Daphne Teck Ching Lai

daphne.lai@ubd.edu.bn

Senior Assistant Professor, School of Digital Science



                                        

Daphne Lai is a Computer Science Lecturer at the School of Digital Science, Universiti Brunei Darussalam. Her research interests lie in the areas of Data Mining, Artificial Intelligence and Metaheuristics. In recent years, she has been investigating on improving techniques for cluster analysis using evolutionary algorithms and machine learning. She is collaborating with researchers in several disciplines of health care, particularly cancer registry and cardiac rehabilitation, in geology and in traffic driving.

Daphne is currently lecturing in Programming Fundamentals, Data Mining and Artificial Intelligence

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For postgraduates
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There are vacancies for interested local and international Masters & PhD candidates with numerous proposed projects listed below as well as research in the areas of Artificial Intelligence, Data Mining, Natural Language Processing and Machine Learning. Please kindly email me your research proposals, academic transcripts, and publications, demonstrating good technical knowledge in the areas above.
Postgraduate admissions: https://ubd.edu.bn/admission/graduate/applying-to-ubd(graduate).html
Scholarships are available for eligible applicants. Please go to: http://www.ubd.edu.bn/admission/scholarships.html
Kindly note admissions deadline for the respective intakes:
August Intake: (Deadline: Around January)
January Intake: (Deadline: Around July)

Other Scholarships: http://mfa.gov.bn/Pages/BDScholarship.aspx

EDUCATION

BSc in Computer Science, University of Strathclyde, UK (Thesis: Elliptic Curve Cryptography)
MSc in Distributed systems and Networks, University of Kent, Canterbury, UK. (Thesis: Economy of Stable Job Scheduling in Grid Computing Systems)
PhD in Computer Science, University of Nottingham, UK (Thesis: An Exploration of Improvements to Semi-supervised Fuzzy c-Means Clustering for Real-World Biomedical Data.)

RESEARCH INTERESTS

Data Mining
Cluster Analysis
Artificial Intelligence
Evolutionary Computation
Natural Language Processing
Machine Learning
We study and investigate in following machine learning algorithms:

1) Evolutionary methods for unsupervised/semi-supervised clustering, in particular fuzzy clustering
2) Metric Learning algorithms, similarity measures
3) Model-based techniques
4) Time-based clustering algorithms
5) Computer Vision algorithms

With applications into
1) Health
2) Geology (Source Rock Characterisation; Raw Water Quality Analysis; Geochemical Atlas)
3) Material Science
4) Biology
5) Social Science
etc

FUTURE PROJECTS

Road driving modelling [Modeling driving patterns using simulation and sensor data for enhancing traffic safety] using 1) Statistical and Machine Learning 2) Time series analysis

Grant UBD/RSCH/I.11lFICBF/2018/002. In this research project, we aim to study the driving patterns of drivers in Brunei using simulated and sensor data, identifying extensively detailed driving parameters including distance from traffic light, pressure during braking or acceleration, acceleration. These actual driving patterns along any road are not easily observable and measured by an analyst. By identifying different profiles (such as safe or unsafe driving) in the driving patterns, a system to warn the driver can be implemented. We have collected time series data and is currently looking for a suitable PhD student to analyse the data, applying AI, Machine Learning and/or Deep Learning techniques. (FOS/IADA/SDS project)


Applications Invited
PhD or MSc

Fuzzy and Evolutionary Algorithms for data clustering

Grant: UBD/RSCH/1.11/FICBF(b)/2019/001. In this project, we aim to develop an evolutionary algorithm which is capable of performing human activities analysis for an autonomous robot. (FOS/IADA/SDS project) Research in developing novel fuzzy and/or evolutionary algorithms for data clustering (feature selection, metric learning, kernel-based approaches, constrained-based) or optimisation is also of interest.


Applications Invited
PhD or MSc

Other projects: 1) Intelligent Modelling for Non-communicable diseases 2) Intelligent Modelling for Geoscience applications 3) Application of Machine Learning and AI in Biomass Processing 4) Automatic Feature Extraction for Text Categorisation using unsupervised techniques

1) In this project, we plan to create a predictive model for determining risk of patients in non-communicable diseases such as cancer or cardiovascular diseases. (IHS/IADA/SDS project) 2) Developing learning frameworks for decision-making support in geoscience problems such as prediction of TOC and other geochemical properties. (Geology/IADA/SDS project) 3) The aim of this project is to characterise processes parameters relating to biomass processes using AI and Machine Learning to gain more insights and information about the processes. 4) In this project, we develop a novel unsupervised algorithm for extracting meaningful features to solve natural language processing and text categorisation problems. (SDS)


Applications Invited
PhD or MSc
26

Scopus Publications

168

Google Scholar Citations

8

Google Scholar h-index

5

Google Scholar i10-index

Scopus Publications

RECENT PUBLICATIONS

[1] Hybrid Multiobjective Evolutionary Algorithms for Unsupervised QPSO, BBPSO and Fuzzy clustering. DTC Lai, Y Sato, 2021 IEEE Congress on Evolutionary Computation (IEEE CEC) 2021 https://doi.org/10.1109/CEC45853.2021.9504968
[2] Identification and classification of driving behaviour at signalized intersections using support vector machine. SL Karri, LC De Silva, DTC Lai, SY Yong, International Journal of Automation and Computing 18 (3), 480-491, 2021 https://doi.org/10.1007/s11633-021-1295-y
[3] Prevalence of undetected hypertension and its association with socio-demographic and non-communicable diseases risk factors in Brunei Darussalam
SK Ong, SZ Kahan, DTC Lai, KA Si-Ramlee, MA Abdullah, N Sidup, ... Journal of Public Health. 2020. https://doi.org/10.1007/s10389-020-01287-y
[4] Semi-supervised data clustering using particle swarm optimisation, DTC Lai, M Miyakawa, Y Sato, Soft Computing 2019. https://doi.org/10.1007/s00500-019-04114-z
[5] An Effective and Efficient Constrained Ward’s Hierarchical Agglomerative Clustering Method. AA Aljohani, EA Edirisinghe, DTC Lai. 2019 Proceedings of SAI Intelligent Systems Conference, 590-611. https://doi.org/10.1007/978-3-030-29516-5_46
[6] Survival Rates and Associated Factors of Colorectal Cancer Patients in Brunei Darussalam. E Leong, O Sok King, F Madli, A Tan, DTC Lai, N Basir, N Ramlee, ... 2020
Asian Pacific Journal of Cancer Prevention 21 (1), 259-265 https://dx.doi.org/10.31557%2FAPJCP.2020.21.1.259

TOP PUBLICATIONS

[1] Integrated TOC prediction and source rock characterization using machine learning, well logs and geochemical analysis: Case study from the Jurassic source rocks in Shams Field … MR Shalaby, N Jumat, DTC Lai, O Malik Journal of Petroleum Science and Engineering 176, 369-380 2019. https://doi.org/10.1016/j.petrol.2019.01.055
[2] A Preliminary Study on Automatic Breast Cancer Data Classification using Semi-supervised Fuzzy c-Means. DTC Lai, JM Garibaldi. International Journal of Biomedical Engineering and Technology 13 (4), 303-322, 2013. https://doi.org/10.1504/IJBET.2013.058535
[3] A comparison of distance-based semi-supervised fuzzy c-means clustering algorithms. DTC Lai, JM Garibaldi. 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 1580-1586. https://doi.org/10.1109/FUZZY.2011.6007562
[4] Cross-sectional STEPwise approach to surveillance (STEPS) population survey of noncommunicable diseases (NCDs) and risk factors in Brunei Darussalam 2016
SK Ong, DTC Lai, JYY Wong, KA Si-Ramlee, LA Razak, N Kassim, ... Asia Pacific Journal of Public Health 29 (8), 635-648. https://doi.org/10.1177%2F1010539517738072
[5] Improving Semi-supervised Fuzzy C-Means Classification of Breast Cancer Data Using Feature Selection. DTC Lai, JM Garibaldi. IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) 2013, 1-8. https://doi.org/10.1109/FUZZ-IEEE.2013.6622544

GRANT DETAILS

1) Grant Number: URG 311, Project Title: Exploring metaheuristics in a semi-supervised fuzzy c-means (ssFCM) clustering framework applied on biomedical data, Investigators (PI/Co-PI): Daphne TC Lai, Funding Details: BND 9,231, Start Date: 01/03/2015, End Date: 29/02/2018
2) Grant Number: UBD/RSCH/1.11/FICBF(b)/2019/001, Project Title: Application of Multi-objective Evolutionary Computation in Human Activity Discovery, Investigators (PI/Co-PI): Daphne TC Lai, Ong Wee Hong, Funding Details: BND 54,650.00, Start Date: 01/01/2020, End Date: 31/12/2021

RESEARCH OUTPUTS (PATENTS, SOFTWARE, PUBLICATIONS, PRODUCTS)

1. Best Abstract and Best Poster for The Cardiac Society of Brunei Darussalam (CSBD) second Annual Scientific Meeting 2015, Brunei for work titled: EVALUATE THE EFFECT OF LONG-TERM COMPREHENSIVE CARDIAC REHABILITATION IN CORONARY ARTERY DISEASE: A COHORT STUDY by authors: SK Jong, DTC Lai, SK Ong and CL Chong.
2. 2018 Hosei International Fund (HIF) Foreign Scholars Fellowship https://www.global.hosei.ac.jp/en/researchers/hif/

CONSULTANCY

Consultancy in Artificial Intelligence, Data Mining and Machine Learning is available.
Interested parties may email at daphne.lai[[AT]]ubd.edu.bn
Happy to chat on the application and potential of AI, DM and ML to businesses/industries.

Previous consultancy:
1. PHP 5 for Ministry of Development staff (16 to 18 and 23 to 26 March 2009)
2. FLOSS for local and CLMV (Cambodia-Laos-Myanmar-Vietnam) teachers and system administrators under the Initiative for ASEAN Integration Programme (3 - 5 March 2009)
3. Cascading Style Sheets for Ministry of Development staff (24 - 25 February 2009)
4. Trainer in Linux Software Development (FLOSS training) for Teachers and Ministry of Development staff (June 2007 and March 2008)

Industry, Institute, or Organisation Collaboration

Current Projects
- Data Analytics using LiDAR data (Aug 2019 - May 2020)
- Evolutionary Computation algorithms with Hosei University, Japan; PSOs & MOEAs (Apr 2018 - present)
- Applying ML techniques with Geology@UBD; source rock characterisation and TOC prediction (Sep 2017 - present)
- Applying Machine Learning techniques to study driving behaviours collected from simulation (Aug 2017 - present)
- Metaheuristics in semi-supervised Fuzzy c-means (Mar 2015-present)

Past Project:
1) Semi-supervised Fuzzy c-Means clustering methodologies for Real-World Biomedical Data with University of Nottingham (Oct 2010 - May 2014)
2) Evaluate Effects of Cardiac Rehab Programme with MOH (Jun 2014 - Sept 2017)
3) - Semi-supervised hierarchical clustering with University of Loughborough, UK (Feb 2016 - Nov 2019)
4) Applying Machine Learning techniques on National Health Survey data with Ministry of Health (Aug 2017 - Dec 2019)
5) Data Analytics of Cancer Registry with Ministry of Health (Aug 2014 - Dec 2020)

INDUSTRY EXPERIENCE

- Volunteer Researcher with local startup in business development (Aug 2018 - present)

STARTUP

Analysing data for decision making support in business development

SOCIAL, ECONOMIC, or ACADEMIC BENEFITS

AI and Machine Learning has been widely applied in the field of medicine, agriculture, education, logistics, ecology, retail and so forth. By learning from data, we can find hidden patterns and create useful models to be used for prediction. This has provided decision-support or additional insights to domain experts and a means towards automation, bringing about advancement in the domain areas, as well as technology and knowledge economy creation.