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Artificial intelligence (AI) has surpassed humans in numerous areas, ranging from games like chess and go to practical applications like cancer detection. Thanks to the recent progress in deep learning and the abundance of big data, AI is enhancing productivity and driving innovation across various fields, and this transformative process remains ongoing.
AI presents both challenges and opportunities for high school students interested in pursuing related subjects in college. This program offers students the chance to delve into AI and its applications across various scenarios. In lieu of delving deeply into the fundamental theories and intricate algorithms of AI, students will grasp the principles and acquire hands-on experience. The course takes a modern approach to AI and spotlights a diverse array of emerging applications driven by recent breakthroughs in machine learning. Upon completion of the course, students should have a solid understanding of different AI modules, as well as comprehend the “what, how, and why” of various AI methods.
Throughout the week, students will explore several pivotal modules and put them into practice through real-world applications, encompassing data analytics, image and video processing, natural language processing, etc. Additionally, they will delve into the current and future trends of AI, including in-depth discussions on generative AI. In addition to lectures, students will have access to coding examples and gain hands-on experience through a series of mini projects.
– 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.
Kaizhu Huang is currently Professor of ECE and Director of Data Science Research Center at Duke Kunshan University (DKU). He is Adjunct Professor and PhD Advisor at Wuhan University. Prof. Huang has extensive working experiences in Hong Kong, Japan, and UK. He won the top research award ‘President Award’ in Fujitsu Laboratories in 2007. Prof. Huang obtained his PhD degree from Chinese University of Hong Kong (CUHK) in 2004. He was the recipient of the 2011 Asia Pacific Neural Network Society Young Researcher Award. He received best paper/book awards or nominations seven times in AI. His research interests include machine learning, computer vision, data mining and deep learning. So far, he has published 9 English books and over 230 international research papers including 120+ SCI-indexed international journals. He serves as associated editors in 6 international journals. He was invited as keynote speaker in more than 40 international conferences or workshops.
Dongmian Zou is an Assistant Professor of Data Science in the Division of Natural and Applied Sciences, Duke Kunshan University. He is also affiliated with the Data Science Research Center (DSRC) and the Zu Chongzhi Center for Mathematics and Computational Sciences (CMCS). He received the B.S. degree in Mathematics (First Honour) from the Chinese University of Hong Kong in 2012 and the Ph.D. degree in Applied Mathematics and Scientific Computation from the University of Maryland, College Park in 2017. From 2017 to 2020, he served as a post-doctorate researcher at the Institute for Mathematics and its Applications, and the School of Mathematics at the University of Minnesota, Twin Cities. His research is in the intersection of applied harmonic analysis, machine learning and signal processing. His current interest includes geometric deep learning and anomaly detection, and applications in e.g., communication, diagnosis and medical imaging. He has published papers in top machine learning and interdisciplinary conferences such as ICLR, KDD and AISTATS, and journals such as Applied and Computational Harmonic Analysis and IEEE TIT, TNNLS, TCAD, etc. He is passionate about mentoring undergraduate research. Many students he mentored for research have been directly admitted to PhD programs (UC Berkeley, UIUC, UPenn, etc.) or admitted to top master’s programs (MIT, Harvard, Brown, etc.).
