Junfeng Yang is Professor of Computer Science, Member of the Data Science Institute, and co-Director of the Software Systems Lab at Columbia University. Yang’s research centers on building reliable, secure, and fast software systems. Today’s software systems are large, complex, and plagued with errors, some of which have caused critical system failures, breaches, and performance degradation. Yang has invented techniques, algorithms, and tools to analyze, test, debug, monitor, and optimize real-world software, including Android, Linux, production systems at Microsoft, machine learning systems, and self-driving platforms, benefiting hundreds of millions of users. His research has resulted in numerous vulnerability patches to real-world systems, practical adoption at the largest technology companies, and press coverage at Scientific American, The Atlantic, The Register, Communications of ACM, and other news outlets. Yang received BS in Computer Science from Tsinghua University and MS and PhD in Computer Science from Stanford University. He won the Sloan Research Fellowship and the Air Force Office of Scientific Research Young Investigator Program Award, both in 2012; the National Science Foundation CAREER award in 2011; the inaugural Rock Star Award of the Association of Chinese Scholars in Computing in 2019; and Best Paper Awards at the USENIX Symposium on Operating System Design and Implementation in 2004, the ACM Symposium on Operating Systems Principles in 2017, and the USENIX Annual Technical Conference in 2021.
报告题目：Debugging Performance Issues in Modern Desktop Applications
摘要： Modern desktop applications involve many asynchronous, concurrent interactions that make performance issues difficult to diagnose. Although prior work has used causal tracing for debugging performance issues in distributed systems, we find that these techniques suffer from high inaccuracies for desktop applications. In this talk, I will present Argus, a fast, effective causal tracing tool for debugging performance anomalies in desktop applications. Argus introduces a novel notion of strong and weak edges to explicitly model and annotate trace graph ambiguities, a new beam-search-based diagnosis algorithm to select the most likely causal paths in the presence of ambiguities, and a new way to compare causal paths across normal and abnormal executions. We have implemented Argus across multiple versions of macOS and evaluated it on 12 infamous spinning pinwheel issues in popular macOS applications. Argus diagnosed the root causes for all issues, 10 of which were previously unknown, some of which have been open for several years. This work won a Best Paper award in USENIX ATC 2021. It is joint with Lingmei Weng (lead PhD student, graduating next academic year), Ryan Peng Huang, and Jason Nieh.
谭光明，研究员、博导、中科院计算技术研究所高性能计算机研究中心主任。国家杰出青年基金获得者，参与了曙光系列高性能计算机包括曙光4000/5000/6000/7000系统研制。发表学术论文100余篇，包括CCF A类论文（TC、SC、PPoPP）和Nature子刊等，曾任IEEE TPDS编委和国际会议（SC、PPoPP）等程序委员。曾获得国家科技进步奖二等奖、卢嘉锡青年人才奖和全国向上向善好青年称号。
Lili Qiu is an Assistant Managing Director at Microsoft Research Asia and a Professor at Computer Science Dept. in UT Austin. She got M.S. and PhD degrees in Computer Science from Cornell University in 1999 and 2001, respectively. After graduation, she spent 2001-2004 as a researcher at System & Networking Group in Microsoft Research Redmond. She joined UT Austin in 2005, and has founded a vibrant research group working on Internet and wireless networks at UT. She is an ACM Fellow and IEEE Fellow. She also got an NSF CAREER award and Google Faculty Research Award, and best paper awards at ACM MobiSys'18 and IEEE ICNP'17. She advised a PhD dissertation that won SIGMOBILE best dissertation award in 2020.
报告题目：Acoustic Sensing and Applications
摘要： Video games, Virtual Reality (VR), Augmented Reality (AR), and Smart appliances (e.g., smart TVs and drones) all call for a new way for users to interact and control them. Motivated by this observation, we have developed a series of novel acoustic sensing technologies by transmitting specifically designed signals or using signals naturally arising from the environments. We further develop a few interesting applications on top of our motion tracking technology such as a follow-me drone and acoustic imaging on mobile phones.
Shan Lu is a Professor in the Department of Computer Science at the University of Chicago. Her research focuses on detecting, diagnosing, and fixing functional and performance bugs in software systems. Shan is an ACM Distinguished Member (2019 class) and an Alfred P. Sloan Research Fellow (2014). Her co-authored papers have won distinguished paper and influential paper awards at ASPLOS, SOSP, OSDI, FAST, ICSE, FSE, CHI, and PLDI. Shan currently serves as the Chair of ACM-SIGOPS, and the Vice Chair of ACM SIG Governing Board Executive Committee. She served as the technical program co-chair for ASPLOS 2022, OSDI 2020, APSys 2018, and USENIX ATC 2015
报告题目： 15 Years of Learning from Mistakes in Building System Software
摘要： Bugs severely threaten the correctness and efficiency of software. With our system software growing its complexity, bugs in system software also evolve, imposing different challenges over the years. In this talk, we look back at our study of concurrency bugs in multi-threaded software, which was done 15 years ago and recently won ASPLOS Influential Paper Award, as well as various bug studies that we conducted over the years about distributed systems, industry cloud systems, database systems, machine learning systems, etc. We discuss the lessons that we have learned, as well as the new challenges faced by today's system building.