Speakers

Associate Professor Zhaoguo Wang

Shanghai Jiao Tong University,  China     

Zhaoguo Wang is currently a tenured Associate Professor at Shanghai Jiao Tong University, where he also serves as a doctoral supervisor and Vice Dean of the School of Software, as well as the principal investigator for key research and development projects. Previously, he held a position as an Assistant Professor at New York University, albeit non-tenure track. Dr. Wang’s research focuses primarily on databases and distributed systems, and he has made significant contributions to the field, with publications in renowned conferences such as OSDI, SIGMOD, VLDB, NSDI, PPoPP, and PODC. Dr. Wang has been honored with several prestigious awards, including the 2023 ACM SIGMOD Research Highlight Award, ACM SIGMOD 2022 Best Paper Honorable Mention, ACM APSys 2017 Best Paper Award, ACM ChinaSys Rising Star Award, Huawei OlympusMons Pioneer Award, and Huawei Spark Award (2 times) for his outstanding contributions. In addition to his academic achievements, Dr. Wang holds important leadership roles within the academic community. He serves as the Director of the Technical Steering Group (TSG) of the OpenHarmony Intelligent Data Management, Secretary General of ACM ChinaSys, and Executive Committee Member of the CCF Technical Committee on Database, Technical Committee on System Software, and High-Performance Computing Special Committees. Dr. Wang is actively engaged in the international academic community through his participation as a program committee member for prestigious conferences such as EuroSys 2025, NSDI 2024, SOCC 2024, IEEE ICDCS 2019/2023, and IEEE Cluster 2021, among others.

Title:Presentation title: Rethinking SQL Rewriting & Optimization and Equivalence Verification

Abstract: In this presentation, the speaker will delve into the latest insights and advancements in the realm of SQL rewriting and optimization. This process involves rewriting or optimizing SQL query statements to enhance their performance and efficiency, which is fundamental to database management systems. The presentation will place a spotlight on web-based scenarios, with a bold exploration of conventional methods of SQL rewriting and optimization presented in two main sections. The first part introduces our newly developed SQL rewriting & optimization system, WeTune. Drawing inspiration from the concept of super optimization, WeTune leverages brute-force enumeration in tandem with formal methods. This approach allows WeTune to automatically generate SQL rewriting rules that are specifically tailored to the requirements of the application, culminating in a significant improvement in SQL rewriting efficiency within web environments. The second part discusses our groundbreaking theory for SQL equivalence verification, Lia*-T, and the development of SQL Solver, a novel SQL equivalence validator based on this theory. By combining SQL Solver with WeTune, we have substantially enhanced WeTune’s ability to synthesize effective SQL rewriting rules. The relevant work has been accepted and open-sourced at SIGMOD’22 and SIGMOD’24.

Professor Tang Liu

Sichuan Normal University,  China     

Tang Liu is currently a professor and vice dean of College of Computer Science at Sichuan Normal University where he directs MobIle computiNg anD intelligence Sensing (MINDs) Lab. He received his B.S. degree in computer science from the University of Electronic and Science of China in 2003 and the M.S. and Ph.D. degrees in computer science from Sichuan University in 2009 and 2015, respectively. From 2015 to 2016, he was a visiting scholar with the University of Louisiana at Lafayette. His current research interests include Internet of Things, Wireless Networks and Mobile Computing. He has published more than 50 peer-reviewed papers in technical conference proceedings and journals, including INFOCOM, TON, TMC, TWC, TCOM, TOSN, IPDPS, etc. He has served as the Reviewer for the following journals: TMC, TOSN, Computer Networks, TSUSC, IEEE IoT J, and so on. He also has served as the TPC member of several conferences, such as HPCC, MSN, ICC, Globecom, etc.

Speech Title：Research and Exploration of Wireless Rechargeable Networks

Abstract：With the maturity of wireless power transfer technology, wireless rechargeable networks consisting of wireless chargers and rechargeable sensors have attracted widespread attention from academia and industry. However, due to the attenuation of radio waves during transmission, charging efficiency has become a key hurdle that stunts the growth of wireless rechargeable networks. In this talk, I will discuss how to leverage the characteristics of wireless chargers and the interference of radio waves to improve charging performance. In addition, we will introduce the latest research work conducted by our team over the past year in the field of wireless charging.

Assistant Professor Wenfei Wu

Peking University, China

Wenfei Wu is an assistant professor from the School of Computer Science at Peking University. He obtained his Ph.D. degree from the University of Wisconsin-Madison in 2015. Dr. Wu researches into computer networks and distributed systems, and has published more than 50 papers in these areas. Dr. Wu's recent research focus is to build in-network computation (INC) methods for distributed systems; his work on INC-empowered distributed machine learning system ATP won the best paper award in NSDI 2021, and that on INC-empowered distributed data analytics system ASK won the distinguished paper award in ASPLOS 2023; Dr. Wu won other awards like IPCCC best paper runner-up in 2019, SoCC best student paper in 2013, etc.

Speech Title：Accelerating Distributed Systems with In-Network Computing

Abstract：With Moore's Law failing to promote the computation power at a single node, building distributed and heterogeneous systems becomes a new trend to support large-scale applications, such as large model training and big data analytics. In-Network Computing (INC) is an effective approach to building such distributed systems. INC leverages programmable network devices to process traversing data packets, and provides line-rate and low-latency data processing capabilities, which could compress traffic volume and accelerate the overall transmission and job efficiency. In this talk, we will share the progress and development of INC technologies, including INC protocol design for machine learning and data analytics, RDMA-compatible INC solutions, and runtime INC job management in clusters. These works are published in NSDI21, NSDI23, ASPLOS23, and INFOCOM23.

Associate Professor Zhenhua Li

Tsinghua University, China

Zhenhua Li is a Tenured Associate Professor and a Doctoral Supervisor at the School of Software, Tsinghua University, specializing in mobile networks, operating systems, and cloud computing technologies. His research has been supported by the National Natural Science Foundation of China  (including the Excellent Young Scholars’ Program and the General Program), the National Key Research and Development Program of China, the Youth Innovation Program from the National Research Center for Information Technology, and collaborative research and development projects with leading companies such as Xiaomi, Tencent, Huawei, Microsoft, Alipay, ByteDance, China Telecom, and UUTest. He has published more than 30 papers at prestigious conferences such as SIGCOMM, MobiCom, OSDI, and NSDI. He has also published six cover papers in important domestic and international journals. He has been recognized with several awards, including the First Prize of the Ministry of Education’s Natural Science Award (as the fourth author), the Outstanding Doctoral Dissertation Award of the Chinese Association for Artificial Intelligence (CAAI), and the Youth Science and Technology Award of the China Computer Federation (CCF). Additionally, he has received the Best Paper Award from IEEE Transactions on Cloud Computing’15, the Sole Best Student Paper Award at MMSys’17, the Best Demo Award at MobiCom’19, the Sole Best Student Paper Award at SIGCOMM’21 (first time in Asia), and the Sole Best Community Paper Award at MobiCom’22, along with multiple Outstanding Program Committee Member Awards at the INFOCOM conferences.

Title:The Culprit behind Android Phones’ Stagnation: Confusions and Reflections from 47 Million Users

Abstract:Compared with iPhones, Android phones suffer much more significantly from stagnations, i.e., slow UI responsivenesses, which have long confused the users. To demystify this problem, we collaborated with Xiaomi Corporation to conduct the first large-scale and long-term measurement study on Android phones’ stagnation, leveraging crowdsourced data from 47 million Xiaomi/Redmi smartphone users. Our study identifies the critical factors that lead to stagnations from the perspectives of device, system, application, and app market. Surprisingly, our findings reveal that the culprit behind Android phones’ stagnation turns out to be quite a few “hogging” apps that are fairly popular and developed by renowned companies, such as Baidu Tieba. These apps persistently occupy an unreasonable amount of system resources by leveraging the optimistic design of Android process management. We have built on the insights to remodel Android process states by fully considering their time-sensitive transitions and the actual behaviors of processes, with remarkable real-world impact on tens of miliions of Xiaomi/Redmi smartphones—the occurrences of stagnation are reduced by 60%, together with 10.7% saving of battery consumption

Professor Xiaofei Wang

Tianjin University, China

Xiaofei Wang is the Chief Scientist of Edge Cloud at PPIO and serves as a Chair Professor and Ph.D. supervisor at the College of Intelligent and Computing, Tianjin University. He is recognized as a National Youth Talent and is a distinguished young scholar in the Technical Committee on Distributed Computing and Systems of the China Computer Federation (CCF TCDCS). Dr. Wang has received numerous accolades, including the Fred W. Ellersick Prize for Best Magazine Paper from the IEEE Communications Society (ComSoc), the Asia-Pacific Outstanding Paper Award from the IEEE ComSoc, the First Prize of Scientific and Technological Progress Award, and Youth Science and Technology Award of Tianjin. His research primarily focuses on edge intelligence theories, edge computing system architectures, cloud-edge collaboration algorithms, and computing power network technologies. He has published over 200 high-impact research papers, with more than 90 of these indexed in the Science Citation Index (SCI). Among his publications, over 40 papers have been featured in top-tier journals classified as CCF-A or ranked in the first quartile by the Chinese Academy of Sciences (CAS), and another 40 papers in journals categorized as CCF-B or in the second quartile by CAS. His work has garnered substantial attention, amassing over 9,000 citations, and he holds more than 50 granted invention patents. Additionally, Dr. Wang serves as an associate editor for several high-ranking journals, including IEEE Communications Surveys & Tutorials (COMST), which boasts an impact factor of 34. He has also acted as an editorial board member of Special Issues for other leading journals. He has spearheaded numerous national and provincial projects, including grants from the National Natural Science Foundation of China (NSFC) and key R&D programs of the Ministry of Science and Technology. Dr. Wang has also overseen over ten projects in collaboration with major companies such as China Telecom, China Mobile, AsiaInfo, Huawei, and the China Electric Power Research Institute (CEPRI).

Title:Presentation title: Distributed Computing Power Network System Technology and Operational Practices

Abstract:The era of general artificial intelligence is ushered in by large-scale model technologies. Constructing low-latency, high-performance, and ubiquitously elastic computing power infrastructure to support large-scale model training, inference, and continuous iteration has become a pivotal challenge. This presentation will delve into the historical development, status quo, trends, and key challenges of edge cloud and computing power networks. It will explore scientific research advancements from perspectives such as intelligent scheduling, service assurance, supply-demand forecasting, and computing power transactions. Furthermore, it will highlight practical applications through industry-university research cooperation initiatives as representative examples.