Mengyan Zhang

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Lecturer (Assistant Professor), School of Computer Science, University of Bristol;
Visiting Researcher, Department of Computer Science, University of Oxford;
Member of Machine Learning and Global Health Network.
Email: mengyan.zhang@bristol.ac.uk | mengyan.zh@outlook.com
[CV|Google Scholar|Github| Twitter|Linkedin]

I am Mengyan Zhang (张梦妍), a lecturer (Assistant Professor) at the university of Bristol. Before that, I was a postdoctoral researcher at the University of Oxford, working with Prof. Seth Flaxman. I received my PhD at the Australian National University in 2023, under the supervision of Dr. Cheng Soon Ong, Prof. Lexing Xie and Prof. Eduardo Eyras. During my PhD, I was affiliated with Data61, CSIRO and interned at Microsoft Research Asia. I obtained my bachelor’s degree with first-class honours at the Australian National University and bachelor’s degree at Shandong University.

My research interests are sequential decision-making in machine learning, including Reinforcement learning, Bayesian optimisation and active learning. I work on both theoretical and practical views of experimental design with two goals: (I) Designing robust algorithms to handle imperfect feedback and understand causal relationships in sequential decision-making. (II) Designing decision-making algorithms to solve real-world problems in various areas, for example, synthetic biology, disease surveillance, survey design, and public policy.

Hiring

I’m looking for highly motivated Ph.D. students who are excited about working on sequential decision making and its applications in health. A strong background in machine learning, statistics, or a related field would be ideal. If this sounds like you, I’d love to hear from you — please send me your CV, transcript, and a short paragraph about your research experience and interests.

Research & Publications/Preprint

  • Artificial intelligence for modelling infectious disease epidemics.
    Moritz U. G. Kraemer, Joseph L.-H. Tsui, Serina Y. Chang, Spyros Lytras, Mark P. Khurana, Samantha Vanderslott, Sumali Bajaj, Neil Scheidwasser, Jacob Liam Curran-Sebastian, Elizaveta Semenova, Mengyan Zhang et al (2025). {Nature}

  • Indirect Query Bayesian Optimization with Integrated Feedback.
    Mengyan Zhang, Shahine Bouabid, Cheng Soon Ong, Seth Flaxman, Dino Sejdinovic (2025). {pre-print}

  • Scalable Spatiotempora l Inference with Biased Scan Attention Transformer Neural Processes.
    Daniel Jenson, Jhonathan Navott, Piotr Grynfelder, Mengyan Zhang, Makkunda Sharma, Elizaveta Semenova, Seth Flaxman. {pre-print}

  • Optimal disease surveillance with graph-based Active Learning.
    Joseph L-H Tsui * , Mengyan Zhang * , Prathyush Sambaturu, Simon Busch-Moreno, Marc A Suchard, Oliver G Pybus, Seth Flaxman, Elizaveta Semenova, Moritz UG Kraemera. {PNAS, epiDAMIK-KDD workshop 2024}

  • Graph Agnostic Causal Bayesian Optimisation.
    Sumantrak Mukherjee * , Mengyan Zhang * , Seth Flaxman, Sebastian Josef Vollmer (2024). NeurPIS Bayesian Decision-making and Uncertainty Workshop.

  • PhD Thesis: Adaptive Recommendations with Bandit Feedback {ANU Open Research Library} (supervisors: Cheng Soon Ong, Lexing Xie, Eduardo Eyras) - Award: CORE Distinguished Dissertation Award Commendation

  • Two-Stage Neural Contextual Bandits for Personalised News Recommendation.
    Mengyan Zhang, Thanh Nguyen-Tang, Fangzhao Wu, Zhenyu He, Xing Xie, Cheng Soon Ong. Under Review. {pre-print}

  • Gaussian Process Bandits with Aggregated Feedback.
    Mengyan Zhang, Russell Tsuchida, Cheng Soon Ong. AAAI 2022. {pre-print; code; poster; one-page abstract}

  • Machine learning guided batched design of a bacterial Ribosome Binding Site.
    Mengyan Zhang, Maciej Bartosz Holowko, Huw Hayman Zumpe, Cheng Soon Ong. ACS Synthetic Biology Journal 2022. {paper; C3DIS 2020 Talk; SEED 2021 Talk}

  • Quantile Bandits for Best Arms Identification.
    Mengyan Zhang, Cheng Soon Ong. International Conference on Machine Learning 2021. {paper; code; poster; talk}

  • Opportunities and Challenges in Designing Genomic Sequences.
    Mengyan Zhang, Cheng Soon Ong. ICML Workshop on Computational Biology 2021. {paper; poster; talk}

  • Active Learning on Knowledge Graph. {software; flowchart; design}

  • Honours project: Classification of historical death and occupation coding {thesis} (supervisors: Peter Christen, Timothy Graham)

Awards & Funding & Scholarship

  • 2024 Award for Excellence at Oxford [top 10%]
  • 2024 CORE Distinguished Dissertation Award Commendation (PhD thesis)
  • 2024 NCCR Automation fellowship (Visiting ETH, up to CHF 18k)
  • 2019 Data61 Top-up Postgraduate Research Scholarship
  • 2018 PhD Scholarship of ANU
  • 2018 ANU HDR Fee Remission Merit Scholarship
  • 2017 Paul Thistlewatte Memorial Honours Year Scholarship of ANU
  • 2015-2016 National Scholarship (China)

Teaching & Supervision

Service

  • Reviewer for NeurIPS 2023, AAAI2024, ICLR2024, ICML2025.

Talks & Presentations

  • Dec. 2025 CFECMStatistics Conference, King’s College London, London, UK
    Sequential decision-making in public health.
  • Feb. 2024: LAS Group, ETH Zurich, Switzerland
    slides: Sequential Decision-Making: Theory and Applications in Public Health
  • Dec. 2023: Google DeepMind, London
    Design Choices in Sequential Decision-Making with Bandit Feedback 
  • Nov. 2023: AIMS seminar, Oxford
    Sequential decision making in public health
  • Nov. 2023: Bayes@CIRM Workshop, Marseille, France
    Bayesian optimisation with aggregated feedback
  • Jul. 2023: University of Adelaide ADSC Seminar
    Sequential Decision-making: Theory and Applications
  • Jun. 2022: ANU AI+ML+Friends seminar (PhD Completion Talk)
    slides: Adaptive Recommendations with Bandit Feedback
  • Feb. 2022: Microsoft Research Asian Social Computing Group Seminar
    slides: Bandits in Recommendation System
  • Jan. 2022: Microsoft News and Feeds Group
    slides: Best arm identifications: classical settings and methods
  • Dec. 2021: WiML workshop in NeurIPS
    Poster presentation: Gaussian Process Bandits with Aggregated Feedback
  • Jul. 2021: ICML Workshop on Computational Biology
    Spotlight Talk: Opportunities and Challenges in Designing Genomic Sequences
  • Jul. 2021: Thirty-ninth International Conference on Machine Learning
    Poster presentation: Quantile Bandits for Best Arms Identification
  • Jul. 2020: Machine Learning Summer School (acceptance rate: 13.84%)
    Poster presentation: Quantile Bandits for Best Arms Identification
  • Dec. 2019: Collaborative Conference on Computational and Data Intensive Science
    Talk: Optimized Experimental Design for Translation Initiation using Machine Learning

Visit & Conferences