Prof. David A. Bader

Distinguished Professor, New Jersey Institute of Technology, USA

Solving Global Grand Challenges with High Performance Data Analytics (05/12, 9:00-10:00)

Abstract: Data science aims to solve grand global challenges such as: detecting and preventing disease in human populations; revealing community structure in large social networks; protecting our elections from cyber-threats, and improving the resilience of the electric power grid. Unlike traditional applications in computational science and engineering, solving these social problems at scale often raises new challenges because of the sparsity and lack of locality in the data, the need for research on scalable algorithms and architectures, and development of frameworks for solving these real-world problems on high performance computers, and for improved models that capture the noise and bias inherent in the torrential data streams. In this talk, Bader will discuss the opportunities and challenges in massive data science for applications in social sciences, physical sciences, and engineering.

David A. Bader is a Distinguished Professor in the Department of Computer Science and founder of the Department of Data Science and inaugural Director of the Institute for Data Science at New Jersey Institute of Technology. Prior to this, he served as founding Professor and Chair of the School of Computational Science and Engineering, College of Computing, at Georgia Institute of Technology. Dr. Bader is a Fellow of the IEEE, AAAS, and SIAM, and advises the White House, most recently on the National Strategic Computing Initiative (NSCI) and Future Advanced Computing Ecosystem (FACE). Bader is a leading expert in solving global grand challenges in science, engineering, computing, and data science. His interests are at the intersection of high-performance computing and real-world applications, including cybersecurity, massive-scale analytics, and computational genomics, and he has co-authored over 300 scholarly papers and has best paper.

Prof. Minyi Guo

Professor, Shanghai Jiao Tong University, China

当云计算遇到人工智能 (05/12, 10:40-11:20)

摘要: 云计算是我国数字化转型的重要基石，特别以阿里云、腾讯云、华为云为代表的云计算提供商在电子商务、社交网络、基础架构方面起到了重要的支撑作用。据IDC预测，到2021年底，预计80%的应用开发部署都将基于云端。为此，以单机程序搬迁、虚拟机构建以及分层服务为典型特征的传统云计算已不适应于我国数字化转型的发展需求。同时，自动驾驶、自然语言处理等人工智能应用也给云计算在实时性、低延迟等方面提出了更高的QoS要求。为此亟需发展以微服务和无服务器计算为特征的新一代云计算技术。依托上海交通大学云计算团队近5年的工作，本讲座将从云原生的微服务调度和无服务器容量规划为抓手，介绍基于人工智能技术的云原生构建方法； 同时，介绍基于新一代云计算技术，如何在负载突变、低吞吐、和混合部署等场景下增强人工智能应用的能力。

过敏意教授是上海交通大学讲席教授，欧洲科学院外籍院士，IEEE Fellow，CCF Fellow，国家杰出青年科学基金获得者。担任教育部创新团队学术带头人，973计划首席科学家，享受国务院特殊津贴。长期从事并行与分布式系统和云计算的研究，在各种学术期刊、会议上发表了450多篇论文，著述英文著作4部。主持国家杰出青年科学基金、973计划项目、国家自然科学基金重点项目、863项目等。曾获国家技术发明二等奖和省部级科技一等奖多项。现任IEEE Transactions on Sustainable Computing主编并长期担任包括IEEE Transactions on Parallel and Distributed Systems，IEEE Transactions on Cloud Computing等国际著名期刊的编委。

Prof. Yinqian Zhang

Professor, Southern University of Science and Technology, China

Confidential Computing: Security and Applications (05/12, 11:20-12:00)

Abstract: With the increasing demand of data security in the era of cloud computing and artificial intelligence, confidential computing has become a promising technology with growing popularity. Confidential computing is enabled by trusted execution environments (TEEs), which are commercially available CPU extensions providing memory isolation, encryption, and remote attestation for applications to guard against rogue system software. Confidential computing offers strong security guarantees for “data in use” even on untrusted computer systems and therefore has broad applications in data security and privacy-enhanced computation. This talk will cover a brief introduction of our recent work and thoughts on the security and future applications of confidential computing.

Prof. Yinqian Zhang is a professor at Southern University of Science and Technology (SUSTech). His research interests span across multiple domains of computer security, including system security, software security, and architecture security, security of decentralized systems and applications, security of AI systems, formal verification for system security, etc. His research has been frequently published at top-tier security venues, such as IEEE S&P, ACM CCS, USENIX Security, and NDSS. Prof. Zhang was a recipient of a CAREER Award from the National Science Foundation in 2018, Lumley Research Award and Outstanding Teaching Award from the Ohio State University in 2019, Rising Star Award from the Association of Chinese Scholars in Computing in 2019, and several honorable mentions of AMiner Most Influential Scholar in Security and Privacy.

Prof. Tarek Abdelzaher

Professor, University of Illinois at Urbana Champaign, USA

Real-Time Intelligent Services for Internet of Things Applications (06/12, 9:00-10:00)

Abstract: Advances in neural network revolutionized modern machine intelligence, but important challenges remain when applying these solutions in IoT contexts; specifically, in cost-sensitive applications on lower-end embedded devices. The talk discusses challenges in offering real-time machine intelligence services at the edge to support applications in resource constrained environments. The intersection of IoT applications, real-time requirements, and AI capabilities motivates several important research directions. For example, how to support efficient execution of machine learning components on low-cost edge devices while retaining inference quality and offering confidence estimates in results? How to reduce the need for expensive manual labeling of IoT application data? How to improve the responsiveness of AI components to critical real-time stimuli in their physical environment? How to prioritize and schedule the execution of intelligent data processing workflows on edge-device GPUs? How to exploit data transformations that lead to sparser representations of external physical phenomena to attain more efficient learning and inference? The talk discusses recent advances and presents evaluation results in the context of different real-time edge AI applications.

Tarek Abdelzaher received his Ph.D. in Computer Science from the University of Michigan in 1999. He is currently a Sohaib and Sara Abbasi Professor and Willett Faculty Scholar at the Department of Computer Science, the University of Illinois at Urbana Champaign. He has authored/coauthored more than 300 refereed publications in real-time computing, distributed systems, sensor networks, and control. He serves as an Editor-in-Chief of the Journal of Real-Time Systems for over 10 years, and has served as Associate Editor of the IEEE Transactions on Mobile Computing, IEEE Transactions on Parallel and Distributed Systems, IEEE Embedded Systems Letters, the ACM Transaction on Sensor Networks, and the Ad Hoc Networks Journal, among others. Abdelzaher's research interests lie broadly in understanding and influencing performance and temporal properties of networked embedded, social and software systems in the face of increasing complexity, distribution, and degree of interaction with an external physical environment. Tarek Abdelzaher is a recipient of the IEEE Outstanding Technical Achievement and Leadership Award in Real-time Systems (2012), the Xerox Award for Faculty Research (2011), as well as several best paper awards. He is a fellow of IEEE and ACM.

Prof. Yongsheng Ou

Professor, Shenzhen Institutes of Advanced Technology, CAS, China

低成本的室内外导航技术研究与应用 (06/12, 11:20-12:00)

摘要: 导航是无人驾驶车与服务机机器人的关键技术，导航技术的成本问题，是制约服务机器人产品功能开发与应用推广的关键。报告在介绍低成本导航的研究背景与意义的基础上，重点介绍了视觉SLAM与低成本激光SLAM两种低成本导航的研究方向，并对低成本、可靠、实用的导航新技术方案进行了探讨。

欧勇盛，中国科学院深圳先进技术研究院，研究员。2010年从美国回国后，获批国家“万人计划” 领军人才、中科院百人计划、科技部中青年科技创新领军人才。近年来主持国家863重点类项目课题、国自然重点类项目、科技部2030-“新一代人工智能”重大项目课题、广东省重点领域研发计划项目等各级重要项目累计获取经费超过6000万元。近五年相关成果在控制和机器人领域发表SCI论文80余篇，其中JCR1区文章40余篇，他引超过 2000次，出版1部英文专著。多项核心关键技术申请专利100余项，并逐步转化到新松、银星、优必选等公司开发的产品中。先后获得广东省技术发明奖、吴文俊人工智能科学技术奖和深圳市科技进步奖等多个奖项。

Prof. Qi Hao

Associate Professor, Southern University of Science and Technology, China

Toward Highly Integration of Real-world Datasets and Virtual-world Simulation for L5 Autonomous Driving (06/12, 12:00-12:40)

Abstract: The current autonomous driving (AD) technologies heavily rely on both real-world datasets and virtual-world simulations to achieve L5 mind-free full autonomy. The former has been used for training and testing perception and prediction algorithms; the latter can provide extreme and complicated testing cases for the whole AD system. However, two platforms are far from being highly integrated: the real-world datasets cannot help construct simulation environments efficiently; meanwhile, the simulation datasets cannot help improve the performance of the perception and prediction algorithms effectively along with real-word datasets. This study aims to solve this problem from three perspectives: dataset quality metrics, scene generation and transfer learning.

Prof. Qi Hao is an associate professor and deputy head of the CSE department of SUSTech, who received the PhD Degree from the Duke university and has been working in the areas of intelligent sensing and autonomous systems. The SUSTech Center for Intelligent Transportation under his supervision has been sponsored by Intel, Huawei, Shenzhen Bus Group, and other companies. He has published more than 100 journal and conference papers in related areas.

Prof. Xiaoyi Lu

Assistant Professor, The University of California Merced, USA

Designing Fast and Scalable Storage Systems for Heterogeneous Memory (05/12, 10:00-10:25)

Prof. Kejiang Ye

Professor, Shenzhen Institute of Advanced Technology, CAS, China

云原生助力工业数字化转型 (05/12, 12:00-12:25)

Dr. Guoyao Xu

Alibaba Group, China

阿里巴巴Serverless基础设施的大规模降本增效 (05/12, 14:00-14:25)

Prof. Yang Wang

Professor, Shenzhen Institute of Advanced Technology, CAS, China

面向智能驾驶的边云协同计算关键技术研究 (05/12, 14:25-14:50)

Prof. Huanle Xu

Assistant Professor, University of Macau, Macau SAR, China

An In-depth Study of Microservice Dependency and Runtime Performance (05/12, 14:50-15:15)

Prof. Ying Li

Professor, Peking University, China

人机物融合的智能运维 (05/12, 15:35-16:00)

Prof. Minxian Xu

Assistant Professor, Shenzhen Institute of Advanced Technology, CAS, China

Multi-faceted Scaling of Microservices with Reinforcement Learning (05/12, 16:00-16:25)

Dr. ‪Yi Qin

Assistant Researcher, Nanjing University, China

云环境下微服务更新适应策略偏差检测 (05/12, 16:25-16:50)

Prof. Tong Zhang

Professor, Rensselaer Polytechnic Institute, USA

Computational Storage: Another Fantasy or A Real Big Thing? (06/12, 10:00-10:25)

Prof. Yu Wang

Professor, Tsinghua University, China

Towards Energy-efficient System and Architecture for Artificial Intelligence (06/12, 10:25-10:50)

Prof. Hui Kong

Associate Professor, University of Macau, Macau SAR, China

Exploratory SLAM for Autonomous High-Definition Map Generation (06/12, 14:00-14:40)

Prof. Dejing Dou

Baidu Research, China

Transfer learning and application to autonomous driving (06/12, 14:40-15:05)

Prof. Li Li

Assistant Professor, University of Macau, Macau SAR, China

Computing System for Autonomous Driving: Design Constraints and System Optimization (06/12, 15:05-15:30)

Prof. Yupeng Li

Assistant Professor, Hong Kong Baptist University, Hong Kong SAR, China

Robust online learning against malicious manipulation with application to traffic classification

(06/12, 15:50-16:15)

Prof. Daxue Liu

National University of Defense Technology, China

人机协同研究进展 (06/12, 16:15-16:40)

Prof. Yuan Wu

Associate Professor, University of Macau, Macau SAR, China

异构混行交通流泛在控制：一种数字孪生驱动的边缘智能技术 (06/12, 16:40-17:05)