Joining the Group

I am always happy to hear from prospective students and researchers who are excited about statistical machine learning. Beyond technical background, I most value curiosity, persistence in working through hard problems, and the ability to communicate ideas clearly and work well with others in the group. Specific openings are listed below; if none of them fit but you think there’s a good match with my research interests, feel free to reach out anyway.

• PhD Student open

  Fully-funded PhD positions in statistical machine learning.

  I am recruiting PhD students to join my group in the Department of Statistics and Data Science at NUS. Research directions include tree-based methods and ensembles, Bayesian machine learning, interpretability, prior-data fitted networks, and agentic data science, though students with their own related interests are welcome to discuss other directions.

  Deadline: See NUS Department of Statistics and Data Science PhD admissions portal for deadlines

  How to apply: Apply through the NUS Department of Statistics and Data Science PhD admissions portal, and also feel free to email me directly with your CV, transcript, and a short note on your research interests.

• Postdoctoral Research Scholar open

  Two-year postdoc position in Bayesian machine learning for healthcare, funded by an NUS Cross Faculty Grant.

  I am hiring a postdoctoral research scholar for a two-year appointment to develop machine learning solutions for healthcare problems, including dynamic treatment regimes, mobile health, and trial simulations. The position is funded by an NUS Cross Faculty Grant on personalized healthcare via Bayesian machine learning, and the successful candidate will be jointly mentored by myself and Bibhas Chakraborty (Duke-NUS Medical School).

  The work will draw on Bayesian machine learning techniques, including Prior-Data Fitted Networks (PFNs), Bayesian Additive Regression Trees (BART), and generative methods for tabular data.

  Day-to-day work spans software development, numerical experimentation with deep learning, proving theorems, academic writing, and communicating with medical collaborators. The postdoc will also mentor PhD, master's, and undergraduate students.

  Ideal candidates have a PhD in statistics, computer science, applied mathematics, or a related field, with a deep understanding of statistical and computational principles.

  Deadline: Open until filled

  How to apply: Please email me directly with your CV, a brief research statement, a representative publication, and contact details for two references.