Dr Vu Nguyen

I am a Senior Machine Learning Scientist at Amazon Australia. Prior to this appointment, I was a Senior Research Associate in Machine Learning at University of Oxford, working with Prof. Mike Osborne in Machine Learning Research Group and Prof. Andrew Briggs. Before that, I was a Research Scientist at Credit AI - Trusting Social and an Associate Research Fellow at PRADA, Deakin University with ARC Laureate Prof Svetha Venkatesh. I obtained my PhD at Deakin University where I am very fortunate to be advised by Prof. Dinh Phung and Prof. Svetha Venkatesh in Dec 2015.

Research Interests

Bayesian Optimization, Gaussian Process, Deep Reinforcement Learning
Bayesian Nonparametric, Multilevel Modelling

Awards

2nd Winner, AutoML competition, Jul 2022
Best Paper Award, PLOS Computational Biology, Jul 2022
Postdoc-NeT-AI Fellows, DAAD, Germany, Oct 2020
Google Cloud Platform Education Grant, Aug 2020
Travel Grant EEML on Deep Learning and Reinforcement Learning, Romania July 2019
Vice Chancellor Award for Outstanding Contribution, Deakin University 2017
Selected as Best Papers for KAIS - ICDM 2017
Best Poster Award - ICME 2017, 4th World Congress on Integrated Computational Materials Engineering 2017
Best Paper Runner up Award and Best Poster Award - ACML 2016
Heidelberg Laurate Forum 2015, Top 200 young scientist around the world to interact with the Laurates in Germany
First Prize in Student Research Competition 2011, University of Science, Vietnam National University HCM
Travel Grant Machine Learning Summer School, Singapore, 2011.

Recent News

Apr 2025 Attend ICLR 2025 and AISTATS 2025 to present our papers SAVA: Scalable Learning-Agnostic Data Valuation (checkout the code ) and High Dimensional Bayesian Optimization using Lasso Variable Selection (checkout the code )
Mar 2025 Welcome Long Vuong to join us as an Applied Scientist Intern, working on Generative AI for movie recommendation.
Feb 2025 Our paper SAVA: Scalable Learning-Agnostic Data Valuation has been accepted at ICLR 2025 ! We present a scalable method for data valuation.
Jan 2025 Our paper High Dimensional Bayesian Optimization using Lasso Variable Selection has been accepted at AISTATS 2025 !
Dec 2024 Presenting our paper Rejection via Learning Density Ratios at NeurIPS 2024. Exciting!
Dec 2024 Organizing ACML 2024, jointly with Prof Hsuan-Tien Lin as the Program co-Chair.
Oct 2024 Attending CIKM 2024.
Oct 2024 Our paper Rejection via Learning Density Ratios has been accepted at NeurIPS 2024. This work was done by Alex Soen during his internship at Amazon Australia. Congratulations!
Sep 2024 We organize the ACML 2024 in Hanoi this December. Please consider attending!
Aug 2024 Our work Self-Supervision Improves Diffusion Models for Tabular Data Imputation has been accepted at CIKM 2024. Congrats to our Amazon Australia intern Yixin Liu. Check out the code and the youtube video summary.
Jul 2024 Attending as the organizing committee Automated Reinforcement Learning: Exploring Meta-Learning, AutoML, and LLMs workshop at ICML 2024.
Jul 2024 The list of accepted papers and the schedule has been released at the workshop homepage.
May 2024 We will organize the Automated Reinforcement Learning: Exploring Meta-Learning, AutoML, and LLMs workshop at ICML 2024
Apr 2024 Attending ICASSP 2024 in Seoul. With Huy Phan, we present our paper Cross-triggering Issue in Audio Event Detection and Mitigation
Mar 2024 I will serve as the Program Chairs for ACML 2024, jointly with Prof Hsuan-Tien Lin. Please consider submitting your papers at Conference Track and/or Journal Track
Jan 2024 Our paper on Adaptive Batch Sizes for Active Learning: A Probabilistic Numerics Approach has been accepted at the AISTATS 2024.
Nov 2023 Attending ACML 2023 in Istanbul.
Sep 2023 Our paper on Distributionally Robust Bayes Opt has been accepted at the NeurIPS 2023.
Sep 2023 I will serve as the Area Chair for the AISTATS 2024.
Aug 2023 Welcome Yixin Liu to join us as an Applied Scientist Intern.
Jul 2023 Attending ICML 2023 in Hawaii. Let connect if you are around at the conference.
May 2023 I will be the Journal Track Chair at ACML 2023. Please consider submitting your paper for which accepted papers will appear in a special issue of the Springer Machine Learning Journal (MLJ). Deadline 02nd June.
Mar 2023 Welcome Alexander Soen to join us as an Applied Scientist Intern.
Nov 2022 Our work on Mixed-Variable Black-Box Optimisation Using Value Proposal Trees has been accepted at the AAAI 2023.
Aug 2022 I will serve as the Area Chair for the AISTATS 2023.
Jul 2022 We release the open sourced code for the Confident Sinkhorn Allocation (CSA). Check it out
Jul 2022 We release the open sourced code for the Bayesian Generational Population-Based Training. Check it out .
Jul 2022 Attending ICML 2022 and AutoML 2022 in Baltimore. Let connect if you are around at the conferences.
Jul 2022 Our personalised Bayesian optimization for brain-stimulation has received the Best Paper Award at the PLOS Computational Biology.
Jun 2022 The preliminary version of our Confident Sinkhorn Allocation (CSA) paper has been accepted at Distribution-Free Uncertainty Quantification workshop at ICML 2022. We present a new method for pseudo-labeling on tabular data. See you all in Baltimore!
Jun 2022 I am visiting Prof Frank Hutter's group at Freiburg and Prof Sebastian Pokutta's group at TU Berlin, as part of the AInet program. If you are in Freiburg/Berlin and interested in research chat/collaboration, please drop me an email.
May 2022 Our paper Bayesian Generational Population-Based Training has been accepted at ALOE workshop at ICLR 2022 as well as the AutoML 2022 conference. Congratulations to all authors, especially Xingchen Wan.
Apr 2022 I will serve as a reviewer for Journal Machine Learning of Research.
Mar 2022 I am giving an invited talk at Applied Machine Learning Conference.
Mar 2022 Our Automated Reinforcement Learning (AutoRL) paper has been accepted at Journal of AI Research. Congratulations to all authors across universities (Oxford, Freiburg, Leibniz U Hannover) and industry research labs (Google Brain, Facebook AI, Amazon).
Mar 2022 Our paper at Amazon Retrieval Augmented Classification for Long-Tail Visual Recognition has been accepted at CVPR 2022.
Jan 2022 On the collective effort across multiple universities (University of Oxford, University of Freiburg, Leibniz University Hannover) and industry research labs (Google Brain, Facebook AI, Amazon) we release the preprint of AutoRL.
Nov 2021 I am invited to give a talk about Bayesian Optimization with Categorical-Continuous Variables at RIKEN High-dimensional Statistical Modeling Team Seminar
Nov 2021 I am invited to give a talk and panel discussion on navigating between industry and academia at the ACEMS final retreat
Sep 2021 Our paper Tuning Mixed Input Hyperparameters on the Fly for Efficient Population Based AutoRL has been accepted at NeurIPS 2021. We present a new method to optimize the mixed categorical-continuous hyperparameters on the fly for AutoRL.
Sep 2021 Our paper entitled Personalized Closed-Loop Brain Stimulation for Effective Neurointervention Across Participants has been accepted at PLOS Computational Biology 2021. This is a fantastic collaboration with Neuron-science scientist to develop a personalised Bayesian optimization for neurointervention which improves the cognitive ability. We have also filled a patent on this research direction.
Aug 2021 Our paper entitled Bayesian Topic Regression for Casual Discovery has been accepted as the Long paper at EMNLP 2021.
Jul 2021 Hierarchical Indian Buffet Neural Networks for Bayesian Continual Learning has been selected as a Spotlight presentation at UAI 2021.
Jun 2021 Our multi-disciplinary work in automating the measurement of quantum devices using DRL has been accepted at Nature NPJ Quantum Information.
May 2021 Our paper entitled Simulation-based Optimisation to Quantify Heterogeneity of Specific Ventilation and Perfusion in the Lung by the Inspired Sinewave Test has been accepted at Scientific Reports. This is the applied research paper using Bayesian optimisation for studying ventilation and perfusion in human lung.
May 2021 Our paper entitled Hierarchical Indian Buffet Neural Networks for Bayesian Continual Learning has been accepted at UAI 2021 ! Big congratulations to Sam.
May 2021 We got two papers accepted at ICML2021 ! Congratulations to all collaborators. Click here for details.
Apr 2021 I gave a talk "Bayesian Black-box Optimization" at the ECMS, University of Adelaide
Feb 2021 We release a preprint paper addressing the problem of mixed optimization between continuous and categorical variables in high-dimensional space.
Jan 2021 The email vu@robots.ox.ac.uk has expired, please use vu@ieee.org for contacting me. Thanks!.
Jan 2021 Our applied science paper using machine learning and Bayesian optimization for neuron-stimulation has been accepted. This is a collaborated work with experimental psychology scientists.
Dec 2020 I have joined Amazon Research Australia to continue working on cutting edge research for industrial scale.
Dec 2020 Attending and presenting our works at NeurIPS2020 .
Nov 2020 Our Population-based Bandit (PB2) has been included into Ray Tune! Check out the blog post.
Nov 2020 I am giving a tutorial on Bayesian optimization at ACML2020.
Oct 2020 I am selected as a Postdoc-NeT-AI Fellow, DAAD, Germany 2021 (acceptance rate 22/196).
Sep 2020 Three of our papers have been fortunately accepted at NeurIPS2020! Congratulations and thank you to all my fantastic collaborators Vaden Masrani, Rob Brekelmans, Mike Osborne , Frank Wood, Jack Parker-Holder, Stephen Roberts, among many others !
Sep 2020 Our recent work using ML for quantum device fine-tuning has been accepted to New Journal of Physics. Congratulations Nina!
Aug 2020 I am awarded the Google Cloud Platform Education Grant 2020-2021 for accelerating my current research in AutoML.
Jul 2020 Our paper Provably Efficient Online Hyperparameter Optimization with Population-Based Bandits has been selected at Top 3% for the Contributed Talk at AutoML workshop at ICML2020. Congratulations Jack !!!
Jun 2020 Our papers Bayesian Optimization for Iterative Learning and Provably Efficient Online Hyperparameter Optimization with Population-Based Bandits have been accepted at AutoML workshop at ICML2020.
Jun 2020 Our papers have been accepted at ICML2020. Congratulations to all collaborators. Knowing The What But Not The Where in Bayesian Optimization and Bayesian Optimisation over Multiple Continuous and Categorical Inputs .
Feb 2020 Excited to share our recent papers on Bayes Continual Learning and One-shot Bayes Opt.
Jan 2020 I am giving a tutorial in Gaussian Process and Bayesian Optimisation at Quinhon University.
Dec 2019 I am giving a talk on Knowing the what, but not the where in Bayesian Optimization at VinAI Research.
Dec 2019 I am attending NeurIPS 2019.
Nov 2019 I am invited to review for ICML 2020.
Oct 2019 I am excited to teach this year Data Estimation and Inference course CDT AIMS.
Oct 2019 Our paper on Controlling Quantum Device Measurement using Deep Reinforcement Learning has been accepted at Deep Reinforcement Learning workshop at NeurIPS 2019 .
Oct 2019 Congratulations to Sam for his paper being accepted at Bayesian Deep Learning workshop at NeurIPS 2019 .
Aug 2019 I am visiting Australia this month. I am giving talks at University of Melbourne, Monash University and RMIT University.
Aug 2019 Our paper entitled Efficient Bayesian Optimization for Uncertainty Reduction over Perceived Optima Locations has been accepted at ICDM 2019.
Jul 2019 I will serve as a reviewer for ICLR 2020.
Jul 2019 I am attending EEML2019. Looking forward to meeting you all.
Jun 2019 I am awarded the Young Investigator Training Program grant to visit Italian university link
May 2019 I am giving an invited talk at Oxford Aging Institute link
May 2019 Check out our recent work with Mike in exploiting the known optimum value for Bayesian optimization arxiv. In many situations in blackbox optimization and hyper-parameter optimization, we observe the optimum output in advance and the goal is to find the optimum input.
Apr 2019 I am excited to be the recipient of the travel grant to attend EEML on Deep Learning and Reinforcement Learning, Bucharest in July 2019
Feb 2019 I am awarded the NVIDIA GPU grant. Many thanks to NVIDIA for their support.
Jan 2019 I am joining University of Oxford to work on a machine learning for quantum technologies project with Prof. Mike Osborne and Prof. Andrew Briggs
Dec 2018 I will serve as the PC for IJCAI 2019
Oct 2018 I have recently joined Credit AI - Trusting Social (for a 3 months contract) as a Research Scientist working on deep reinforcement learning for finance technology
Sep 2018 Our paper Algorithmic Assurance: An Active Approach to Algorithmic Testing using Bayesian Optimisation has been accepted at NIPS 2018. Congratulations Shiva
Aug 2018 Our paper Accelerating Experimental Design by Incorporating Experimenter Hunches has been accepted at ICDM 2018. Congratulations Cheng
June 2018 Our paper Exploration Enhanced Expected Improvement for Bayesian Optimization has been accepted at ECML 2018. Congratulations Julian
May 2018 Invited to serve as a reviewer for PloS ONE
Dec 2017 Awarded Vice Chancellor Award for Outstanding Contribution with our team
Nov 2017 Attended IEEE ICDM 2017 New Orleans, USA to present the paper entitled Weakly Specified Search Space in Bayesian optimization
Nov 2017 Attended ACML 2017 Seoul, Korea to present the paper entitled Regret for Expected Improvement under Stopping Condition
Aug 2017 Attended IJCAI 2017 Melbourne, Australia to present the paper entitled Discriminative Bayesian Nonparametric Clustering
Dec 2016 Attended NIPS 2016 Workshop on Bayesian Optimization Barcelona, Spain to present our posters.
Dec 2016 Attended IEEE ICDM 2016 Barcelona, Spain to present the paper entitled Budgeted Batch Bayesian Optimization and One-Pass Logistic Regression
Dec 2016 Attended ICPR 2016 Cancun, Mexico to present multiple papers.
Nov 2016 Awarded Best Paper (Runner up) and Best Poster Award for a single paper A Bayesian Nonparametric Approach for Multi-label Classification .
Nov 2016 Attended ACML 2016 Hamilton, Newzeland to present the paper entitled A Bayesian Nonparametric Approach for Multi-label Classification .

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