Quantitative Researcher | Bayesian Statistics | Financial Markets
Core Expertise:
- Bayesian statistical methods and uncertainty quantification
- Sequential learning algorithms and regime detection
- State space modeling and probabilistic inference
Hi there! I am currently a Quantitative Researcher at Brevan Howard, where I develop and implement statistical methodologies for financial markets.
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..
Experience
Education
Skills
Bayesian Statistics, Parameter Estimation, Prediction, Model Selection and Validation, Uncertainty Quantification
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)
