I am a Ph.D. in Computer Science with a focus on computational biology and applied machine leanring. I obtained my PhD training at McGill University under the supervision of Prof. Mathieu Blanchette. I will join Data Science and Analytics Thrust at the Hong Kong University of Science and Technology (Guangzhou) as an assistant professor. I received my bachelor’s degree from the City University of Hong Kong (2015). Prior to studying at McGill University, I worked as a research assistant (intern) at Indiana University (2013-2014), and the City University of Hong Kong (2015-2017). For more information, please check my CV.

Openings: I have multiple fully-funded Ph.D. positions (Spring/Fall) and research assistant/intern positions available at HKUST(GZ). In particular, I am looking for students who are interested in computational biology and machine learning to work in the following directions:

• Computational 3D genomics
• Data integration via multi-modal learning
• Interpretable machine learning for (epi)genome data analysis
• Representation learning approaches in biological data analysis

If you have a degree in physics or similar programs and want to do research in computational biology and machine learning, you can also contact me to discuss possible options in the following directions:

• 3D genome modeling (i.e., Physics-informed GNN for 3D genome modeling, etc.)

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. This includes:

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

Selected Publications

• 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)

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.