Profiles

Son Tran

UTAS Home Dr Son Tran

Son Tran

Lecturer
Information & Communication Technology

Sandy Bay Campus

6226 7277 (phone)

sn.tran@utas.edu.au

Biography

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

Qualifications

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

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

Expertise

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

Research Themes

Human Level Artificial Intelligence, Artificial Intelligence for IoT

Awards

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

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

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

Fields of Research

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

Research Objectives

  • Health related to ageing (200502)
  • Artificial intelligence (220403)
  • Information systems, technologies and services (220499)
  • Diagnosis of human diseases and conditions (200101)
  • Mental health services (200305)
  • Intelligence, surveillance and space (140105)
  • Defence (140199)
  • Wild caught crustaceans (excl. rock lobster and prawns) (100303)
  • Learner and learning (160199)
  • Legal processes (230406)
  • Emerging defence technologies (140104)
  • Behaviour and health (200401)
  • Ecosystem adaptation to climate change (190102)
  • Evaluation of health outcomes (200202)
  • Visual communication (130205)
  • The media (130204)
  • Agricultural and environmental standards and calibrations (150401)
  • 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)

Publications

Total publications

28

Journal Article

(15 outputs)
YearCitationAltmetrics
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 - 1Web of Science - 1

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

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2022Wang X, 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) Article afac135. ISSN 0002-0729 (2022) [Refereed Article]

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

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

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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: Web of Science - 5

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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 - 9Web of Science - 6

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

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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 - 14Web of Science - 6

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

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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 - 1Web of Science - 16

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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 - 1Web of Science - 1

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

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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 - 8Web of Science - 4

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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

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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 - 5Web of Science - 4

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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

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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 - 6Web of Science - 3

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

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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 - 47

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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 - 8Web of Science - 8

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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 - 66Web of Science - 47

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Chapter in Book

(1 outputs)
YearCitationAltmetrics
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]

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Conference Publication

(12 outputs)
YearCitationAltmetrics
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]

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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]

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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

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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]

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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 - 9

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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

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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

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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 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

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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

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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]

Grants & Funding

Funding Summary

Number of grants

5

Total funding

$571,615

Projects

Neuro-Symbolic for Reasoning with Uncertain Temporal Graph Knowledge (2022)$20,000
Description
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.
Funding
Defence Science Institute ($20,000)
Scheme
Artificial Intelligence for Decision Making
Administered By
University of Tasmania
Research Team
Tran SN
Year
2022
Augmented Reality Remote Assistance system for managing rural patients (2021)$387,245
Description
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.
Funding
CSIRO-Commonwealth Scientific & Industrial Research Organisation ($387,245)
Scheme
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
Year
2021
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
Description
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.
Funding
Fisheries Research & Development Corporation ($135,000)
Scheme
Grant
Administered By
Victorian Fisheries Authority
Research Team
Jeavons T; Hadley SA; Leon RI; Quigley B; Tran SN
Period
2021 - 2023
AI in a box: A proof-of-concept for Tasmania giant crabs detection and gender classification. (2021)$24,995
Funding
University of Tasmania ($24,995)
Scheme
null
Administered By
University of Tasmania
Research Team
Tran SN; Maiti A; Hadley SA
Year
2021
Fermented beverage quality analysis (2021)$4,375
Funding
University of Tasmania ($4,375)
Scheme
null
Administered By
University of Tasmania
Research Team
Wilson MD; Sawyer S; Tran SN
Year
2021

Research Supervision

Current

3

Current

DegreeTitleCommenced
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