About Me
Here is Hanlin Cai (Lance, 蔡汉霖).
I am a graduate student in the Department of Engineering at the University of Cambridge, advised by Prof. Özgür Akan, within Internet of Everything (IoE) Group. I also spent a lovely summer research program with Prof. Pietro Liò at Artificial Intelligence Group. Prior to Cambridge, I have worked on System Engineering, Cybersecurity and Wireless Communication with Prof. Zhezhuang Xu and Dr. Meng Yuan. Recently, I was honored to be selected as AAAI and SIGKDD Undergraduate Scholars.
If you are interested in any aspect of me, I am always open to discussions and collaborations. Feel free to reach out to me at - hc663 [at] cam.ac.uk
Research Interests
- Internet of Everything
- Cyber-Physical System
- Applied Machine Learning
My current research focuses on practical problems that artificial intelligence faces in real life. My interests are on the Machine Learning and its applications in Industrial IoT. In a word, advanced technologies like ML and IoT positively influence the life of everybody. I wish to devote my talent to this meaningful cause and bring well-being to society.
News and Updates
- June 2024:Very excited to be selected as KDD UC Scholar. See you in Spain!
- May 2024:My bachelor thesis won the Annual Best Thesis Award (Top 1/300).
- April 2024:Our work BLEGuard has been accepted to MobiSys 2024 as a poster paper. See you in Japan!
- March 2024:Very excited to get a MPhil offer from Engineering department at Cambridge University!
- Dec 2023:Very excited to be selected as AAAI UC Scholar. See you in Canada!
- Jun 2022:Started research programme at Cambridge AI Group, advised by Prof. Pietro Liò.
Thrilled to be an AAAI-UC Scholar at #AAAI24, thanks to #AAAI & #GoogleExploreCSR for the sponsorship. Grateful for the knowledge gained and new friendships formed.
— Hanlin CAI (seeking a PhD position 2025) (@lancecai2002) February 26, 2024
Wonderful trip in Vancouver. Looking forward to staying connected with all.#AAAI24 #Vancouver #GoogleExploreCSR pic.twitter.com/wUQUp8XlSM