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 - See Vaccines

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