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AI for Financial Services
As an accomplished data scientist with strong engineering foundation, I am passionate about leveraging AI to drive business efficiency and solve complex enterprise problems. With over 7 years of industry experience, I have developed scalable AI products and solutions across finance, e-commerce, and technology sectors. I excel in leveraging machine learning (ML), natural language processing (NLP), and generative AI (GenAI) to drive digital transformation and optimize business processes. At EFG International, I have implemented LLM solutions for internal knowledge search (RAG system) and AI agents for credit memo generation, significantly enhancing productivity for business users. Additionally, I am skilled in fine-tuning LLMs and applying graph analytics to uncover hidden insights and detect anomalies in financial data. I am particularly well-versed in integrating AI into enterprise operations, collaborating with cross-functional teams, and fostering AI adoption through training and mentorship. I earned my Ph.D. in Information Systems in 2017 and have extensive publications and patents in AI and data science. Beyond work, I’m a curious traveler who always enjoys engaging with diverse perspectives and exploring new experiences.
Carnegie Mellon University
Doctor of Philosophy (PhD), Artificial Intelligence
August 1, 2014 – July 1, 2015
Singapore Management University
Doctor of Philosophy (PhD), Information Systems
January 1, 2013 – July 1, 2017
Nanyang Technological University Singapore
Master of Science (M.Sc.), Mathematical Sciences
January 1, 2011 – July 1, 2012
Nanyang Technological University Singapore
Bachelor of Engineering (B.Eng.), Computer Engineering
August 1, 2005 – July 1, 2009
Deutsche Bank
AI Quant Strat
March 1, 2026 – Present
London Area, United Kingdom · On-site
EFG International
Senior Data Scientist (VP)
January 1, 2024 – March 1, 2026
Singapore · On-site
Credit Suisse
Senior Data Scientist (AVP)
November 1, 2021 – December 1, 2023
Singapore · On-site
Agoda
Data Scientist
September 1, 2019 – November 1, 2021
Singapore · On-site
SAP
Machine Learning Developer
July 1, 2017 – July 1, 2019
Singapore · On-site
IBM
Research Intern
May 1, 2016 – July 1, 2016
Singapore · On-site
Carnegie Mellon University - H. John Heinz III College
Graduate Teaching Assistant
January 1, 2015 – April 1, 2015
Greater Pittsburgh Area · On-site
Text to Knowledge Graphs with LLMs
August 1, 2025 – September 1, 2025
This is an application that extracts knowledge graphs from natural language text. It's a Gradio web app that uses either OpenAI GPT-4.1-mini via Azure or Phi-3-mini-128k-instruct-graph via Hugging Face to extract entities and relationships from text, then visualizes them as interactive graphs.
Real-time Dwell Time Prediction Using Passive Wi-Fi Data
May 1, 2016 – July 1, 2016
The vast penetration of wireless devices today gives rise to a secondary functionality as a means of tracking and localization. In order to connect to Wi-Fi networks, mobile devices have to scan and broadcast probe requests on all available channels, which can be captured and analyzed in a non-intrusive manner. Thus, one of the key applications of this feature is the ability to track and analyze human behaviors in real-time directly from the patterns observed from their Wi-Fi-enabled devices. This project aims develop such a system to obtain these Wi-Fi signatures in a completely passive manner coupled with adaptive machine learning techniques to predict in real-time the expected dwell-time of the device owners.
Self-driving Using Reinforcement Learning
December 1, 2015 – April 1, 2016
Reinforcement learning is at the heart of modern AI techniques. In this project, I apply the main ideas of reinforcement learning called Q-learning to an interesting hypothetical application called Smartcab. Smartcab is a simplistic simulation of a self-driving car that ferries people from one arbitrary location to another. This application demonstrates how Q-learning can be used to train smartcabs to desired self-driving behaviors through trials and errors. The application is developed using Python and the Pygame framework.
Fine-grained Traffic Speed Prediction Using Big Data
June 1, 2015 – April 1, 2016
• Collected large-scale and fine-grained traffic speed readings from sensor networks in two U.S. cities: Pittsburgh and Washington, D.C. • Proposed efficient local Gaussian processes based on matrix factorization for real-time and fine-grained inferences of traffic flow throughout the road networks.
Image Classification
August 1, 2014 – December 1, 2014
We perform image classification on the CIFAR-10 dataset, where each image belongs to one of the 10 distinct classes. The images are small, of uniform size and shape, and are RGB colored. We implement an image preprocessing framework to learn and extract the salient features of the training images. With preprocessing, we see a considerable improvement of more than 15% from the baseline. We also experiment with a variety of classifiers on the preprocessed images and find out that a linear SVM performs the best. We finally experiment with ensemble learning by combining a SVM with a multinomial logistic regression, which marginally improves on the linear SVM at a high complexity cost.
Human Mobility Analytics
January 1, 2013 – May 1, 2015
We collected big mobility data of visitors to the resort island of Sentosa, Singapore. The data was collected through bundle packages and associated RFID-enabled devices. We propose various analytical frameworks to model and predict the visitors’ trajectories. Our methods include revealed preference analysis and reinforcement learning. Applications include designing optimal bundle packages and recommending optimal plans to the visitors to avoid congestion.
Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
Coursera
June 24, 2026 – Present
Neural Networks for Machine Learning
Coursera
June 24, 2026 – Present
IRB Training for SMU Researchers
CITI Program
June 24, 2026 – Present
Neural Networks and Deep Learning
Coursera
June 24, 2026 – Present
Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
Coursera
June 24, 2026 – Present
Structuring Machine Learning Projects
Coursera
June 24, 2026 – Present
Convolutional Neural Networks
Coursera
June 24, 2026 – Present
Sequence Models
Coursera
June 24, 2026 – Present
Natural Language Processing
Coursera
June 24, 2026 – Present
Functional Programming Principles in Scala
Coursera
June 24, 2026 – Present
Pair Programming with AI
June 24, 2026 – Present
Convolutional Neural Networks in TensorFlow
Coursera
June 24, 2026 – Present
Natural Language Processing in TensorFlow
Coursera
June 24, 2026 – Present
Sequences, Time Series and Prediction
Coursera
June 24, 2026 – Present
DeepLearning.AI TensorFlow Developer Specialization
Coursera
June 24, 2026 – Present
Build Basic Generative Adversarial Networks (GANs)
Coursera
June 24, 2026 – Present
Generative AI with Large Language Models
Coursera
June 24, 2026 – Present
Large Language Models: Text Classification for NLP using BERT
June 24, 2026 – Present
Introduction to Prompt Engineering for Generative AI
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's diverse project portfolio, ranging from academic research to industry applications in finance and travel, indicates adaptability and a broad interest in applying data science across various domains. The continuous pursuit of certifications in cutting-edge AI topics (LLMs, GANs, TensorFlow) demonstrates a proactive learning mindset. The experience in global teams (Europe and Asia) suggests an ability to work in diverse environments. The target role of 'Data Analyst' might be a slight mismatch given the candidate's extensive senior-level Data Scientist/AI Quant Strat experience, which is typically more advanced than a standard Data Analyst role. However, the core analytical skills are highly relevant.
Soft Skills & Operational Fit
The candidate's project descriptions highlight problem-solving, innovation (patents), and collaboration with cross-functional teams. The role as a Graduate Teaching Assistant also suggests communication and mentorship abilities. The focus on optimizing operational efficiency and mitigating risks aligns well with practical application in a business context.