ML & applied-mathematics researcher working at the intersection of spectral methods, regularization theory, graph signal processing, and learning-to-rank — with an applied bent toward demand forecasting and information retrieval.

I build principled, math-forward methods and turn them into working systems. Recurring threads in my work:

• Spectral & graph signal processing — Laplacian/eigenstructure methods, signals and flows on (directed) graphs
• Regularization theory — well-posed, parameter-insensitive formulations for recovery and ranking
• Learning-to-rank & information retrieval — multi-metric rank prediction, search, and retrieval
• Applied forecasting — demand and time-series modeling grounded in the above

Day to day I work in my own Python and Scala research stacks.

• Latent Aspect Detection from Online Unsolicited Customer Reviews — an unsupervised method for extracting latent aspects from reviews. Forouhesh, Mansouri, Fani (2022). arXiv:2204.06964 · code
• Tracking Legislators' Expressed Policy Agendas in Real Time — real-time computational-social-science pipeline for policy-agenda tracking. code

🎁 Project	📝 What it does	⭐ Stars	🍴 Forks
Latent Aspect Detection	Unsupervised extraction of latent aspects from customer reviews (arXiv:2204.06964)
Tracking Policy Agendas	Real-time tracking of legislators' expressed policy agendas
Persian POS Tagger	Conditional Random Field framework for Persian part-of-speech tagging
Twitter Sentiment Engine	BERT-based Persian Twitter sentiment-analysis framework
Persian Relatio	Persian reimplementation of Elliott Ash's Relatio narrative-extraction framework
Inverted Index	A simple search engine built from scratch

Languages

ML & NLP

Distributed & Streaming

Infrastructure & Ops