LATEST NEWS

27 Jul. 2026: one paper accepted at Transactions on Knowledge and Data Engineering (Q1) 

02 May 2026: two papers accepted at ICML 2026 (A* ranked), top conference in Machine Learning

27 Jan. 2026: two papers accepted at ICLR 2026 (A* ranked), top conference in Deep Learning

23 Dec. 2025: I am proud to receive two prestigious honors from Deakin University: Research and Innovation Award and 10 Years Service Milestone Award

15 May 2025: our paper got accepted at KDD 2025 conference (A* ranked), top conference in Data Mining

06 Sep. 2024: one paper got accepted at ICDM 2024 conference (A* ranked), top conference in Data Mining

01 May 2024: I am promoted to Research Lecturer at Deakin University from March 2024

15 Sep. 2022: one paper got accepted at NeurIPS 2022 conference (A* ranked), top-1 conference in Machine Learning

10 Mar. 2022: I am delighted with my new position “Alfred Deakin Postdoctoral Research Fellow” along with a research support grant of $15,000

18 Dec. 2020: my optimization algorithm [source code] ranked the 4th place on the “warm start friendly” leaderboard of the Black-box optimization (BBO) competition organized by NeurIPS 2020 conference: https://bbochallenge.com/altleaderboard

01 Jun. 2020: our paper gets accepted at ICML 2020 conference (A* ranked)

11 Nov. 2019: our paper is accepted at AAAI 2020 conference (A* ranked)

08 Feb. 2019: my thesis is shortlisted for the Alfred Deakin Medal for Doctoral Thesis

12 Sep. 2018: my paper receives the Best Student Machine Learning Paper Runner Up Award at ECML-PKDD 2018 conference (A ranked)

RESEARCH INTERESTS

Data Mining, Machine Learning, Tabular Data

EDUCATION

PhD in Data Science, 05/2015 – 10/2018
Deakin University, Geelong, Australia

MSc in Computer Science, 2006 – 2009
University of Information Technology (Vietnam National University), HCMC, Vietnam

BSc in Mathematics and Computer Science, 2001 – 2005
University of Science (Vietnam National University), HCMC, Vietnam

SELECTED PUBLICATIONS (Google scholar, ORCID)
(21 A*/A-conference papers, 13 Q1-journal papers)

International conference papers

  1. Dang Nguyen*, Tu Anh Hoang Nguyen*, Thuc Duy Le, Svetha Venkatesh, Trung Le, Sunil Gupta (2026). Causal-aware Anomaly Detection for Tabular Data. ICML (A* ranked conference) (* equivalent first authors)
  2. Hoang Tran Vuong, Linh Ngo Van, Dang Nguyen, Thin Nguyen, Phuoc Nguyen, Mehrtash Harandi, Trung Le (2026). f-Divergence Self-Play for Tabular Anomaly Detection via Large Language Models. ICML (A* ranked conference)
  3. Tri-Nhan Vo, Dang Nguyen, Sunil Gupta (2026). TAKE: Trajectory-Aware Knowledge Estimation for Text Dataset Distillation. ECML-PKDD (A ranked conference)
  4. Giang Ngo, Dat Phan-Trong,  Dang Nguyen, Sunil Gupta, Svetha Venkatesh (2026). Adaptive Acquisition Selection for Bayesian Optimization with Large Language Models. ICLR (A* ranked conference)
  5. Duc Kieu, Kien Do, Tuan Hoang, Thao Le, Tung Kieu, Dang Nguyen, Thin Nguyen (2026). Universal Multi-Domain Translation via Diffusion Routers. ICLR (A* ranked conference) 
  6. Giang Ngo, Dat Phan-Trong,  Dang Nguyen, Sunil Gupta (2026). High-dimensional Level Set Estimation with Trust Regions and Double Acquisition Functions. AISTAT (A ranked conference)
  7. Duc Kieu, Kien Do, Toan Nguyen, Dang Nguyen, Thin Nguyen (2025). Bidirectional Diffusion Bridge Models. KDD (A* ranked conference, acceptance rate = 23.4%)
  8. Dang Nguyen, Sunil Gupta, Kien Do, Thin Nguyen, Svetha Venkatesh (2024). Generating Realistic Tabular Data with Large Language Model. ICDM (A* ranked conference, acceptance rate = 19.5%) [Source code]
  9. Kien Do, Dung Nguyen, Hung Le, Thao Le, Dang Nguyen, Haripriya Harikumar, Truyen Tran, Santu Rana, Svetha Venkatesh (2024). Revisiting the Dataset Bias Problem from a Statistical Perspective. ECAI (A ranked conference)
  10. Vo Tri-Nhan, Dang Nguyen, Kien Do, Sunil Gupta (2024). Improving Diversity in Black-box Few-shot Knowledge Distillation. ECML-PKDD (A ranked conference)
  11. Kien Do, Hung Le, Dung Nguyen, Dang Nguyen, Haripriya, Harikumar, Truyen Tran, Santu Rana, Svetha Venkatesh (2022). Momentum Adversarial Distillation: Handling Large Distribution Shifts in Data-Free Knowledge Distillation. NeurIPS (A* ranked conference, acceptance rate = 2672/10411 = 25.6%)
  12. Dang Nguyen, Sunil Gupta, Kien Do, Svetha Venkatesh (2022). Black-box Few-shot Knowledge Distillation. ECCV (A* ranked conference, acceptance rate = 1650/5803 = 28%) [Source code]
  13. Kien Do, Haripriya Harikumar, Hung Le, Dung Nguyen, Truyen Tran, Santu Rana, Dang Nguyen, Willy Susilo, Svetha Venkatesh (2022). Towards Effective and Robust Neural Trojan Defenses via Input Filtering. ECCV (A* ranked conference, acceptance rate = 1650/5803 = 28%)
  14. Dang Nguyen, Sunil Gupta, Trong Nguyen, Santu Rana, Phuoc Nguyen, Truyen Tran, Ky Le, Shannon Ryan, Svetha Venkatesh (2021). Knowledge Distillation with Distribution Mismatch. ECML-PKDD (A ranked conference)
  15. Phuoc Nguyen, Truyen Tran, Ky Le, Sunil Gupta, Santu Rana, Dang Nguyen, Trong Nguyen, Shannon Ryan, Svetha Venkatesh (2021). Fast Conditional Network Compression Using Bayesian HyperNetworks. ECML-PKDD (A ranked conference)
  16. Thomas Patrick Quinn*, Dang Nguyen*, Santu Rana, Sunil Gupta, Svetha Venkatesh (2020). DeepCoDA: personalized interpretability for compositional health data. ICML (A* ranked conference, acceptance rate = 1088/4990 = 21.8%) (* equivalent first authors) [Source code]
  17. Phuc Luong, Dang Nguyen, Sunil Gupta, Santu Rana, Svetha Venkatesh (2020). Bayesian Optimization with Missing Inputs. ECML-PKDD (A ranked conference)
  18. Dang Nguyen, Sunil Gupta, Santu Rana, Alistair Shilton, Svetha Venkatesh (2020). Bayesian Optimization for Categorical and Category-Specific Continuous Inputs. AAAI (A* ranked conference, acceptance rate = 1591/8800 = 20.6%) [Source code]
  19. Dang Nguyen, Wei Luo, Tu Dinh Nguyen, Svetha Venkatesh, Dinh Phung (2018). Sqn2Vec: Learning Sequence Representation via Sequential Patterns with a Gap Constraint. ECML-PKDD (Best Student Machine Learning Paper Runner Up Award) (A ranked conference) [Source code]
  20. Dang Nguyen, Tu Dinh Nguyen, Wei Luo, Svetha Venkatesh (2018). Trans2Vec: Learning Transaction Embedding via Items and Frequent Itemsets. PAKDD (A ranked conference)
  21. Dang Nguyen, Wei Luo, Tu Dinh Nguyen, Svetha Venkatesh, Dinh Phung (2018). Learning Graph Representation via Frequent Subgraphs. SDM (A ranked conference) [Source code]

International journal papers

  1. Henry Simmons, Dang Nguyen, Benjamin Misiuk, Daniel Ierodiaconou, Sunil Gupta, Oli Dalby, Mary Young (2025). Comparing Convolutional Neural Network and Random Forest for Benthic Habitat Mapping in Apollo Marine Park. Remote Sensing in Ecology and Conservation (Q1)
  2. Deepthi Kuttichira, Sunil Gupta, Dang Nguyen, Santu Rana, Svetha Venkatesh (2022). Verification of integrity of deployed deep learning models using Bayesian optimization. Knowledge-Based Systems, 241, 108238 (Q1) [Source code]
  3. Dang Nguyen, Wei Luo, Bay Vo, Loan T.T. Nguyen, Witold Pedrycz (2021). Con2Vec: Learning Embedding Representations for Contrast Sets. Knowledge-Based Systems, 229, 107382 (Q1) [Paper]
  4. Dang Nguyen, Sunil Gupta, Santu Rana, Alistair Shilton, Svetha Venkatesh (2021). Fairness Improvement for Black-box Classifiers with Gaussian Process. Information Sciences, 576, 542-556 (Q1) [Paper] [Source code]
  5. Phuc Luong, Dang Nguyen, Sunil Gupta, Santu Rana, Svetha Venkatesh (2021). Adaptive Cost-aware Bayesian Optimization. Knowledge-Based Systems, 232, 107481 (Q1)
  6. Dang Nguyen, Wei Luo, Bay Vo, Witold Pedrycz (2020). Succinct Contrast Sets via False Positive Controlling with an Application in Clinical Process Redesign. Expert Systems with Applications, 161, 113670-113687 (Q1) [Paper]
  7. David Rubín de Celis Leal*, Dang Nguyen*, Pratibha Vellanki*, Cheng Li, Santu Rana, Nathan Thompson, Sunil Gupta, Keiran Pringle, Surya Subianto, Svetha Venkatesh, Teo Slezak, Murray Height, Alessandra Sutti (2019). Efficient Bayesian Function Optimization of Evolving Material Manufacturing Processes. ACS Omega, 4, 20571-20578 (Q1) (* equivalent first authors)
  8. Dang Nguyen, Wei Luo, Dinh Phung, Svetha Venkatesh (2018). LTARM: A novel temporal association rule mining method to understand toxicities in a routine cancer treatment. Knowledge-Based Systems, 161, 313-328 (Q1) [Source code]
  9. Dang Nguyen, Loan T.T. Nguyen, Bay Vo, Witold Pedrycz (2016). Efficient mining of class association rules with the itemset constraint. Knowledge-Based Systems, 103, 73-88 (Q1)
  10. Dang Nguyen, Bay Vo, Duc-Lung Vu (2016). A Parallel Strategy for the Logical-probabilistic Calculus-based Method to Calculate Two-terminal Reliability. Quality and Reliability Engineering International, 32(7), 2313–2327 (Q1)
  11. Dang Nguyen, Loan T.T. Nguyen, Bay Vo, Tzung-Pei Hong (2015). A Novel Method for Constrained Class Association Rule Mining. Information Sciences, 320, 107-125 (Q1)
  12. Dang Nguyen, Bay Vo, Bac Le (2015). CCAR: An efficient method for mining class association rules with itemset constraints. Engineering Applications of Artificial Intelligence, 37, 115–124 (Q1)
  13. Dang Nguyen, Bay Vo, Bac Le (2014). Efficient strategies for parallel mining class association rules. Expert Systems with Applications, 41(10), 4716-4729 (Q1)

RESEARCH EXPERIENCE

  • Research Lecturer, 03/2024 – present
    Applied Artificial Intelligence Institute (A2I2), Deakin University, Geelong, Australia
  • Alfred Deakin Postdoctoral Research Fellow, 03/2022 – 03/2024
    Deakin University, Geelong, Australia
  • Research Fellow, 01/2022 – 03/2022
    Applied Artificial Intelligence Institute (A2I2), Deakin University, Geelong, Australia
  • Associate Research Fellow, 06/2018 – 12/2021
    Applied Artificial Intelligence Institute (A2I2), Deakin University, Geelong, Australia
  • Research Assistant, 06/2017 – 08/2017
    School of Exercise and Nutrition Sciences, Deakin University, Geelong, Australia
  • Research Fellow, 08/2014 – 08/2017
    Division of Data Science, Ton Duc Thang University, HCMC, Vietnam

AWARDS AND SCHOLARSHIPS

  • Vice-Chancellor’s 10 Years Service Milestone Award, Deakin University, Australia. 12/2025
  • Vice-Chancellor’s Award for Research Partner Engagement, Deakin University, Australia. 12/2025
  • Alfred Deakin Postdoctoral Research Fellowship, Deakin University, Australia, 03/2022
  • Shortlisted for the Alfred Deakin Medal for Doctoral Thesis, Deakin University, Australia, 02/2019
  • Best Student Machine Learning Paper Runner Up Award, The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Ireland, 09/2018
  • Certificate of Outstanding Reviewer, Knowledge-Based Systems, Elsevier, 05/2018
  • Student Travel Award, The SIAM International Conference on Data Mining, USA, 05/2018
  • Student Travel Award, The 29th Australasian Joint Conference on Artificial Intelligence, Australia, 12/2016
  • Postgraduate Research Scholarship, Deakin University, Australia, 05/2015
  • Extra Mile Award, U.S. Consulate General, Vietnam, 04/2015
  • Franklin Award, U.S. Consulate General, Vietnam, 10/2012

PHD STUDENT SUPERVISION

  • Current:
    1. Tu Anh Hoang Nguyen (commenced in 2026)
      Principal supervisor, research topic: Generative Models for Tabular Data
    2. Vo Tri Nhan (commenced in 2022)
      Principal supervisor, research topic: Knowledge and Data Distillation
  • Past:
    1. Ngo Nam Giang (commenced in 2022)
      Associate supervisor, research topic: Level Set Estimation
    2. Azhar Mohammed (commenced in 2021)
      Associate supervisor, research topic: Causal Reasoning
    3. Luong Huu Phuc (completed in 2021)
      Associate supervisor, research topic: Bayesian Optimization
    4. Deepthi Praveenlal Kuttichira (completed in 2021)
      Associate supervisor, research topic: Deep Learning Deployment

CONTACT DETAILS

Dang Nguyen

Name: Dang Nguyen

Email: nguyenphamhaidang [at] outlook.com

Last update: 28 July 2026