Louis Tiao

Louis Tiao

Machine Learning Researcher (PhD Candidate)

University of Sydney

CSIRO's Data61


Louis Tiao is a machine learning researcher and PhD candidate at the University of Sydney. His research interests are in probabilistic machine learning, and specifically includes approximate Bayesian inference, Gaussian processes, and its applications to areas such as Bayesian optimization and graph representation learning.

  • Probabilistic Machine Learning
  • Approximate Bayesian Inference
  • Gaussian Processes
  • Ph.D. in Computer Science (Machine Learning), 2022 (expected)

    University of Sydney

  • B.Sc. (Honours 1st Class) in Computer Science (Artificial Intelligence and Mathematics), 2015

    University of New South Wales


Research Experience

Amazon Web Services
Applied Scientist Intern
Amazon Web Services
Jun 2022 – Present Cambridge, United Kingdom
Doctoral Placement Researcher
Oct 2021 – May 2022 Cambridge, United Kingdom
I was a Student Researcher at Secondmind (formerly known as PROWLER.io), a machine learning technology start-up based in Cambridge, UK with a strong focus on basic research and an excellent track record of scientific publications in the areas of Gaussian processes, variational inference, and Bayesian optimization (you may know them by their popular open-source library, GPFlow).
Amazon Web Services
Applied Scientist Intern
Amazon Web Services
Jun 2019 – Dec 2019 Berlin, Germany
I spent the Summer-Fall of 2019 at Amazon in Berlin, Germany, conducting research in the area of AutoML in contribution to the Automatic Model Tuning service on AWS SageMaker. I was fortunate to been given the opportunity to work with eminent researchers in the field, Matthias Seeger, Aaron Klein, and Cédric Archambeau. Together, we tackled the challenges of extending multi-fidelity Bayesian optimization with asynchronous parallelism. The research developed during my internship culminated in a research paper and the release of our code as part of the open-source AutoGluon library.
CSIRO's Data61
Software Engineer
Jul 2016 – Apr 2019 Sydney, Australia

After NICTA was subsumed under CSIRO (Australia’s national science agency), I continued as a member of the Inference Systems Engineering team, working to apply probabilistic machine learning to a multitude of problem domains, including spatial inference and Bayesian experimental design, with an emphasis on scalability. In this time, I led the design and implementation of new microservices and contributed to the development of open-source libraries for large-scale Bayesian deep learning.

During this period, I also served a brief stint with the Graph Analytics Engineering team (the team behind StellarGraph), where I contributed to research into graph representation learning from a probabilistic perspective. These efforts culminated in a research paper that went on to be awarded a spotlight presentation at the field’s premier conference.

National ICT Australia (NICTA)
Software Engineer
National ICT Australia (NICTA)
May 2015 – Jun 2016 Sydney, Australia
As a software engineer with a specialization in machine learning, I was a member of a team of machine learning researchers and engineers engaged in an interdisciplinary collaboration with leading researchers from multiple areas of the natural sciences, as part of the Big Data Knowledge Discovery initiative sponsored by the Science Industry Endowment Fund (SIEF). During this time I helped lead the development and release of numerous open-source libraries for applying Bayesian machine learning at scale.
Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Research Intern
Nov 2013 – Feb 2014 Sydney, Australia
I joined the CSIRO’s Language and Social Computing team as a Summer Vacation Scholar for the summer of 2013-14 and worked on applying machine learning and natural language processing (NLP) techniques to develop a text classification system for automated sentiment analysis.


Recent & Upcoming Talks


COMP9418: Advanced Topics in Statistical Machine Learning (UNSW Sydney)

The course has a primary focus on probabilistic machine learning methods, covering the topics of exact and approximate inference in directed and undirected probabilistic graphical models - continuous latent variable models, structured prediction models, and non-parametric models based on Gaussian processes.

Lab exercise on Gaussian Process Regression, running in JupyterLab.
Lab exercise on Gaussian Process Regression, running in JupyterLab.

This course has a major emphasis on maintaining a good balance between theory and practice. As the teaching assistant (TA) for this course, my primary responsibility was to create lab exercises that aid students in gaining hands-on experience with these methods, specifically applying them to real-world data using the most current tools and libraries. The labs were Python-based, and relied heavily on the Python scientific computing and data analysis stack (NumPy, SciPy, Matplotlib, Seaborn, Pandas, IPython/Jupyter notebooks), and the popular machine learning libraries scikit-learn and TensorFlow.

Students were given the chance to experiment with a broad range of methods on various problems, such as Markov chain Monte Carlo (MCMC) for Bayesian logistic regression, probabilistic PCA (PPCA), factor analysis (FA) and independent component analysis (ICA) for dimensionality reduction, hidden Markov models (HMMs) for speech recognition, conditional random fields (CRFs) for named-entity recognition, and Gaussian processes (GPs) for regression and classification.


Quickly discover relevant content by filtering publications.
(2022). Batch Bayesian Optimisation via Density-ratio Estimation with Guarantees. In NeurIPS2022.

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(2021). BORE: Bayesian Optimization by Density-Ratio Estimation. In ICML2021. Accepted as Long Presentation (Awarded to Top 3% of Papers).

PDF Cite Code Poster Slides Video Conference Proceeding Supplementary material

(2020). Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings. In NeurIPS2020. Accepted as Spotlight Presentation (Awarded to Top 3% of Papers).

PDF Cite Code Dataset Video

(2020). Model-based Asynchronous Hyperparameter and Neural Architecture Search.

PDF Cite Code Video

(2018). Cycle-Consistent Adversarial Learning as Approximate Bayesian Inference. In ICML2018 Theoretical Foundations and Applications of Deep Generative Models. Accepted as Contributed Talk..

PDF Cite Code Poster Slides Workshop Homepage


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