Weisong Shi

Alumni Distinguished Professor and Chair of Computer and Information Sciences University of Delaware

  • Newark DE

Prof. Shi is an internationally renowned expert in edge computing, autonomous driving, and connected health.

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From Experimentation to Implementation: A Look at the Real-World Applications Edge Computing on Autonomous Driving

What exactly is edge computing and how does it relate to self-driving cars? A University of Delaware expert has been diving deep into the subject through the university's Connected and Autonomous Research Laboratory (CAR Lab).  Weisong Shi is a professor and chair of the Department of Computer and Information Sciences at UD, where he leads the CAR Lab.  Edge computing is how scientists move computations closer to the user. Self-driving cars have to gather and process big batches of data to work, and in a short amount of time. The time spent sending data to a physically distant server and then back again may cause delay that could impact real-time decisions. Shi's research with edge computing is working to alleviate that issue without turning every car into a supercomputer that consumes a lot of computing power and energy. Shi and his team are building a world-class live research and education infrastructure on the STAR Campus at the University of Delaware.  He can be reached by clicking his profile. 

Weisong Shi

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Biography

Dr. Weisong Shi is an Alumni Distinguished 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

Transportation/Trucking/Railroad
Computer Networking
Health Care - Services
Computer Software

Areas of Expertise

Edge Computing
Vehicle Computing
Autonomous Driving
Mobile and Connected Health

Media Appearances

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.”

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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.

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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.

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Articles

Towards Resilient Network Slicing for Satellite-Terrestrial Edge Computing IoT

IEEE Internet of Things Journal

2023

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.

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Fuel Rate Prediction for Heavy-Duty Trucks

IEEE Transactions on Intelligent Transportation Systems

2023

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.

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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.

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Accomplishments

Most Influential Scholar Award, AI 2000

2022

Outstanding Scientific Article Award, CAST

2020

IEEE TCI Distinguished Service Award

2020

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Education

Chinese Academy of Sciences

PhD

Computer Engineering

2000

Xidian University

BEng

Computer Engineering

1995

Affiliations

  • 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

Languages

  • English
  • Chinese

Event Appearances

"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) LFEdge Akraino Summit  

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