Courses
Courses
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A snapshot of ongoing specialised training in data-driven industrial optimisation, advanced photonics, and applied software automation.
Statistical Thinking for Industrial Problem Solving
Statistical Thinking for Industrial Problem Solving
Overview: A comprehensive data-analytics program focused on applying data-driven statistical methods for process optimisation and industrial scalability. This curriculum covers foundational and advanced statistical concepts designed to improve quality control, streamline manufacturing processes, and make rigorous, data-backed engineering decisions.
Status: completed
Module 1: Statistical Thinking and Problem Solving.
Module 2: Exploratory Data Analysis
Module 4: Decision Making with Data
Module 5: Correlation and Regression
ASQ-Certified Six Sigma Black Belt (CSSBB) Exam Prep Specialisation
ASQ-Certified Six Sigma Black Belt (CSSBB) Exam Prep Specialisation
Overview: Advanced professional training focused on mastering the full DMAIC (Define, Measure, Analyze, Improve, Control) methodology, waste reduction, and rigorous statistical analysis for industrial process optimization and quality management.
Status: Ongoing
Course 1: Six Sigma for Process Improvement
Course 2: Leadership and Team Management
Course 3: Advanced Statistical Analysis and Tools
Applied Software and Automation Projects
Applied Software and Automation Projects
Lumerical HEAT
Lumerical HEAT
Utilised advanced numerical simulations via Lumerical HEAT to model thermal distribution and heat dissipation within quantum dot downconverter films and Distributed Bragg Reflector (DBR) configurations. These simulations provided critical insights for thermal management, enabling the mitigation of thermal quenching under high-power optical excitation and optimising the integration of high-conductivity sapphire substrates to ensure long-term operational stability.
Python simulation
Python simulation
Developed custom Python scripts implementing the Transfer Matrix Method (TMM) to model the optical electric field distribution and microcavity resonance effects within Top-Emission QLED (TQLED) architectures. By simulating light propagation through multi-layer thin films, the script maps out optical performance across varying top and bottom ITO layer thicknesses to maximize light extraction, optimize cavity length, and precisely position the emission layer at the standing wave's antinode based on the following formula:
Algorithmic Trading Automation Framework
Algorithmic Trading Automation Framework
Overview:
Overview:
Developed a modular Python-based automation framework to execute and manage trading strategies within a virtual demo environment. The system integrates real-time technical indicators to govern automated entry, exit, and risk-management logic.
Why AI Was Essential:
Why AI Was Essential:
As a researcher, transitioning into software automation, writing robust, multi-threaded API communication scripts, exception-handling loops, and real-time data parsing from scratch would have presented a steep, time-consuming coding barrier. Utilizing AI as an interactive development partner allowed for rapid prototyping, syntax debugging, and translating trading logic into clean production code in a fraction of the time.
Project Workflow (Logic Path):
Project Workflow (Logic Path):
Data Ingestion: Pulling real-time pricing feeds via API.
Indicator Calculation: Processing data through custom functions.
Execution Logic: Evaluating market conditions against pre-defined risk parameters.
Automated Placement: Sending order commands to the virtual demo environment.
Validation & Verification Strategy:
Validation & Verification Strategy:
Unlike complex scientific models where ground truth can be abstract, software validation is empirical and direct:
Functional Testing: Verified that script outputs precisely matched manual calculations on historical data subsets.
Execution Logs: Monitored console logs to confirm that API requests returned successful status codes and error-handling triggers functioned correctly during connection drops.
Live Demo Verification: Confirmed system reliability by observing automated trade execution, position sizing, and trailing stop placements in real-time within the virtual sandbox environment.
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