I am an assistant professor at the Data Science and Analytics (DSA) Thrust at the Hong Kong University of Science and Technology (Guangzhou). I obtained my Ph.D. in Computer Science with a focus on computational biology and applied machine learning at McGill University under the supervision of Prof. Mathieu Blanchette. I received my bachelor’s degree in Computer Science from the City University of Hong Kong. For more information, please check my CV.

Openings:

1. PhD: I have multiple fully-funded Ph.D. positions (Fall 2025) available at HKUST(GZ). I am particularly looking for students with a strong background in machine learning, such as LLMs, multimodal models, federated learning, etc., to work with me in the following directions:
   • Generative biology
   • Large language models
   • Data integration via multi-modal learning
   • Computational 3D genomics

2. RA/Intern: We have openings (remote) for research assistant/intern positions focusing on topics related to AI4Biology. Please email me if you are interested in joining.

3. RBM Students: If you are an RBM student who wants to work with me in computational biology, feel free to drop by my office.

4. Students at HKUST(GZ): If you are a student at HKUST(GZ) and want to do research with me, have a quick chat, or learn about computational biology, you are welcome to drop by my office.

Please contact me by email with your CV, transcripts, publications (if any), motivation letter (required for shortlisted Ph.D. applicants), and research proposal (optional).

Research

My research focuses on developing and applying computational methods (i.e., algorithmic and machine learning approaches) for interpreting complex biological data sets. I have an increasing interest in developing large models for biology. This includes:

• Developing deep learning and statistical inference approaches to analyze and comprehend DNA sequence and 3D genome organization.
• Developing and tailoring representation learning algorithms to analyze biological data sets.
• Developing and applying computational tools to analyze omics data (i.e., population genetics, cancer genomics, and MS/MS proteomics).
• Developing and applying LLM and applications in bioinformatics.

Selected Publications

• Zhang Yanlin, Rola Dali, and Mathieu Blanchette, “RobusTAD: Reference panel based annotation of nested topologically associating domains.” Genome Biology (minor revision submitted)
• Zhang Yanlin, and Mathieu Blanchette. “Reference panel guided topological structure annotation of Hi-C data.” Nature Communications 13.1 (2022): 7426.
• Zhang Yanlin, and Mathieu Blanchette. “Reference panel-guided super-resolution inference of Hi-C data.” Bioinformatics 39.Supplement_1 (2023): i386-i393. (ISMB/ECCB 2023 proceedings; Accept. rate=14%)
• Zhang Zhe, et al. “The Tibetan-Yi region is both a corridor and a barrier for human gene flow.” Cell Reports 39.4 (2022). (co-first author, cover article)
• Michael J. Johnston, et al. “TULIPs decorate the three-dimensional genome of PFA ependymoma”, Cell 187, 1-20 (2024)

Teaching

• Course Lecturer: COMP462/561, Computational Biology Methods and Research, McGill U. Fall 2021 [~110 students]
• Teaching assistant: COMP 462/561, Computational Biology Methods and Research, McGill U. Fall 2020 [~120 students]
• Head teaching assistant: COMP 551, Applied Machine Learning, MCGILL U. Winter 2020 [395 students]

Scholarships & Awards

2020, FRQNT Doctoral Scholarship, Quebec
2018, Grad Excellence Award in Computer Science, McGill, Quebec
2017, Grad Excellence Award in Quantitative Life Sciences, McGill, Quebec
2016, Outstanding Academic Papers by Students, CityU, Hong Kong S.A.R.
2015, Hong Kong government scholarship, Hong Kong S.A.R.
2013, Chan Wing Fui Scholarship, CityU, Hong Kong S.A.R.
2010, University Entrance Scholarship, CityU, Hong Kong S.A.R.