I am a Statistics student at the University of Tehran, working on reliable machine learning and applied AI systems. My recent projects study calibration, uncertainty, distribution shift, and sequential decision-making. I also build the software around that work—from experiment pipelines and APIs to interfaces that make results easier to inspect.
I previously studied Electrical Engineering and completed Maktab Sharif's software-development program. I use AI tools throughout development to move faster on implementation, testing, debugging, and documentation, while checking the important decisions against code, data, and reproducible artifacts.
| Project | Focus |
|---|---|
| Calibrated Predictive Reliability | A reproducible C-MAPSS study of remaining-useful-life prediction under operating-condition and fault-mode shift, with a focus on calibration, interval reliability, leakage-safe splits, frozen protocols, and artifact-level verification. |
| PromoGuard Retail Intelligence | Evidence-aware promotion auditing on real retail data. It combines time-aware forecast comparison, uncertainty guardrails, explicit abstention, FastAPI contracts, and a Persian Streamlit review interface. |
| Bearing Prognostics & Value of Information | An in-progress research pipeline for sequential bearing-degradation detection, false-alarm control, and inspection decisions. It separates development, calibration, and external NASA IMS validation rather than re-tuning on the final dataset. |
| Project | What I built |
|---|---|
| AURALIS | Persian and multilingual meeting intelligence with speech capture, evidence-grounded insights, workspaces, and action tracking. Latest release: v0.10.5 — Audio Path Hardening. |
| Professor-Aware Exam Coach | A local-first study workspace built around course-source retrieval, structured feedback, FastAPI, Next.js, and SQLite. |
| Tabrizi Bakery | A responsive bakery website built with Next.js and TypeScript, with an editorial visual system and a live deployment. |
I am most interested in work that needs both statistical reasoning and solid engineering. I try to define the claim before running an experiment, keep data boundaries explicit, and document what the available evidence does and does not support.
My current stack includes Python, scikit-learn, FastAPI, TypeScript, React, Next.js, SQLite, Streamlit, GitHub Actions, and LLM/RAG tooling. I choose the stack around the problem rather than treating it as the point of the project.
My current direction is reliable decision-making for sequential and structured data: calibrated prediction, evaluation under shift, and policies that account for uncertainty before recommending an intervention.
For research, software, or data/AI product collaboration, reach out through GitHub or LinkedIn.