Resume

• Built and deployed algorithmic market-making strategies, incorporating volatility and skew estimation, order flow modelling, queue-aware execution, market impact, and reference price dynamics.
• Built a dynamic liquidity allocation model for smart order routing, reducing slippage and transaction costs by an average of 5.8% through real-time optimization over aggregated fragmented order books.
• Developed cross-exchange rebalancing methods that optimised timing and execution of inventory transfers under latency constraints, transaction costs, funding rates, and market liquidity.

• Monitored production market-making algorithms, managing real-time risk parameters and system health to minimize inventory exposure during high-volatility periods.
• Designed and deployed a live trading terminal using ccxt and native exchange APIs for real-time position and order tracking, integrating Tardis.dev for historical position reconstructions.

• Designed scalable likelihood-based estimation algorithms for functional scale models, optimising computational performance through vectorised computations and parallel processing.
• Reduced execution time of large-scale Monte Carlo simulations by 92.3% on average using NumPy vectorisation and parallel computing.

• Researching decomposition theorems, generic chaining, majorizing measures, weak convergence, and Donsker–Skorokhod theorems for stochastic processes satisfying absolute regularity.
• Organized and led a weekly graduate seminar on weak convergence and empirical process theory.

The full technical writeup, including a walkthrough of the \(\gamma_2\) functional and the majorizing measure theorem, is on the Blogposts page.

• Honours Programme, GPA 8.9/10 (magna cum laude).
• Developed a novel functional stationarity test for multidimensional diffusion processes for a thesis project, implementing and packaging the mathematical framework into an open-source Git repository (see Projects).
• Relevant coursework: Measure Theoretic Probability, Quantitative Financial Risk Management, Stochastic Processes, Stochastic Integration.

• Relevant coursework: Statistical Learning, Time Series, Deep Learning, Risk Theory.

• Extending the functional GARCH framework to a generalized autoregressive score (GAS) model to estimate and capture time-varying intraday volatility surfaces.
• Designed efficient estimation procedures using B-splines, applying Numba JIT compilation to enable scalable modelling of volatility surfaces from granular intraday return data.

• Implemented Hierarchical Risk Parity via tree clustering using scipy.cluster to stabilize high-dimensional asset allocation, bypassing classical covariance inversion to eliminate noise sensitivity.
• Generated a mean alpha premium of 3.9% above the benchmark across diverse simulated horizons in a look-ahead-free method that outperformed actively rebalanced benchmark portfolios.

• Represented the interests, opinions, and concerns of the PhD candidates.
• Contributed to improvements in budgeting, strategic planning, and day-to-day matters.

• Participated in live Q&A sessions and (virtual) educational fairs.
• Assisted prospective students by answering questions on the online chat platform.

Programming & Tools

• Python
• numba
• asyncio
• ccxt
• NumPy & SciPy
• SAS
• Bash
• Tardis.dev

Mathematics & Statistics

• Stochastic processes
• Empirical process theory
• Generic chaining
• Time series & volatility modelling
• Statistical estimation