LATEST NEWS


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

07 Jul. 2022: two papers got accepted at ECCV 2022 conference (A* ranked), top-3 conference in Computer Vision

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

17 Jan. 2022: one paper got accepted at Knowledge-Based Systems journal (Q1, IF: 8.038), which proposes to use Bayesian optimization to detect Trojan attacks in ML models

28 Jun. 2021: one paper got accepted at Information Sciences journal (Q1, IF: 6.795), which proposes to use Gaussian process to improve the fairness of a black-box pre-trained ML classifier

19 Jun. 2021: two papers got accepted at ECML-PKDD 2021 conference (A ranked), which address an emerging topic in machine learning — the compression a deep learning model to a smaller version that is applicable to deploy on edged devices such as smart phones, autonomous cars, and robots

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, acceptance rate = 1088/4990 = 21.8%)

11 Nov. 2019: our paper is accepted at AAAI 2020 conference (A* ranked, acceptance rate = 1591/8800 = 20.6%)

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, Representation Learning, Bayesian Optimization, Fairness and Interpretable Machine Learning, Knowledge Distillation

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

PUBLICATIONS (Google scholar, ORCID)
(12 Q1-journal papers, 10 A*/A-conference papers)


International journal papers

  1. 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, IF: 8.038) [Source code]
  2. 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, IF: 8.038) [Paper]
  3. 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, IF: 6.795) [Paper] [Source code]
  4. Phuc Luong, Dang Nguyen, Sunil Gupta, Santu Rana, Svetha Venkatesh (2021). Adaptive Cost-aware Bayesian Optimization. Knowledge-Based Systems, 232, 107481 (Q1, IF: 8.038)
  5. 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, IF: 5.452) [Paper]
  6. 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, IF: 2.584) (* equivalent first authors)
  7. 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, IF: 4.396)
  8. Dang Nguyen, Wei Luo, Svetha Venkatesh, Dinh Phung (2018). Effective Identification of Similar Patients Through Sequential Matching over ICD Code Embedding. Journal of Medical Systems, 42(5), Article 94 (Q2, IF: 2.460) [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, IF: 2.947)
  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, IF: 1.457)
  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, IF: 4.038)
  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, IF: 2.207)
  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, IF: 2.240)

International conference papers

  1. Dang Nguyen, Sunil Gupta, Kien Do, Svetha Venkatesh (2022). Black-box Few-shot Knowledge Distillation. ECCV 2022 (A* ranked conference, acceptance rate = 1650/5803 = 28%) [Source code]
  2. 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 2022 (A* ranked conference, acceptance rate = 1650/5803 = 28%)
  3. Azhar Mohammed, Dang Nguyen, Bao Duong, Melanie Nichols, Thin Nguyen (2022). Handling missing data with Markov boundary. ADMA 2022
  4. Azhar Mohammed, Dang Nguyen, Bao Duong, Thin Nguyen (2022). Efficient Classification with Counterfactual Reasoning and Active Learning. ACIIDS 2022
  5. 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 2021 (A ranked conference)
  6. 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 2021 (A ranked conference)
  7. Cheng Li, Santu Rana, Andrew William Gill, Dang Nguyen, Sunil Kumar Gupta, Svetha Venkatesh (2020). Factor Screening Using Bayesian Active Learning and Gaussian Process Meta-Modelling. ICPR 2020
  8. Thomas Patrick Quinn*, Dang Nguyen*, Santu Rana, Sunil Gupta, Svetha Venkatesh (2020). DeepCoDA: personalized interpretability for compositional health data. ICML 2020 (A* ranked conference, acceptance rate = 1088/4990 = 21.8%) (* equivalent first authors) [Source code]
  9. Phuc Luong, Dang Nguyen, Sunil Gupta, Santu Rana, Svetha Venkatesh (2020). Bayesian Optimization with Missing Inputs. ECML-PKDD 2020 (A ranked conference)
  10. Dang Nguyen, Sunil Gupta, Santu Rana, Alistair Shilton, Svetha Venkatesh (2020). Bayesian Optimization for Categorical and Category-Specific Continuous Inputs. AAAI 2020 (A* ranked conference, acceptance rate = 1591/8800 = 20.6%) [Source code]
  11. Phuc Luong, Sunil Gupta, Dang Nguyen, Santu Rana, Svetha Venkatesh (2019). Bayesian Optimization with Discrete Variables. AI 2019
  12. Deepthi Kuttichira, Sunil Gupta, Dang Nguyen, Santu Rana, Svetha Venkatesh (2019). Detection of Compromised Models Using Bayesian Optimization. AI 2019 [Source code]
  13. Dang Nguyen, Cheng Li, Santu Rana, Andrew Gill, Sunil Gupta, Svetha Venkatesh (2019). Factor screening high-dimensional, stochastic combat simulations. MODSIM 2019
  14. 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 2018 (Best Student Machine Learning Paper Runner Up Award) (A ranked conference) [Source code]
  15. Dang Nguyen, Tu Dinh Nguyen, Wei Luo, Svetha Venkatesh (2018). Trans2Vec: Learning Transaction Embedding via Items and Frequent Itemsets. PAKDD 2018 (A ranked conference)
  16. Dang Nguyen, Wei Luo, Tu Dinh Nguyen, Svetha Venkatesh, Dinh Phung (2018). Learning Graph Representation via Frequent Subgraphs. SDM 2018 (A ranked conference) [Source code]
  17. Dang Nguyen, Wei Luo, Dinh Phung, Svetha Venkatesh (2016). Control Matching via Discharge Code Sequences. NIPS 2016 Workshop on Machine Learning for Health
  18. Dang Nguyen, Wei Luo, Dinh Phung, Svetha Venkatesh (2016). Exceptional Contrast Set Mining: Moving Beyond the Deluge of the Obvious. AI 2016 [Source code]
  19. Dang Nguyen, Wei Luo, Dinh Phung, Svetha Venkatesh (2015). Understanding Toxicities and Complications of Cancer Treatment: A Data Mining Approach. AI 2015
  20. Bay Vo, Dang Nguyen, Thanh-Long Nguyen (2015). A parallel algorithm for frequent subgraph mining. ICCSAMA 2015 [Source code]
  21. Dang Nguyen, Bay Vo, Bac Le (2014). A novel method for mining class association rules with itemset constraints. ICCCI 2014
  22. Dang Nguyen, Bay Vo (2013). Mining Class-Association Rules with Constraints. KSE 2013

RESEARCH EXPERIENCE


  • Alfred Deakin Postdoctoral Research Fellow, 03/2022 – present
    Deakin University, Geelong, Australia
  • Research Fellow, 01/2022 – present
    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


  • 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. Vo Tri Nhan (commenced in 2022)
      Principal supervisor, research topic: Knowledge Distillation
    2. Ngo Nam Giang (commenced in 2022)
      Associate supervisor, research topic: Algorithmic Assurance
    3. Azhar Mohammed (commenced in 2021)
      Associate supervisor, research topic: Causal Reasoning
  • Past:
    1. Luong Huu Phuc (completed in 2021): first job was Research Fellow at Monash University
      Associate supervisor, research topic: Bayesian Optimization
    2. Deepthi Praveenlal Kuttichira (completed in 2021): first job was Postdoc at Central Queensland University
      Associate supervisor, research topic: Deep Learning Deployment

PROFESSIONAL SERVICES


Program Committee Members

  • CVPR (2022), AAAI (2021, 2022, 2023), ECML-PKDD (2020), AusDM (2019, 2020, 2021, 2022), ACIIDS (2022), MAPR (2022), IEEE SSCI (2020), SoICT (2019)

Invited Reviewers for Journals

  • TKDE (IEEE), Applied Intelligence (Springer), Transactions on Fuzzy Systems (IEEE), KBS (Elsevier), Transactions on Data Science (ACM), Information Systems (Elsevier), Information Sciences (Elsevier), Data Technologies and Applications (Emerald), Cluster Computing (Springer), Pattern Recognition (Elsevier), BMJ Open (BMJ), Machine Learning (Springer), WWWJ (Springer), DKE (Elsevier), VJCS (Springer)

Invited Reviewers for Conferences

  • PAKDD, ACIIDS, IJCRS, INISTA, KSE, CISIM, ICTCC, ICCASA

CONTACT DETAILS


Dang Nguyen

Name: Dang Nguyen

Email: nguyenphamhaidang [at] outlook.com


Last update: 20 Sep. 2022