Hi 🤗, this is my homepage of an autodidact who's deeply passionate about probabilistic machine learning and forecasting.
- Real-world applications: Demand/Sales Forecasting, Business Problems (Credit Scoring, Customer Retention, Portfolio Optimization, Inventory Optimization).
- Machine learning methodologies:
- Time Series (TS) Deep Learning: TS Forecasting, TS Representation Learning, TS Generation.
- Probabilistic/Statistical Machine Learning: Deep Generative Models (Energy-Based Models, VAE, Normalizing Flows, Diffusion Models), Approximate Bayesian Inference (MCMC, VI).
- High-dimensional Statistics: Variable Selection, Missing Data, Imputation.
- Programming languages:
,
.
- Machine learning (Deep learning) frameworks:
,
, JAX.
- Probabilistic Programming: Pyro, PyMC3
- Visualization Libraries:
, ggplot2.
- Editors:
,
.
- Database:
