Ron Taieb
Statistical machine learning, generative modeling, probabilistic learning, and applied AI.
I work at the intersection of statistical methodology and modern machine learning systems, with interests spanning generative models, graphical models, signal processing, symbolic music analysis, and representation learning.
About
I am pursuing a Ph.D. in Data Science at Tel Aviv University. My research focuses on probabilistic generative modeling, statistical machine learning, and structured dynamical systems.
Alongside research, I have experience in applied data science, forecasting systems, statistical modeling, and AI engineering.
Publications
Probabilistic Multilabel Graphical Modelling of Motif Transformations in Symbolic Music
Probabilistic graphical modeling framework for analyzing motif transformations in symbolic music.
arXivProjects
Beethoven Motif Transformations CRF
Probabilistic graphical modeling framework for motif transformations in symbolic music using CRFs and interpretable transformation families.
Diffusion Models with Alternative Noising
Research-oriented implementation exploring alternative diffusion noising processes and schedules.
Goodreads Fantasy Subgenre Classification
NLP and topic modeling framework for literary genre and subgenre classification.
Robust K-Centroids Clustering
Comparative implementation of robust clustering methods.
Experience
Bank of Israel — Research Division
Statistical modeling, forecasting systems, and research-oriented data science workflows.
AI Engineering — Stealth Startup
Built applied AI pipelines involving OCR, information extraction, embeddings, retrieval-augmented generation (RAG), and document analysis systems.
Teaching Assistant — Statistics A&B for Economics
Teaching assistant at the Hebrew University supporting probability, statistical inference, and applied statistical programming.