Ka Ho Chow

Ka Ho Chow is an Assistant Professor in the Department of Computer Science at the University of Hong Kong, part of the School of Computing and Data Science. He holds a PhD from Georgia Institute of Technology and was previously a research scientist at IBM Research. His research focuses on the intersection of machine learning, cybersecurity, and scalable systems, emphasizing trustworthy AI and defense against security/privacy threats in federated learning, large language models, and visual recognition systems. Key achievements include IBM PhD Fellowship (2022) and Croucher Scholarship (2021).

Education: PhD in Computer Science from Georgia Tech (2020), advised by Prof. Ling Liu. His work spans algorithmic optimization, infrastructure resilience, and adversarial machine learning. Current research explores attack-resilient solutions for centralized/federated learning and AI system vulnerabilities.

Recent articles highlight innovations in federated learning security, gradient inversion attacks, backdoor detection, and privacy-preserving techniques. He has openings for PhD students interested in AI security and trustworthy systems. His lab collaborates on projects involving blockchain fraud detection (ZipZap), facial recognition privacy (Personalized Masks), and graph neural network robustness.

Awards: IBM PhD Fellowship (2022), Croucher Scholarship (2021). Active in guiding PhD candidates and advising on microservices cloud migration (Atlas/SCAD systems). Research outputs include over 30 peer-reviewed papers spanning cybersecurity, AI ethics, and distributed learning frameworks.