I’m a Machine Learning Engineer / Data Scientist with a strong mathematical background and a passion for building models that are interpretable, probabilistic, and statistically sound.
My work focuses on combining machine learning, Bayesian inference, and stochastic modeling to analyze complex and noisy data. Professionally, I work as a Pay Equity Specialist / Data Analyst at Mercer, applying statistical modeling in workforce and compensation analytics.
- Probabilistic & Bayesian Machine Learning – Bayesian regression, Naive Bayes, MCMC, MAP, variational inference, uncertainty modeling
- Reinforcement Learning & Bandits – Epsilon-Greedy, UCB, Thompson Sampling, exploration–exploitation frameworks
- Time Series & State-Space Modeling – ARIMA family, VAR, GARCH, HMMs, Kalman filtering, spectral methods
- Stochastic Processes – DTMCs, CTMCs, Wiener processes, Brownian motion, Gaussian processes
- Statistical Inference & Experimental Design – hypothesis testing, A/B testing, Bayesian vs Frequentist methods
- Machine Learning Engineering – model selection, Bayesian optimization, CV, pipelines, reproducibility
I actively expand my skills in applying ML methods to real-world problems, including:
- Recommender Systems – collaborative filtering, content-based filtering
- Natural Language Processing (NLP) – LSI, text representation and semantic analysis
- Dimensionality Reduction & Feature Engineering
- Applied Predictive Analytics and Decision Modeling
I enjoy designing models that explain mechanisms behind data, simulating complex dynamics, and extracting signal from uncertainty.
