Dr Vu Nguyen

Work Experience

• Senior Machine Learning Scientist. 07/2024 – now
  Amazon, Australia
• Machine Learning Scientist. 12/2020 – 06/2024
  Amazon, Australia
• Senior Research Associate. 01/2019 – 11/2020
  Project: Machine learning for quantum technologies
  Machine Learning Research Group, University of Oxford, UK
• Research Scientist (short-term contract). 11/2018 – 1/2019
  Project: Deep reinforcement learning for marketing
  Credit AI - Trusting Social, Melbourne, Australia
• Associate Research Fellow. 06/2015 – 10/2018
  Project: Machine learning and Bayesian optimization for experimental design
  Center for Pattern Recognition and Data Analytics, Deakin University, Geelong, Australia

Education

• Doctor of Philosophy. 02/2012-12/2015:
  Deakin University. Supervisors: Prof Dinh Phung and Prof Svetha Venkatesh
  Thesis: Bayesian Nonparametric Multilevel Modeling and Applications.
• Bachelor of Science (First Class Honour) of Information Technology. 09/2007-09/2011:
  University of Science - Vietnam National University - HCMC. Supervisor: Prof Bac Le
  Thesis: Interactive Image Segmentation using Graph-cut.

Awards

• 2nd Winner, AutoML competition, Jul 2022
• Best Paper Award, PLOS Computational Biology, Jul 2022
• Selected as a Postdoc-NeT-AI Fellow, DAAD, Germany, Oct 2020
• Young Investigator Training Program Grant, Italy, June 2019
• 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, Bayesian Optimization of Superalloy Design. 4th World Congress on Integrated Computational Materials Engineering 2017
• Best Paper Runner up Award and Best Poster Award - ACML 2016, Bayesian Nonparametric Multi-label Classification.
• Heidelberg Laureate Forum 2015, Top 200 young scientists around the world to interact with the Laureates in Germany
• First Prize in Student Research Competition 2011, University of Science, Vietnam National University HCM
• Travel Grant - Machine Learning Summer School, Singapore, 2011.
• Lawrence Sting Award and Scholarship Awarded for Vietnamese Outstanding Students, 2010

Invited Talks and Tutorials

• Mar 22. Applied Machine Learning Conference — Bayesian Optimization with Categorical and Continuous Variables.
• Nov 21. ACEMS. Invited speaker and panel discussion — Tales of the two worlds: Navigating between industry and academia.
• Apr 21. Amazon - Indian Machine Learning — Advanced Bayes Opt for Machine Learning Hyperparameters Tuning.
• Oct 20. ACML 2020 - Tutorial — Recent advances in Bayesian optimization.
• Jun 20. University of Liverpool, UK — Recent advances in Bayesian optimization.
• Jun 20. Oxford-Man Institute 11st Machine Learning Workshop — Controlling quantum device measurement using DRL.
• Jun 20. University of Twente, The Netherlands — Recent advances in Bayesian optimization.
• Jun 20. University of Glasgow, UK — Recent advances in Bayesian optimization.
• Dec 19. VinAI Research, VN — Knowing the what but not the where in Bayesian optimization.
• Nov 19. University of Oxford, Dept of Materials — DRL for controlling quantum device.
• Aug 19. University of Melbourne, Aus — Machine learning for decision making under uncertainty.
• Jun 19. University of Roma, Italy — Bayesian optimization for accelerating hyperparameter tuning.
• May 19. University of Southampton, UK — Machine learning for decision making under uncertainty.
• Apr 19. Oxford-Man Institute on Quantitative Finance — Deep reinforcement learning for marketing.
• Oct 18. Credit AI, Australia — Bayesian optimization for experimental design.
• Dec 17. Deakin-Defense Science Technology Workshop — Bayesian optimization for hyper-parameter tuning.

Grant and Funding

• Google Cloud Platform Education Grant, Aug 2020-2021. $5000 USD
• Young Investigator Training Program Grant, Italy, June 2019. €3000
• NVIDIA, GPU Grant. 2019
• Networked Quantum Information Technologies Hub (NQIT). Machine Learning for Quantum Technologies. 2018-2019. £105,978
  Named Researcher.
• Telstra Grant - COE in Big Data and Machine Learning. 2016-2019. $1,600,000 AUD
  With PRADA team at Deakin University.

Teaching

• Teaching Assistant. Course: Introduction to Programming. Undergraduate Course, Fall 2011. University of Science - VNU HCMC
• Teaching Assistant. Course: Advanced Machine Learning. Master Course, Trinity Term 2019. University of Oxford
• Guest Lecturer. Course: Introduction to Machine Learning. Undergraduate Course, Fall 2011. University of Science - VNU HCMC
• Guest Lecturer. Course: Advanced Data Science. Undergraduate Course, Spring 2018. Deakin University
• Lecturer. Course: Data Estimation and Inference. CDT AIMS. Postgraduate Course, Michaelmas Term 2019. University of Oxford
• Senior Demonstrator. Course: Practical Gaussian Process and Bayesian Optimisation. Postgraduate Course, Hilary Term 2020. University of Oxford

Mentorship

• Julian Berk. PhD at Deakin University. Associate Supervisor. 2017-2019.
  Topic: Methods for Improving Bayesian Optimization through Thompson Sampling.
• Samuel Kessler. DPhil at University of Oxford. Informal Supervisor. 2019-2020
  Topic: Indian Buffet Neural Networks for Continual Learning.
• Labiba Noorain. Master Thesis. University College London. Co-supervisor. Jun 2020 - Oct 2020
  Topic: A machine learning approach for polymer nanoparticles and metal nanoparticles designed in antiviral drug delivery.
• Xinglong Wang. Undergraduate - EUROP summer placement at University of Oxford. Main supervisor. Jun 2020 - Dec 2020
  Topic: Interpretable Gaussian Process.
• Xinqi Zhu. Applied Scientist Intern at Amazon. Main supervisor. Apr 2022 - Feb 2023
• Alexander Soen. Applied Scientist Intern at Amazon. Main supervisor. Mar 2023 - Oct 2023
• Yixin Liu. Applied Scientist Intern at Amazon. Main supervisor. Jul 2023 - Jan 2024

Publications

• See Publications

Professional Service

Technical Program Committee

• NeurIPS '17,'19,'20
• ICML '19,'20,'21
• ICLR '19,'20,'21,'22
• AAAI '20,'21,'22
• AISTATS '20,'21,'22
• IJCAI '18,'19,'20
• ACML '18,'19,'20
• ICDM '18,'19,'20

Senior Technical Program Committees / Area Chairs

• AAAI '22
• AISTATS '23, '24, '25

Organizing Committees

• Journal Track Co-chair ACML '23
• AutoML workshop at ICML '24
• Program Co-chairs ACML '24

References

• Available Upon Request