Senior Assistant Professor, Faculty of Science and Director, Institute of Applied Data Analytics
Daphne Lai is a Computer Science Lecturer at the Faculty of 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, Computer Programming and Data Mining.
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 and Machine Learning. Please kindly email me your research proposals, academic transcripts, and publications, demonstrating good technical knowledge in the areas above. Research students in this group will be affiliated to the Institute of Applied Data Analytics.
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
For local research assistants
Here at the Institute of Applied Data Analytics at UBD, we are looking for a research assistant who is looking to further their knowledge with us in an AIML project (see link below). If you are interested, send me an email with your CV.
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.)
Natural Language Processing
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
2) Geology (Source Rock Characterisation; Raw Water Quality Analysis; Geochemical Atlas)
3) Material Science
5) Social Science
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. (FOS/IADA/DS project)
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/DS 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.
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/DS project) 2) Developing learning frameworks for decision-making support in geoscience problems such as prediction of TOC and other geochemical properties. (Geology/IADA/DS project) 3) In this project, we develop a novel unsupervised algorithm for extracting meaningful features to solve text categorisation problems.
Google Scholar Citations
Google Scholar h-index
Google Scholar i10-index
 An Effective and Efficient Constrained Wardâ€™s Hierarchical Agglomerative Clustering Method, AA Aljohani, EA Edirisinghe, DTC Lai, Proceedings of SAI Intelligent Systems Conference, 590-611
 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
 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 â€¦, MR Shalaby, N Jumat, D Lai, O Malik, Journal of Petroleum Science and Engineering 176, 369-380, 2019
 Population Based Lifetime Risk Estimation Of Malignant Cancers In Brunei Darussalam, SK Ong, F Alikhan, DTC Lai, A Abdullah, K Othman, L Naing, Brunei International Medical Journal 14, 92-101, 2019
 Comprehensive Cardiac Rehabilitation Programmes Improves Quality Of Life And Exercise Tolerance In Low Risk Cardiac Patients, SK Jong, DTC Lai, CF Chong, SK Ong, CL Chong,
Brunei International Medical Journal 14 (1), 17-27, 2018
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
1. Best Abstract and Best Poster for The Cardiac Society of Brunei Darussalamâ€™s (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
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.
1. PHP 5 for Ministry of Development staff (16 â€“ 18 and 23 â€“ 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)
- Data Analytics using LiDAR data (Aug 2019 - present)
- 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)
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)
- Volunteer Researcher with local startup (Aug 2018 - present)
Analysing data for decision making support in business development
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.