PORTFOLIO LOG / 2026
An undergraduate background in Port, Waterway and Coastal Engineering, paired with freight-forwarding and marine cargo insurance experience—now developed through data analysis, simulation, and operations research.
01 / BACKGROUND
My engineering training began with ports, waterways, and coastal systems: the physical infrastructure that makes maritime trade possible. A later freight-forwarding internship exposed a different layer of the same system— cargo operations, insurance decisions, uncertainty, and commercial trade-offs.
That connection now shapes my interests in maritime digitalisation, industrial and systems engineering, supply-chain resilience, and intelligent transportation. I am building the quantitative foundation needed to move from observing operational problems to modelling and improving them.
02 / FEATURED PROJECT
A risk-aware decision-support prototype for maritime cargo insurance and intermodal route planning.
DECISION QUESTION
HarborShield combines a transparent risk model, Monte Carlo simulation, joint batch CVaR, whole-container allocation, and independent synthetic evaluation against four policy alternatives. It also displays public vessel-arrival data from the Maritime and Port Authority of Singapore.
RESEARCH INTEGRITY
Official and simulated inputs are separated. Vessel-arrival volume is not mislabelled as congestion. Model coefficients are not presented as actuarial rates until real claims data can support calibration.
View source registerv0.2 / EXPERIMENT EVIDENCE
Four disruption regimes, 10,000 held-out synthetic worlds per regime, five training seeds, paired uncertainty intervals, and shared-shock sensitivity. Baselines can coincide; the experiment does not assume that a more complex method wins.
Read the experiment evidenceThis is a static evidence page, not a hosted Python application. Built with AI-assisted coding; synthetic evaluation is not real-world validation.
03 / METHOD
Each layer answers a different question and remains visible enough to audit.
Estimate how cargo, packaging, transshipment, weather, and reliability affect claim probability.
Simulate thousands of cargo-loss and delay outcomes instead of relying on a single average.
Make cost, risk, time, and carbon priorities explicit through sensitivity analysis.
Assign whole containers under joint batch risk, then evaluate frozen decisions on separate synthetic samples.
04 / ACADEMIC DIRECTION
Port digitalisation, maritime data, intermodal logistics
Probability, systems modelling, optimisation, decision analysis
Supply-chain resilience, risk coordination, service trade-offs
Multimodal networks, disruption response, sustainable mobility
05 / CONTACT
Code, methodology, data provenance, experiment results, and learning notes are published openly on GitHub.
Visit GitHub profile