Quantitative Researcher | Bayesian Statistics | Financial Markets
Core Expertise:
Hi there! I’m a quantitative researcher at Brevan Howard, where I develop statistical frameworks for signal generation in global macro markets. I have experience building and deploying Bayesian models that help navigate regime changes and market transitions.
I hold a PhD in Statistics from the London School of Economics, where I specialized in Sequential Bayesian Learning for State Space Models under the guidance of Kostas Kalogeropoulos and Pauline Barrieu. My doctoral research centered on developing methodologies for parameter estimation in dynamic systems, with particular emphasis on latent variable models and time-varying environments.
Beyond my professional work, I have contributed to open-source projects and share technical insights here. I am happy to engage with fellow quantitative professionals and researchers exploring innovative applications in financial markets..
Bayesian Statistics, Parameter Estimation, Prediction, Model Selection and Validation, Uncertainty Quantification
Gaussian Processes, State Space Models, Mixture Models, Regime and Changepoint Detection
Markov Chain Monte Carlo, Sequential Monte Carlo, Variational Inference/Optimization, Particle Filtering
Python, Julia, R
PyMC, Stan, Turing
Linux, Distributed Computing (AWS, JuliaHub), Version Control (Git, GitHub)
Data Visualization (Dash, Plotly), Data Pipeline Engineering
Time Series Analysis, Forecast Aggregation Methods, Bottom-up Analysis
Feature Generation and Selection, Missing Data Imputation, Manipulation and Exploration
Critical Thinking, Adaptability, Problem Solving
Oral (Teaching, Seminars, Conferences), Written (Papers, Editing, Blogging), Project Management (PhD Thesis), Teamwork (Collaborations)