Son Tran

UTAS Home Dr Son Tran

Son Tran

Information & Communication Technology

Sandy Bay Campus

6226 7277 (phone)


Dr. Son Tran obtained his Ph.D in Computer Science at City, University of London, the United Kingdom in 2016. His research focuses on theoretical Artificial Intelligence, i.e. bridging the gap between Connectionism and Symbolism, and applications of (deep) Neural Networks for various tasks: Internet of Things, Music Informatics, Natural Language Processing/Information Retrieval, Computer Vision. He has publications in flagship conferences and journals such as SIGIR, IJCAI, IJCNN, ECIR, IEEE TNNLS. Before taking the position of Lecturer at the University of Tasmania Dr. Son was a postdoctoral research fellow at CSIRO, working on the development of a novel prototype of smart homes for aged care.

Career summary


DegreeUniversityThesis Title
Ph.D.City, University of LondonRepresentation decomposition for knowledge extraction and sharing using restricted Boltzmann machines
MScUniversity of ReadingMap-Reduce for Distributed Matrix Computations
BScHanoi University of Technology


Teaching expertise

Machine Learning, Neural Computation, Artificial Intelligence, Data Structures and Algorithms

City, University of London:

INM427 (MSc): Neural Computation (Teaching Assistant, 2015)

INM432 (MSc): Big Data (Teaching Assistant, 2014)

IN2001 (BSc): Computation and Reasoning (Visiting Lecturer, 2014)

IN2002 (BSc): Data Structures and Algorithms (Visiting Lecturer, 2012,2013)

University of Greenwich (Vietnam campus)

COMP1550 (BSc): Application Development for Mobile Devices (Lecturer)

View more on Dr Son Tran in WARP


Artificial Intelligence, Deep Learning, Neural Networks, IoT, Neural-Symbolic, Pattern Recognition and Data Mining.

Research Themes

Human Level Artificial Intelligence, Artificial Intelligence for IoT


AES 53 Reproducible Prize (2013)
IEEE CIS Outstanding Student-paper Travel Grants (2014, 2015)
Outstanding Academic Achievement Award (2010)

-Erasmus Mundus MSc scholarship
-University Doctoral scholarship (City, University of London)

-Pump-priming Grant (City , University of London): 5000 GBP
-Acorn grant (CSIRO): 10000 AUD

Fields of Research

  • Intelligent robotics (460205)
  • Natural language processing (460208)
  • Computer vision (460304)
  • Artificial intelligence (460299)
  • Knowledge representation and reasoning (460206)
  • Applications in health (460102)
  • Artificial life and complex adaptive systems (460201)
  • Neural networks (461104)
  • Geriatrics and gerontology (320210)
  • Neurology and neuromuscular diseases (320905)
  • Autonomous agents and multiagent systems (460202)
  • Deep learning (461103)
  • Ecological impacts of climate change and ecological adaptation (410102)
  • Aquaculture and fisheries stock assessment (300502)
  • Rural and remote health services (420321)
  • Aged health care (420301)
  • Maritime transportation and freight services (350904)
  • Cognition (520401)
  • Testing, assessment and psychometrics (520108)
  • Higher education (390303)
  • Modelling and simulation (460207)
  • Mixed initiative and human-in-the-loop (460808)
  • Pattern recognition (460308)
  • Machine learning (461199)
  • Pervasive computing (460809)
  • Fisheries management (300505)

Research Objectives

  • Health related to ageing (200502)
  • Information systems, technologies and services (220499)
  • Artificial intelligence (220403)
  • Diagnosis of human diseases and conditions (200101)
  • Defence (140199)
  • Wild caught crustaceans (excl. rock lobster and prawns) (100303)
  • Learner and learning (160199)
  • Mental health services (200305)
  • Intelligence, surveillance and space (140105)
  • Maritime (140108)
  • The media (130204)
  • Agricultural and environmental standards and calibrations (150401)
  • Emerging defence technologies (140104)
  • Assessment and management of coastal and estuarine ecosystems (180201)
  • Behaviour and health (200401)
  • Ecosystem adaptation to climate change (190102)
  • Visual communication (130205)
  • Legal processes (230406)
  • Human-computer interaction (220407)
  • Evaluation of health outcomes (200202)
  • Telehealth (200208)
  • Autonomous and robotic systems (241202)
  • Higher education (160102)
  • Autonomous water vehicles (270401)
  • Health education and promotion (200203)
  • Professional development and adult education (160104)


Total publications


Journal Article

(17 outputs)
2022Huang G, Tran SN, Bai Q, Alty J, 'Real-time automated detection of older adults' hand gestures in home and clinical settings', Neural Computing and Applications pp. 1-14. ISSN 0941-0643 (2022) [Refereed Article]

DOI: 10.1007/s00521-022-08090-8 [eCite] [Details]

Co-authors: Huang G; Bai Q; Alty J


2022Maqsood S, Xu S, Tran S, Garg S, Springer M, et al., 'A survey: from shallow to deep machine learning approaches for blood pressure estimation using biosensors', Expert Systems With Applications, 197 Article 116788. ISSN 0957-4174 (2022) [Refereed Article]

DOI: 10.1016/j.eswa.2022.116788 [eCite] [Details]

Citations: Scopus - 6Web of Science - 5

Co-authors: Maqsood S; Xu S; Garg S; Springer M; Mohawesh R


2022Wang Xinyi, St George RJ, Bai Q, Tran S, Alty J, 'New horizons in late-onset essential tremor: a pre-cognitive biomarker of dementia?', Age and Ageing, 51, (7) pp. 1-8. ISSN 0002-0729 (2022) [Refereed Article]

DOI: 10.1093/ageing/afac135 [eCite] [Details]

Co-authors: Wang Xinyi; St George RJ; Bai Q; Alty J


2022Wang Xinyi, St George RJ, Bai Q, Tran SN, Alty J, 'Differences in clinical manifestations of late onset, compared to earlier onset essential tremor: a scoping review', Journal of the neurological sciences, 440 pp. 1-5. ISSN 0022-510X (2022) [Refereed Article]

DOI: 10.1016/j.jns.2022.120336 [eCite] [Details]

Co-authors: Wang Xinyi; St George RJ; Bai Q; Alty J


2021Iqbal MS, Ali H, Tran SN, Iqbal T, 'Coconut trees detection and segmentation in aerial imagery using mask region-based convolution neural network', IET Computer Vision, 15, (6) pp. 428-439. ISSN 1751-9632 (2021) [Refereed Article]

DOI: 10.1049/cvi2.12028 [eCite] [Details]

Citations: Scopus - 7Web of Science - 7


2021Mohawesh R, Tran S, Ollington R, Xu S, 'Analysis of concept drift in fake reviews detection', Expert Systems with Applications, 169 Article 114318. ISSN 0957-4174 (2021) [Refereed Article]

DOI: 10.1016/j.eswa.2020.114318 [eCite] [Details]

Citations: Scopus - 15Web of Science - 10

Co-authors: Mohawesh R; Ollington R; Xu S


2021Mohawesh R, Xu S, Tran SN, Ollington R, Springer M, et al., 'Fake reviews detection: A survey', IEEE Access, 9 pp. 65771-65802. ISSN 2169-3536 (2021) [Refereed Article]

DOI: 10.1109/ACCESS.2021.3075573 [eCite] [Details]

Citations: Scopus - 26Web of Science - 11

Co-authors: Mohawesh R; Xu S; Ollington R; Springer M; Maqsood S


2021Nguyen D, Nguyen DT, Zeng R, Nguyen TT, Tran S, et al., 'Deep auto-encoders with sequential learning for multimodal dimensional emotion recognition', IEEE Transactions on Multimedia pp. 1-12. ISSN 1520-9210 (2021) [Refereed Article]

DOI: 10.1109/TMM.2021.3063612 [eCite] [Details]

Citations: Scopus - 6Web of Science - 7


2021Wang X, Garg S, Tran SN, Bai Q, Alty J, 'Hand tremor detection in videos with cluttered background using neural network based approaches', Health Information Science and Systems, 9, (1) pp. 1-14. ISSN 2047-2501 (2021) [Refereed Article]

DOI: 10.1007/s13755-021-00159-3 [eCite] [Details]

Citations: Scopus - 4Web of Science - 5

Co-authors: Wang X; Garg S; Bai Q; Alty J


2020Ngo TS, Bui NA, Tran TT, Le PC, Bui DC, et al., 'Some algorithms to solve a bi-objectives problem for team selection', Applied Sciences, 10, (8) Article 2700. ISSN 2076-3417 (2020) [Refereed Article]

DOI: 10.3390/APP10082700 [eCite] [Details]

Citations: Scopus - 11Web of Science - 5


2020Tran SN, Ngo S, Garcez Ad, 'Probabilistic approaches for music similarity using restricted Boltzmann machines', Neural Computing and Applications, 32, (8) pp. 3999-4008. ISSN 0941-0643 (2020) [Refereed Article]

DOI: 10.1007/s00521-019-04106-y [eCite] [Details]

Citations: Scopus - 1Web of Science - 1


2020Tran SN, Ngo T-S, Zhang Q, Karunanithi K, 'Mixed-dependency models for multi-resident activity recognition in smart homes', Multimedia Tools and Applications, 79 pp. 23445-23460. ISSN 1380-7501 (2020) [Refereed Article]

DOI: 10.1007/s11042-020-09093-0 [eCite] [Details]

Citations: Scopus - 8Web of Science - 5


2020Tran SN, d'Avila Garcez A, Weyde T, Yin J, Zhang Q, et al., 'Sequence classification restricted Boltzmann machines with gated units', IEEE Transactions on Neural Networks and Learning Systems pp. 1-10. ISSN 2162-237X (2020) [Refereed Article]

DOI: 10.1109/TNNLS.2019.2958103 [eCite] [Details]

Citations: Scopus - 3Web of Science - 3


2020Wei Y, Tran S, Xu S, Kang B, Springer M, 'Deep learning for retail product recognition: challenges and techniques', Computational Intelligence and Neuroscience, 2020 Article ID 8875910. ISSN 1687-5265 (2020) [Refereed Article]

DOI: 10.1155/2020/8875910 [eCite] [Details]

Citations: Scopus - 15Web of Science - 13

Co-authors: Wei Y; Xu S; Kang B; Springer M


2019Garcez AD, Gori M, Lamb LC, Serafini L, Spranger M, et al., 'Neural-symbolic computing: An effective methodology for principled integration of machine learning and reasoning', Journal of Applied Logics, 6, (4) pp. 611-631. ISSN 2631-9810 (2019) [Refereed Article]

[eCite] [Details]

Citations: Scopus - 65


2019Tran SN, Nguyen D, Ngo TS, Vu XS, Hoang L, et al., 'On multi-resident activity recognition in ambient smart-homes', Artificial Intelligence Review pp. 1-17. ISSN 0269-2821 (2019) [Refereed Article]

DOI: 10.1007/s10462-019-09783-8 [eCite] [Details]

Citations: Scopus - 10Web of Science - 11


2018Ali H, Tran SN, Benetos E, d'Avila Garcez AS, 'Speaker recognition with hybrid features from a deep belief network', Neural Computing and Applications, 29, (6) pp. 13-19. ISSN 0941-0643 (2018) [Refereed Article]

DOI: 10.1007/s00521-016-2501-7 [eCite] [Details]

Citations: Scopus - 68Web of Science - 7


Chapter in Book

(1 outputs)
2019Tran SN, Zhang Q, 'Towards Multi-resident Activity Monitoring with Smarter Safer Home Platform', Smart Assisted Living. Computer Communications and Networks, Springer, F Chen, R Garcia-Betances, L Chen, M Cabrera-Umpierrez, and C Nugent (ed), Switzerland, pp. 249-267. ISBN 978-3-030-25590-9 (2019) [Research Book Chapter]

DOI: 10.1007/978-3-030-25590-9_12 [eCite] [Details]


Conference Publication

(13 outputs)
2022Chen Y, Zhou Y, Tran S, Park M, Hadley S, et al., 'A Self-learning Approach for Beggiatoa Coverage Estimation in Aquaculture', Lecture Notes in Artificial Intelligence 13151, 02-04 February 2022, Sydney, pp. 405-416. ISSN 0302-9743 (2022) [Refereed Conference Paper]

DOI: 10.1007/978-3-030-97546-3_33 [eCite] [Details]

Co-authors: Chen Y; Park M; Hadley S; Lacharite M; Bai Q


2021Tran SN, 'Compositional neural logic programming', Proceedings of the 30th International Joint Conference on Artificial Intelligence, 19-26 August 2021, Virtual Conference, Online (Montreal, Canada), pp. 3059-3066. ISBN 978-0-9992411-9-6 (2021) [Refereed Conference Paper]

DOI: 10.24963/ijcai.2021/421 [eCite] [Details]


2020Riveret R, Tran S, d'Avila Garcez A, 'Neural-symbolic probabilistic argumentation machines', Proceedings of the 17th International Conference Principles of Knowledge Representation and Reasoning, 12-18 September 2020, Rhodes, Greece, pp. 871-881. (2020) [Refereed Conference Paper]

DOI: 10.24963/kr.2020/90 [eCite] [Details]


2020Wei Y, Xu S, Tran S, Kang B, 'Data augmentation with generative adversarial networks for grocery product image recognition', Proceedings of the 16th IEEE International Conference on Control, Automation, Robotics and Vision, ICARCV 2020, 13-15 December 2020, Virtual Conference, Online (Shenzhen, China), pp. 963-968. ISBN 978-1-7281-7709-0 (2020) [Refereed Conference Paper]

DOI: 10.1109/ICARCV50220.2020.9305421 [eCite] [Details]

Citations: Scopus - 2

Co-authors: Wei Y; Xu S; Kang B


2019Vu X-S, Tran SN, Jiang L, 'dpUGC: Learn differentially private representation for user generated contents', Proceedings of the 20th International Conference on Computational Linguistics and Intelligent Text Processing, 7-13 April 2019, La Rochelle, France, pp. 1-16. (2019) [Refereed Conference Paper]

[eCite] [Details]


2019Vu X-S, Vu T, Tran SN, Jiang L, 'ETNLP: A visual-aided systematic approach to select pre-trained embeddings for a downstream task', Proceedings of the 2019 Recent Advances in Natural Language Processing International Conference, 2-4 September 2019, Varna, Bulgaria, pp. 1285-1294. ISSN 1313-8502 (2019) [Refereed Conference Paper]

DOI: 10.26615/978-954-452-056-4_147 [eCite] [Details]

Citations: Scopus - 14


2018Ireland D, Hassanzadeh H, Tran SN, 'Sentimental Analysis for AIML-Based E-Health Conversational Agents', Neural Information Processing: ICONIP 2018. Lecture Notes in Computer Science, volume 11302, 13-16 December 2018, Siem Reap, Cambodia, pp. 41-51. ISBN 978-3-030-04178-6 (2018) [Refereed Conference Paper]

[eCite] [Details]

2018Tran SN, Zhang Q, Nguyen A, Vu X-S, Ngo S, 'Improving recurrent neural networks with predictive propagation for sequence labelling', Proceedings of the 25th International Conference on Neural Information Processing (ICONIP 2018), Lecture Notes in Computer Science, volume 11301, 13-16 December 2018, Siem Reap, Cambodia, pp. 452-462. ISBN 978-3-030-04166-3 (2018) [Refereed Conference Paper]

DOI: 10.1007/978-3-030-04167-0_41 [eCite] [Details]

Citations: Scopus - 1


2018Tran SN, Zhang Q, Smallbon V, Karunanithi M, 'Multi-resident activity monitoring in smart homes: a case study', Proceedings of the 2018 IEEE International Conference on Pervasive Computing and Communications Workshops, 19-23 March 2018, Athens, Greece, pp. 698-703. ISBN 978-1-5386-3227-7 (2018) [Refereed Conference Paper]

DOI: 10.1109/PERCOMW.2018.8480132 [eCite] [Details]

Citations: Scopus - 8


2018Yin J, Tran SN, Zhang Q, 'Human identification via unsupervised feature learning from UWB radar data', Lecture Notes in Computer Science, volume 10937 - Proceedings of the 2018 Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2018): Advances in Knowledge Discovery and Data Mining, 3-6 June 2018, Melbourne, Australia, pp. 322-334. ISBN 978-3-319-93033-6 (2018) [Refereed Conference Paper]

DOI: 10.1007/978-3-319-93034-3_26 [eCite] [Details]

Citations: Scopus - 5Web of Science - 5


2017Cherla S, Tran SN, d'Avila Garcez A, Weyde T, 'Generalising the discriminative restricted Boltzmann machines', Proceedings of the 26th International Conference on Artificial Neural Networks: Artificial Neural Networks and Machine Learning, Part II, 11-14 September 2017, Alghero, Italy, pp. 111-119. (2017) [Refereed Conference Paper]

DOI: 10.1007/978-3-319-68612-7_13 [eCite] [Details]

Citations: Scopus - 8Web of Science - 7


2017Tran SN, 'Unsupervised neural-symbolic integration', Proceedings of the 2017 International Joint Conference on Artificial Intelligence - Workshop on Explainable AI, 20 August 2017, Melbourne, Australia, pp. 58-62. (2017) [Refereed Conference Paper]

[eCite] [Details]

2017Tran SN, Zhang Q, Karunanithi M, 'Improving multi-resident activity recognition for smarter homes', Proceedings of the 2017 International Joint Conference on Artificial Intelligence - Workshop on AI for Internet of Things, 19 August 2017, Melbourne, Australia (2017) [Refereed Conference Paper]

[eCite] [Details]

Other Public Output

(1 outputs)
2019Vu X-S, Tran SN, Jiang L, 'dpUGC: Learn differentially private representation for user generated contents', Best Paper Awards (3rd place) at the 20th International Conference on Computational Linguistics and Intelligent Text Processing, 07-13 April 2019, La Rochelle, France (2019) [Award]

[eCite] [Details]

Grants & Funding

Funding Summary

Number of grants


Total funding



Neuro-Symbolic for Reasoning with Uncertain Temporal Graph Knowledge (2022)$20,000
This project investigates the capability of neural-symbolic in reasoning with uncertain temporal knowledge graphs. Knowledge graphs play a critical role in Artificial Intelligence as they provide the semantics of the real world to help computer agents to make well informed decisions. However, most current studies on knowledge graphs do not take into account the uncertain and temporal aspects of the world. Meanwhile, deep neural networks have shown a capability of learning from relational knowledge efficiently. This project will study a novel Neuro-symbolic approach to integrate uncertain temporal graph knowledge into a deep neural network for effective learning and efficient reasoning. The approach will consist of efficient methods for representing uncertainty and manipulating the knowledge over time.
Defence Science Institute ($20,000)
Artificial Intelligence for Decision Making
Administered By
University of Tasmania
Research Team
Tran SN
Augmented Reality Remote Assistance system for managing rural patients (2021)$387,245
Vietnam is facing overcrowding in central hospitals due to the disproportional distribution of health workforces in terms of both quantity and quality. In many rural and remote areas, the healthcare staff lack to access real-time, effective assistance, and realistic training provided by experts located centrally. It may lead to less effectiveness of health care services and additional costs for rural/ remote residents. UTAS Augmented Reality Remote Assistance system (UTAS-ARRA) is a solution developed by the Centre for Rural Health and the Human Interface Technology Laboratory, University of Tasmania. In this project, we will employ the UTAS-ARRA to improve the effectiveness of the remote situational tele-mentorship between central experts and rural/ remote less-experienced healthcare staff in Vietnam. The system aims to improve the response of rural/remote staff to manage patients under the assistance and guidance provided by central experts in real-time.
CSIRO-Commonwealth Scientific & Industrial Research Organisation ($387,245)
Grant - Aus4Innovation Partnership
Administered By
University of Tasmania
Research Team
Barnett AP; Chinthammit W; Tran SN; Hoang THH; Grattidge D; Nhan Vu V; Giap Vu V; Giang Trinh K; Phong Le H; Anh Hoang V; Hong Van Le T; Anh Tran L
Giant Crab Enhanced Data Collection - Innovative approaches to enhance data collection in the Victorian, South Australian and Tasmanian Giant crab fisheries (2021 - 2023)$135,000
This project will address identified shortcomings in the quality of data currently available for Giant Crab stock assessment and fisheries management (Tas, Vic and SA). This project will look to develop a method for fishers to images of crabs on the vessel. The images will processed using Visual Intelligence methods to determine length, sex and unique ID for each crab. This data will be used in stock assessment models to make more accurate assessment. Hopefully this process will be rolled out across the Giant Crab fishery and a database of images can be collected and stored in a central repository.
Fisheries Research & Development Corporation ($135,000)
Administered By
Victorian Fisheries Authority
Research Team
Jeavons T; Hadley SA; Leon RI; Quigley B; Tran SN
2021 - 2023
Development of a computational model of adversarial decision making to support human-AI teaming in the undersea environment (2021 - 2023)$254,411
This project uses a state-of-the-art model of human decision-making to understand how humans make decisions against adversaries and examine ways to support human-AI teaming in undersea adversarial contexts. In Phase 1, we develop a computer-based undersea decision superiority task. In Phases 2 & 3, we use this task to develop and test the model. In Phase 4, we examine whether we can use the model as an AI agent, enabling the human to achieve decision superiority against an adversary
Defence Science and Technology Group ($2,300)
University of Newcastle ($2,300); University of Queensland ($249,811)
Administered By
University of Tasmania
Research Team
Tran SN; Heathcote A; Loft S; Neal A; Palada H; Farrell S
2021 - 2023
AI in a box: A proof-of-concept for Tasmania giant crabs detection and gender classification. (2021)$24,995
University of Tasmania ($24,995)
Administered By
University of Tasmania
Research Team
Tran SN; Maiti A; Hadley SA
Fermented beverage quality analysis (2021)$4,375
University of Tasmania ($4,375)
Administered By
University of Tasmania
Research Team
Wilson MD; Sawyer S; Tran SN

Research Supervision






PhDApplying Artificial Intelligence to Identify New Tremor Biomarkers of Dementia Risk2021
MastersMaking sense from Images with Deep Learning2021
PhDOptimizing Supply chain management using Big Data technology in the Forest Sector2021


PhDDeep Learning for Multiple Retail Products Image Recognition
Candidate: Yuchen Wei
PhDMachine Learning Approaches for Fake Online Reviews Detection
Candidate: Rami Issa Mohammed Mohawesh