I am an assistant professor at the Electrical and Computer Engineering department, Michigan Technology University. My research interests lie in deep learning-related cybersecurity, hardware security, and network security.

I received my Ph.D. in Electrical Engineering from the University of Florida in 2022, under the supervision of Professor Shuo Wang, Professor Yier Jin, and Professor Yuguang Fang. I received my M.S. (2016) and B.S. (2013) in Information Science from University of Science and Technology of China (USTC) advised by Dr. Chi Zhang. You can find my CV here: Kaichen Yang’s Curriculum Vitae.

I am looking for Ph.D. students who are interested in cybersecurity topics, including but not limited to deep learning security, IoT security, and hardware security. Full financial support in research assistantship (RA) or teaching assistantship (TA) will be offered for qualified students. If interested, please send your CV, transcript(s), and sample publications to Dr. Kaichen Yang at kaicheny@mtu.edu. Please refer to this link for the GRE/TOEFL requirements and other application-related information.

For students at Michigan Tech, multiple paid part-time and volunteer research assistant positions are available. If you are interested, please send me your CV and transcripts (unofficial copies are acceptable).

News

• \({\color{red}[2024.11]}\)[Activity]: Congratulations to Madhureeta Das to defend his Ph.D. thesis!
• \({\color{red}[2024.07]}\)[Grant]: Thanks NSF for the NSF OAC CICI Grant (Co-PI) to fund our project on Safeguarding AI in Bioinformatics!
• \({\color{red}[2024.06]}\)[Award]: I received NSF ERI (pre-career) Award. Thanks, NSF!
• \({\color{red}[2024.01]}\)[Panel]: I am happy to serve as the NSF panelist and complete a two-day panel discussion!
• \({\color{red}[2023.12]}\)[Talk]: I attended and presented our paper “Llm4sechw: Leveraging domain-specific large language model for hardware debugging” at IEEE AsianHOST 2023, Tainjin, China.
• \({\color{red}[2023.11]}\)[Paper]: Our paper “Hardware phi-1.5 b: A large language model encodes hardware domain specific knowledge” has been accepted by IEEE/ACM ASP-DAC 2024.
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