Weisong Shi
Professor and Chair of Computer and Information Sciences | University of Delaware
Newark, DE, UNITED STATES
Prof. Shi is an internationally renowned expert in edge computing, autonomous driving, and connected health.
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Biography
Dr. Weisong Shi is a Professor and Chair of the Department of Computer and Information Sciences at the University of Delaware (UD). Before joining UD, he was a faculty of Computer Science at Wayne State University and served on multiple administrative roles, including the Associate Dean for Research and Graduate Studies at the College of Engineering, Interim Chair of Computer Science and a Program Director of NSF. He founded the Connected and Autonomous Research Laboratory (The CAR Lab) in December 2017. He is an IEEE Fellow and a Distinguished Scientist of ACM.
Industry Expertise (2)
Computer Networking
Health Care - Services
Areas of Expertise (4)
Edge Computing
Vehicle Computing
Autonomous Driving
Mobile and Connected Health
Media Appearances (5)
Weisong Shi named Computer and Information Sciences Chair
University of Delaware online
2022-09-30
Shi, who holds dozens of academic honors and awards and has published over 250 journal articles and conference papers, said he is humbly honored and excited to join UD faculty, work with renowned professors and contribute to the University’s legacy. “As one of the nation’s oldest computer and information sciences departments, UD’s CIS department is renowned for its high-quality programs and high-impact research,” he said. The faculty are acclaimed experts in their domain, more than 30% of tenured/tenured track are recipients of the prestigious NSF CAREER Award. In addition, the department has committed to building a diverse and inclusive environment, where more than 30% of faculty members are female — probably the highest among all computer science departments nationwide.”
Weisong Shi recognized as leader in electric vehicle revolution
Today@Wayne online
2022-06-08
Weisong Shi, professor and interim chair of computer science in Wayne State’s College of Engineering, has been recognized by Crain’s Detroit Business as a notable leader in electric vehicles. Shi, who is also a Charles H. Gershenson Distinguished Faculty Fellow and leads the Wayne Mobility Initiative and the Connected and Autonomous Research Laboratory, is the only academic on the prestigious list. A global leader in edge computing research, Shi’s work has influenced automotive, technology, health care and energy. His students graduate in demand and are leaders at companies in Detroit and around the world, including General Motors Co., Ford Motor Co., Stellantis, Microsoft, Google, Siemens AG, Intel, IBM, Nokia Corp., Volkswagen and more.
The cutting edge of healthcare: How edge computing will transform medicine
Computer World online
2021-10-12
Weisong Shi, a Wayne State University computer science professor and an expert on edge computing and connected health, points to the future of emergency services as an example. Medics working in an ambulance could take pictures that could be collated with a patient’s biometric data from onboard medical devices and analyzed by an edge device. Results then could guide the medics on treatments to give en route and could alert emergency room clinicians on how best to prepare for the patient’s arrival. Or, Shi says, edge devices performing in-field real-time data computations could let medics evaluate and even effectively treat patients on site, helping cut down on unnecessary hospital visits.
Wayne State researchers receive NSF funding to develop COVID-19 risk-prediction system
Today@Wayne online
2020-07-25
Researchers from Wayne State University’s College of Engineering and the Henry Ford Health System are teaming up to design and implement such a system — and taking on several challenges in doing so. According to Weisong Shi, Ph.D., associate dean of research and graduate studies and professor of computer science at Wayne State, the team will work together to derive a system that can assess infection risk at different levels, such as individually or at large-event and institution levels. Shi also noted that the system must dynamically update the risk level based on the latest outbreak reports, as well as be able to preserve user sensitive data while sharing adequate and appropriate data that will allow risk-level calculation.
Wayne State researchers tabbed for Michigan Mobility Challenge at NAIAS
Today@Wayne online
2019-10-23
Weisong Shi, professor of computer science and director of the Connected and Autonomous Driving (CAR) Lab, and his students will be responsible for the autonomous shuttle testing data collection and analysis. The team is partnering with NAVYA, a French mobility company, which has offices in Saline - about 40 miles west of Wayne State's campus - that will provide shuttles for the event.
Articles (5)
Towards Resilient Network Slicing for Satellite-Terrestrial Edge Computing IoT
IEEE Internet of Things Journal2023 Satellite-Terrestrial Edge Computing Networks (STECNs) emerged as a global solution to support multiple Internet of Things (IoT) applications in 6G networks. The enabling technologies to slice STECNs such as Software-Defined Networking (SDN), satellite edge computing, and Network Function Virtualization (NFV) are key to realizing this vision. In this paper, we survey and analyze network slicing solutions for STECNs. We discuss slice management and orchestration for different STECNs integration architectures, satellite edge computing, mmWave/THz, and AI solutions to make network slicing adaptive. In addition, we identify challenges and open issues to slice STECNs.
Fuel Rate Prediction for Heavy-Duty Trucks
IEEE Transactions on Intelligent Transportation Systems2023 Fuel cost contributes significantly to the high operation cost of heavy-duty trucks. Developing fuel rate prediction models is the cornerstone of fuel consumption optimization approaches for heavy-duty trucks. However, limited by accurate features directly related to the truck’s fuel consumption, state-of-the-art models show poor performance and are rarely deployed in practice. In this paper, we use the truck’s engine management system (EMS) and Instant Fuel Meter (IFM) to collect a three-month dataset during the period of December 2019 to June 2020. Seven prediction models, including linear regression, polynomial regression, MLP, CNN, LSTM, CNN-LSTM, and AutoML, are investigated and evaluated to predict real-time fuel rate.
An Open Approach to Energy-Efficient Autonomous Mobile Robots
EEE International Conference on Robotics and Automation (ICRA)2023 Autonomous mobile robots (AMRs) have the capability to execute a wide range of tasks with minimal human intervention. However, one of the major limitations of AMRs is their limited battery life, which often results in interruptions to their task execution and the need to reach the nearest charging station. Optimizing energy consumption in AMRs has become a critical challenge in their deployment. Through empirical studies on real AMRs, we have identified a lack of coordination between computation and control as a major source of energy inefficiency. In this paper, we propose a comprehensive energy prediction model that provides real-time energy consumption for each component of the AMR. Additionally, we propose three path models to address the obstacle avoidance problem for AMRs.
Connected Living—Part I
IEEE Internet Computing2023 With the recent, remarkable advancements in the Internet of Things, wireless communication, edge/cloud computing, and artificial intelligence, the connection between human beings and their surrounding environments has dramatically increased, entering a new era of connected living. New challenges and opportunities are also brought up in the complex interaction between human beings and technologies. The focus of this special issue of IEEE Internet Computing is on investigating new technologies and human-technology interactions that will enable connected living to enhance well-being management and quality of life. The articles in this special issue investigate and present new technologies, visions, and projects in human–technology interactions toward connected living.
Vehicle computing: Vision and challenges
Journal of Information and Intelligence2023 Vehicles have been majorly used for transportation in the last century. With the proliferation of onboard computing and communication capabilities, we envision that future connected vehicles (CVs) will be serving as a mobile computing platform in addition to their conventional transportation role for the next century. In this article, we present the vision of Vehicle Computing, i.e., CVs are the perfect computation platforms, and connected devices/things with limited computation capacities can rely on surrounding CVs to perform complex computational tasks. We also discuss Vehicle Computing from several aspects, including several case studies, key enabling technologies, a potential business model, a general computing framework, and open challenges.
Accomplishments (4)
Most Influential Scholar Award, AI 2000 (professional)
2022
Outstanding Scientific Article Award, CAST (professional)
2020
IEEE TCI Distinguished Service Award (professional)
2020
Most Downloaded Paper Award, IEEE Computer (professional)
2018
Education (2)
Chinese Academy of Sciences: PhD, Computer Engineering 2000
Xidian University: BEng, Computer Engineering 1995
Affiliations (5)
- NSF CISE Advisory Committee
- Research Advisory Board of IEEE Computer Society
- IEEE Special Technical Community on Autonomous Driving Technologies : Chair
- IEEE Technical Committee on the Internet : Chair, 2012 - 2016)
- Elsevier Smart Health Journal : Editor-In-Chief, 2016-Present
Links (5)
Languages (2)
- English
- Chinese
Event Appearances (5)
"Vehicle Computing: Vision and Challenges"
(2023) PennEngineering's Autoware Safe Autonomy Seminar
"Vehicle Computing: Vision and Challenges"
(2022) Uber
"Vehicle Computing: Vision and Challenges"
(2022) IEEE International Conference on Uitility and Cloud Computing (UCC)
"Vehicle Computing: Vision and Challenges"
(2022) LFEdge Akraino Summit
"Vehicle Computing: Vision and Challenges"
(2022) University of Toledo
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