Mark S. Ackerman from the University of Michigan, School of Information will give an in-person talk on sociotechnical approaches to computing. Specifically, he will be reflecting on his several decades of research helping pioneer the sociotechnical study and design of making computing systems useful for humans and organizations, drawing lessons for future research.
Date and time: February 7 (Friday) 10:30 AM - 12:00 PM (KST)
Location: N22 1st floor Room 103 (이민화홀)
Language: English
Host: Prof. Tom Steinberger

The memory wall has long been recognized as a critical challenge in high-performance systems, and it has recently become even more significant due to the exponential growth of machine learning model sizes. Meanwhile, recent advancements in interconnect technology, such as Compute Express Link (CXL), enable scalable memory system designs to address the memory capacity wall. Moreover, by offloading data and computation to CXL memory expanders to realize Near-Data Processing (NDP), the memory bandwidth wall can also be effectively mitigated. However, designing such a system should be done carefully, considering various design aspects that can affect the practicality of the solution.
In this talk, I will discuss key considerations and directions for building a practical NDP system architecture, including general-purpose computing, low-latency host communication, standard compliance, and cost-effectiveness. I will then present our recent work on an NDP architecture called Memory-Mapped NDP (M²NDP). M²NDP consists of two components: 1) Memory-Mapped Function (M²func), which enables low-latency host-device communication by addressing the overhead of conventional ring buffer-based task offloading, and 2) Memory-Mapped μthreading (M²μthread), a general-purpose, cost-effective NDP unit architecture that aims to maximize resource utilization by hybridizing CPU and GPU architectures. Finally, I will briefly outline future research directions based on the M²NDP architecture.

Gwangsun Kim is an Assistant Professor in the Department of Computer Science and Engineering at POSTECH. Previously, he was a Senior Research Engineer and Senior Performance Engineer at Arm Inc. He received the B.S. degrees in Electronic and Electrical Engineering and Computer Science and Engineering from POSTECH in 2010, and the M.S. and Ph.D. degrees in Computer Science from KAIST in 2012 and 2016, respectively. He has worked on various areas of computer architecture and systems, including memory systems, parallel architectures, GPU computing, systems for machine learning, near-data processing, networking, deep learning compiler, and simulation methodology. He is particularly interested in designing practical architectures for high-performance and scalable systems.

Despite significant progress in verifying protocols, services that implement distributed protocols , e.g., Chubby or Etcd, can exhibit safety bugs in production deployments. These bugs are often introduced by programmers when converting protocol descriptions into code. In this talk I will describe a new technique we have been developing to identify these bugs at runtime: Runtime Protocol Refinement Checking} (RPRC). RPRC systems observe a deployed service's runtime behavior and notify operators when this behavior evidences a protocol implementation bug, allowing operators to mitigate the bugs impact and developers to fix the bug. We have developed an algorithm for RPRC and implemented it in a system called Ellsberg that targets services that assume the asynchronous or partially synchronous model, and fail-stop failures. We designed Ellsberg so it makes no assumptions about how services are implemented, and requires no additional coordination or communication. We have used Ellsberg with three open source services: Etcd, Zookeeper and Redis Raft.

Aurojit Panda is an assistant professor in the Computer Science department at New York University working on systems and networking. He received his PhD in 2017 from UC Berkeley, where he was advised by Scott Shenker. He has received several awards, including a VMware Early Career Faculty Award, a Google Research Scholar Award, an NSF Career award, best paper awards at EuroSys, SIGCOMM and OSDI, and a EuroSys test of time award.