Daphne Teck Ching Lai

daphne.lai@ubd.edu.bn

Lecturer, 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.

---------------------------------------
For postgraduates
---------------------------------------
There are vacancies for interested local and international Masters & PhD candidates with numerous proposed projects listed below. Please kindly email me your research proposals, academic transcripts, and publications.

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 - 28th February (Opens end of Dec)
January intake - 31 July (Opens end of May)

-------------------------------------------------------------------
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.
https://www.linkedin.com/pulse/local-machine-learning-research-assistant-required-18-lai

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
Machine Learning
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
1) Clinical/Medical Data
2) Geology (Source Rock Characterisation; Raw Water Quality Analysis; Geochemical Atlas)
3) Material Science
4) Biology
etc

FUTURE PROJECTS

The application of natural language processing to solve social issues such as depression, hate content, etc

The main objective of this study is to develop a machine learning framework for identifying behavioural patterns relating to a social issue using information found in the website as well as social media.


Applications Invited
for PhD

Application of Multi-objective Evolutionary Computation in Human Activity Discovery

In this project, we aim to develop multi-objective evolutionary algorithms (MOEA) for solving human activities analysis problems a. To build and investigate on an existing framework (unsupervised machine learning and/or deep learning) for HAA and test on available daily living activities data. b. To refine existing framework using MOEA methods on activities data.


Applications Invited
for PhD

Modelling E-commerce Customer Behavior using Natural Language Processing and Machine Learning

The main objective of this study is to develop a machine learning framework for modelling e-commerce customer behavioural patterns using information found in the product website/social media.


Applications Invited
for PhD
21

Scopus Publications

86

Google Scholar Citations

5

Google Scholar h-index

3

Google Scholar i10-index

Scopus Publications

RECENT PUBLICATIONS

[1] An Effective and Efficient Constrained Ward’s Hierarchical Agglomerative Clustering Method, AA Aljohani, EA Edirisinghe, DTC Lai, Proceedings of SAI Intelligent Systems Conference, 590-611
[2] 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
[3] 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, D Lai, O Malik, Journal of Petroleum Science and Engineering 176, 369-380, 2019
[4] 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
[5] 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

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

RESEARCH OUTPUTS (PATENTS, SOFTWARE, PUBLICATIONS, PRODUCTS)

1. Best Abstract and Best Poster for The Cardiac Society of Brunei Darussalam’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

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 – 18 and 23 – 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 - 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 on National Health Survey data with Ministry of Health (Aug 2017 - present)
- Applying Machine Learning techniques to study driving behaviours collected from simulation (Aug 2017 - present)
- Semi-supervised hierarchical clustering with University of Loughborough, UK (Feb 2016 - present)
- Metaheuristics in semi-supervised Fuzzy c-means (Mar 2015-present)
- Data Analytics of Cancer Registry with Ministry of Health (Aug 2014 - present)

Past Project:
1) 2) Evaluate Effects of Cardiac Rehab Programme with MOH (Jun 2014 - Sept 2017)
2) Semi-supervised Fuzzy c-Means clustering methodologies for Real-World Biomedical Data with University of Nottingham (Oct 2010 - May 2014)

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.