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Have you ever wanted to learn about machine learning but didn’t know where to start? Duke Kunshan University’s AI-powered Self-Driving program at the Global Summer Institute (GSI) offers the perfect introduction. This fun and interactive program allows high school students to dive into AI through AWS DeepRacer—a mini autonomous race car designed to learn and improve as it races.
In this program, students will explore how machine learning enables self-driving cars to make smart decisions on the road. With no prior technical background required, students will experiment with AWS DeepRacer to understand how the car learns to stay on track, make turns, and even compete in races. From setting up the car to fine-tuning its performance on the track, students will experience the excitement of building their own model and seeing it in action.
Through hands-on activities, students will use built-in resources to guide their car’s learning process, discovering the basics of creating smart systems that improve through practice. By the end of the program, they’ll have a clearer understanding of how self-driving technology works and how it’s applied in the real world.
By the end of the program, students will:
This program will not only introduce students to the basics of self-driving technology, but also inspire them to think creatively and build their own solutions to real-world problems using AI.
Dr. Bing Luo has a long-term collaboration with Amazon AWS in China and has organized several events related to this Summer Camp. Featured events include:
His students have won the 7th Place in the 2024 Amazon DeepRacer China Enterprise Championship Finals.
Bing Luo is an Assistant Professor of Data and Computational Science at Duke Kunshan University (DKU), and Adjunct Professor at Wuhan University. Prior to joining DKU, he was a joint Postdoc Researcher at The Chinese University of Hong Kong (Shenzhen) and Yale University. He received his Ph.D. from The University of Melbourne, and the B.E. and M.S. degrees from Beiling University of Posts and Telecommunications (BUPT). Before pursuing his Ph.D, he gained several years of industry experience as a project manager at China Mobile Corporation Headquarter. His research interests include the theory and practice of federated and edge learning, as well as embedded Al for loT and mobile systems. His work has been published in leading journal and conference papers, including IEEE JSAC, TCOM, TMC, INFOCOM, ICDCS, and ACM MobiHoc. He has been invited to deliver multiple talks at various world-renowned universities, including Yale University, Purdue University, Imperial College London, and Hong Kong University. He is a senior member of the IEEE. For more information, please visit his webpage: https://luobing1008.github.io/
Paul Weng is a tenured associate professor at Duke Kunshan University. Before joining Duke Kunshan, he was an associate professor at the University of Michigan-Shanghai Jiao Tong University Joint Institute. Besides, he was a regular or visiting faculty member of many universities (Sorbonne Université, Carnegie Mellon University, Sun Yat-sen University, University of Nottingham Ningbo China). Before joining academia, he was a financial quantitative analyst in London, UK. As a researcher, he regularly publishes in top AI and machine learning venues (e.g., IJCAI, AAAI, ICML…). He has served as an area chair at AAAI and ECAI. Several of his papers received a best paper award (e.g., MIWAI, ALA). His work has been funded both by public funding agencies (NSFC, Shanghai NSF) and private companies (Yahoo, Huawei, Netease).
His main research work lies in artificial intelligence (AI) and machine learning. Notably, it focuses on (deep) reinforcement learning, a research direction he has been exploring for more than 10 years.
For more information, please visit his webpage: https://weng.fr/
For Chinese Students:
For International Students:
* Program fee included: Tuition, visit fee, accommodation, partial catering costs, study materials, insurance, etc.
* Exclude: Travel to and from Kunshan, personal expenses and expenses not mentioned, etc.
For Chinese Students:
For International Students:
– Current high school students (grades 10-12) with excellent overall qualities and good listening, speaking, reading, and writing skills in English.
– Open to all arts and science students and admission is based on merit according to batch.
Applicants must complete the information requested within the link in its entirety which includes:
The high school transcript must affix an official seal or the academic affairs department seal. Chinese students are recommended to use the transcript template provided by the program team. Transcripts should include end-of-semester grades for each semester since high school (full marks for individual subjects must be indicated). Please also provide overall rankings or rankings by arts/science and indicate the total number of students If there is a grade ranking. You can find the transcript template here:
Please introduce yourself in English with no more than 300 words (e.g. personal background, reasons for choosing the program, academic interests, leadership experience, extracurricular activities…).
Notes: While these materials are not required, it is recommended that you merge them into a PDF file before uploading.
Email: international@dukekunshan.edu.cn
