Alex Lin Ling

Alex Lin Ling

Ph.D. Candidate in Computer Science

City University of Hong Kong


I am a Ph.D. student in Computer Science at City University of Hong Kong. Advised by Dr. Chee Wei Tan, my research interests include convex optimization and distributed algorithms, as well as their applications in graph analytics, machine learning and large-scale education. My research has been featured on the IEEE Information Theory Society Newsletter.

I am also an entrepreneur and an active open-source contributor. I co-founded an AI startup that is incubated in the Hong Kong Science Park and generating steady revenue. I have been maintaining and contributing to open-source projects for years and I have received over one thousand stars on GitHub.

CV Research Statement Teaching Statement


  • Convex and Non-Convex Optimization
  • Distributed Algorithms
  • Statistical Machine Learning
  • Personalized Learning at Scale


  • Ph.D. in Computer Science, 2021

    City University of Hong Kong

  • B.Sc. in Applied Physics, 2017

    City University of Hong Kong



Visiting Student Research Collaborator

Department of Electrical Engineering, Princeton University

Nov 2019 – Apr 2020 Princeton, NJ, US
Advised by Dr. Yuxin Chen, I worked on the problem of crowd-sourced rank aggregation. We are currently working on a conference submission.

Co-founder, CTO

Nautilus Software Technology Limited

Feb 2018 – Present Hong Kong
Our company is recognized by the Hong Kong Science and Technology Park (HKSTP) and incubated under the prestigious Incu-Tech program. We provide educational AI solutions to our clients including Singapore International School, Tung Wah Group of Hospitals, and South China Morning Post.

Ph.D. Candidate

Department of Computer Science, City University of Hong Kong

Sep 2017 – Present Hong Kong

Undergraduate Student

Department of Physics, City University of Hong Kong

Sep 2013 – Jul 2017 Hong Kong
I was put on the dean’s list for four times, and my final year research was awarded Outstanding Academic Papers by Students (OAPS).