Mohammad Panahi

Senior AI Technical Lead / CTO

AI Technical Lead & systems architect — LLMs, RAG, computer vision and production AI at scale.

Experience

  • Chief Technology Officer (CTO) — Booq

    2025 – Present · Isfahan

  • AI Engineer — Kartez Contract

    2025 – 2026 · Isfahan

  • AI Researcher & Engineer — Sino

    2022 – 2025 · Isfahan

  • Software Engineer — Atlas Peimay Hooshmand

    2021 – 2022 · Isfahan

    Delivered software solutions; managed, reviewed and accepted large projects built by external contractors.

  • AI Researcher — National Elites Foundation (Shahid Ahmadi Roshan program)

    2021 – 2022 · Isfahan

    Persian text-to-speech research, commissioned and supervised by MCI / Hamrah-e Aval (Iran's largest mobile operator) under Iran's National Elites Foundation.

  • AI Researcher — Private company (name withheld)

    2020 – 2021 · Isfahan

    Speckle-noise reduction in MRI imaging and related modalities.

  • Software Engineer — Sahand Sanat Partikan

    2019 · Isfahan

    Built a SIM-card hardware-management module driving AT commands, in Qt/C++.

Education

  • PhD — Isfahan University of Technology

    2025 – Present · Isfahan

  • MSc — Isfahan University of Technology

    2022 – 2025 · Isfahan

  • BSc — Isfahan University of Technology

    2018 – 2022 · Isfahan

  • Diploma — Shahid Ejei School for Exceptional Talents

    2014 – 2018 · Khomeinishahr, Isfahan

    SAMPAD — Iran's national gifted-students programme.

Research

  • Predicting gene expression (molecular biology) from digital pathology images

    2025

    Gene-expression assays are slow, expensive and logistically painful. Whole-slide pathology images are fast and cheap. This thesis asks whether the second can stand in for the first — recovering molecular-biology signal directly from gigapixel pathology images, under a hard constraint on compute and latency. The approach combines graph neural networks, vision foundation models, large-language-model techniques adapted for this task, and statistical/probabilistic methods.

  • Simulating human visual cognition and distinguishing real perception from hallucination

    2022

    A year-long study using generative models — autoencoders and GANs — to model how the human brain constructs visual understanding, and to distinguish genuine perception from hallucination.

Skills

  • AI & Data

    Large Language Models (LLM), Retrieval-Augmented Generation (RAG), Natural Language Processing, Chatbots & QA systems, Computer Vision, Image Processing, Satellite image interpretation, Medical image analysis, Graph Neural Networks, Deep Learning, Machine Learning, Generative models (GAN, Autoencoder), Speech synthesis & recognition (TTS/ASR), Data Mining, Data Analysis, Business Intelligence

  • Software & Web Engineering

    Software Engineering, Python, Programming (general), Web engineering, Databases (relational & NoSQL), Qt / C++ (embedded tooling)

  • Infrastructure & DevOps

    DevOps, GPU optimisation, High-load architecture

  • Leadership

    Technical leadership (CTO), Project planning & management, Vendor/team delivery oversight, Product 0→1→sale, Mentoring