Hands-on work
Projects
Applied research projects that demonstrate the methods from the dissertation in working systems — from physics simulation to live financial markets to real-world social networks.
Aerodynamic Shape Optimization via Genetic Algorithms
→ Minimum-drag geometries evolved from scratch using physics simulation
Applied evolutionary algorithms to optimize aircraft geometry for minimum aerodynamic drag, using Blender as the physics simulator and drag coefficient as the fitness signal. This project serves as a direct applied demonstration of the EA-surrogate paradigm central to the dissertation — real-world physics replaces expensive analytical gradient computation.
Key components
- Genetic Algorithm with real-valued encoding
- Blender physics engine as fitness evaluator
- Surrogate model to reduce simulation calls
- Convergence analysis across multiple generations
Reinforced Trading Bot — C51 Distributional DQN
→ Achieved consistent profitability without any hand-crafted rules or labelled data
Trained a Categorical Deep Q-Network (C51) on live candlestick market data. The agent autonomously learned when to trigger stop-losses, execute buy/sell signals, and switch between long and short positions — with no human-designed rules. Demonstrates that distributional RL outperforms standard DQN in environments with high reward variance, such as financial markets.
Key components
- C51 distributional return modelling
- Live OHLCV candlestick data pipeline
- Learned stop-loss, buy, sell, long/short strategies
- Gymnasium custom trading environment
- Compared against DQN and random baselines
Information Cascade Prediction via Graph Embeddings
→ DFS-biased node2vec outperforms state-of-the-art CasFlow on cascade prediction
Investigated whether depth-first or breadth-first structural priors produce better embeddings for predicting information cascade popularity. DFS-biased node2vec consistently outperforms the state-of-the-art CasFlow model — establishing that deep structural exploration captures diffusion dynamics better than breadth-first neighbourhood aggregation on social network cascade data.
Key components
- DFS-biased node2vec embedding
- Comparison vs CasFlow baseline
- Cascade popularity as regression target
- Real-world social network datasets
- Depth-first prior as key inductive bias