Industrial and Systems Engineering Student with a Minor in Data Engineering at the National University of Singapore.
Hello!
Supply Chain & Operations
Corporate & Investment Bank, Corporate Analyst Development Program (CADP) 2022
Supply Chain Fulfillment Experience Team
> Designed fulfillment solutions for supply chain systems to improve user experience and meet business requirements.
> Coordinated testing across 8 regions and debugged discrepancies between business and system logic.
Corporate Supply Chain Exellence Team
> Analyzed key drivers of digital transformation and the development of digital competencies in the organization.
> Conceptualized lighthouse data science project on order lead time prediction.
Tools & frameworks: Tableau, Confluence
Smart Manufacturing Applications and Research Centre (SMARC) Data Analytics Team
> Built and deployed Power BI data dashboard with real-time analytics and anomaly detection features.
> Developed production scheduling tool for optimizing resource allocation using operations research techniques, reducing planning time from 12hrs to 5mins.
> Built expenditure forecasting machine learning models using classification and deep learning methods to optimize budget allocation.
> Designed data collection pipeline for model and deployed forecasting web-app in Docker container.
> Presented projects to senior management and received buy-in to further develop tool capabilities.
Tools & frameworks: Power BI (DAX, SQL), Git/Github, Google OR-Tools, tkinter, sklearn, Keras, Docker, Flask, plotly/Dash
Public Sector Science & Technology Policy and Plans Office
Investigated the automation landscape in Singapore by identifying emerging technologies, market players, implementation strategies and policy interventions. Examined approaches and frameworks in implementation science and how they may be employed with technology interventions.
Leading undergraduate and graduate students at NUS to compete in the DJI RoboMaster Robotics Competition.
> Oversaw the procurement, development, and deployment of robots from 6 project teams.
> Implemented Agile methodologies and scrum framework to manage multiple teams.
> Established credit-bearing module for team members.
> Secured $50k in funding and forged research partnerships with MNCs.
> Initiated collaborations with external organizations and other robotics communities.
> Conducted outreach, recruitment, and training programs to grow team from 30 members to 70.
> Placed 4th globally (1st in SEA) for RoboMasters University Championship Online Competition.
Links: Team Website |
Featured on NUS Engineering |
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Director of organizing committee for RVRC Freshman Orientation Program (FOP). Led a committee of 83 student leaders through the planning and coordination of the annual orientation camp for over 300 participants. Conceptualized and adapted camp programs to digitized online platforms. Engineered the first Online Virtual e-FOP amid COVID-19 pandemic.
Strike Observer Mission Officer, planned and executed coordinated air-land fire missions.
Best PT award for Service Term |
Platoon Commander (Lieutenant) |
Bilateral Exercise with U.S. Army 2019.
Assembly and operation of ShopBot PRS Alpha CNC Milling Machine. Self-taught electrical systems to convert internal electrical components to run on 230V. Carpenter for solid wood and peranakan style designer furniture.
Ridge View Residential College Program (RVRC) | RVRC Freshman Orientation Program 2020 | RVRC Ultimate Frisbee Team | RVRC Swim Team | NUS Robomaster Robotics Team
GPA: 4.76, Honors (Highest Distinction)
FIRST Robotics Team 4817 Captain | Robotics Club | National/IASAS Swimmer | National Honors Society | Science National Honors Society
GPA: 4.19 (Weighted)
Team 955 Robotics Fabrication Lead | Team Captain/MVP for Varsity Swimming | State Swim Team | National Honors Society
GPA: 3.95
Solidworks, AutoCAD, Inventor, Fusion360, Mastercam, G-Code, @RISK, Python, SQL, HTML, CSS, Django
Capsule Endoscopy is an invaluable test for the investigation of small bowel pathology, but presents considerable challenges and limitations, such as long and laborious reading times, risk of missing lesions, and lack of locomotion.
This project aims to develop a deep learning model to accurately detect abnormalities in endoscopy images, thereby serving as an aid for the human reader (Gastroenterology specialist). The model will utilize computer vision techniques and transfer learning to leverage on existing pre-trained models.
Choice modeling attempts to model the decision process of individuals given a set of choices. These models predict the outcome of discrete choices, often based on the comparison of utility amongst the options. They are highly relevant in the consumer industry to estimate how consumers will respond to new products or services.
The choice modeling field has been dominated by theory-based modeling approaches. These approaches rely on some theoretical assumption to decision approach, leaving the predictions susceptible to subjective biases. In contrast, machine learning (ML) models can learn decisions from past data without forming any prior assumptions. This approach can remove the biases associated with theory-based models and focuses on the accuracy of the decision outcome. Using these ML models we can then attempt to uncovering how the model arrives at its prediction, thereby the revealing the model's decision process.
Experimenting with deployment of ML models. Attempting to predict stock price movements with deep learning methods.
Currently incorporating other common indicators such as RSIs and MACD lines.
Utilizing Keras, Flask, Heroku, Google Colab
GitHub Repo
Learnt the elements of full stack web-dev. Built a recording, tracking, and visualization interface for procurement and inventory of robot parts. Integrated features such as user authentication, search queries, automated data entry and validation.
Utilized Apache web server, Django framework and PostgreSQL DBMS.
GitHub Repo
As the Vaccine Cold Chain requires specialized storage facilities, nodes in the COVID-19 transport network are limited by transport hubs with the required facilities. This paper highlights the possible routes of transport for the vaccines which are time-effective and cost-effective from the distribution hubs to Singapore, taking into consideration the optimum conditions for the vaccines to be at their maximum potency. Operation Research (OR) methods are applied to determine the most time-effective and cost-effective out of the different transportation means, i.e land, air and sea, from Pfizer’s manufacturing plants in Michigan and Belgium to Singapore. Some constraints and considerations (e.g the optimal temperature, the size of the boxes carrying the vaccine doses) are put into place to enhance the reliability of the solutions.
The Personal Mobility Device (PMDs) were among the most used modes of transportation in Singapore, known for its practicality and affordability in comparison to cars and motorbikes. Following the ban of PMDs by the Land Transport Authority, most food couriers were left without a viable form of transportation. Considering both the economic and non-economic factors, uncertainties and risks were modelled and forecast through Monte-Carlo Simulations. This project examines and recommends the best alternative for the food couriers following the PMD ban.
In disaster relief aid, the food provided to victims consists of bulk items that are readily available. The nutritional value of these items are often overlooked and as a result, victims do not receive the minimum nutritional requirement. The project aims to provide a template of food combinations that fulfills the minimum nutritional requirement. This optimal combination serves as a guide for disaster relief organizations to stock and distribute aid, minimizing the total cost of food supply while ensuring the foods meet the minimum nutritional requirement.
Carbon harvesting temperature optimization of Monoethanolamine carbon-capture systems at atmospheric pressure. Temperature optimization for Carbon Dioxide release in post combustion carbon capture. Coal-fired power plants are notorious for releasing large amounts of carbon dioxide into the atmosphere; the ability to capture carbon dioxide released from these plants are critical to reducing our carbon footprint. The Post-combustion carbon capture process utilizing the chemical Monoethanolamine (MEA) heated in a pressurized chamber. It is an expensive but efficient way of capturing carbon dioxide from coal-fired power plants. Because carbon capture is critical to reducing our carbon footprint, the ability to conduct the process at an optimal temperature in an unpressurized environment is critical for the overall performance and efficiency of the post-combustion process.
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