cv

Skills

Programming Python, C++, JavaScript, Dart
ML & LLMs PyTorch (CUDA), Hugging Face (Transformers, Datasets), TensorFlow, scikit-learn, Activation Steering, TransformerLens, data curation & evaluation
Systems & Backend Node.js/Express, MongoDB, REST APIs, Docker/Podman, CI/CD, Git, Linux
Mobile & BLE Flutter/Dart, flutter_blue_plus, background services & notifications, on-device pipelines
Cloud/Compute Cloud GPUs, batch pipelines

Projects

  • 2025–Present
    LLM Activation Steering with Guardrailed Evaluation
    • Controlled verbosity and tone in Llama 3.1 and Qwen-2.5 via residual-stream injections (layer/α sweeps).
    • Built judge-driven evaluation with JSON-schema checks; reduced hallucinations and stabilized behavior.
  • 2025
    dKS — Efficient Multidimensional Two-Sample Testing
    • Near-linear 2D algorithm (O(n log n)); ε-accurate, unit-invariant IPM; stability gains vs. mdKS.
    • Paper: Efficient and Stable Multidimensional Kolmogorov–Smirnov Distance (SIMODS submission; arXiv: 2504.11299).
  • 2024–Present
    MotionPI — Privacy-First Wearable Sensing (Full-Stack)
    • Flutter + BLE wristbands → Node.js/Express + MongoDB; background pipelines and secure ingestion.
    • Deploy in Huntsman Cancer Institude.
    • Interactive visualization for activity-trigger thresholds.
    • Paper: Designing a Secure Distributed Participant Data Collection System — EAI SmartSP 2025 (accepted).
  • 2023
    Region-Aggregated Spatial Scan Statistics
    • Replaced centroid scans with multi-point region sampling; increased detection power; C++ experiments.
    • Under review - double blind.

Education

  • 2023 – Expected 2027
    Ph.D. in Computer Science
    University of Utah, Salt Lake City, UT, USA
    • Advisor: Prof. Jeff Phillips.
    • Focus: LLM interpretability; geometric data analysis; spatial statistics.
    • Relevant coursework
      • Machine Learning
      • Probabilistic Machine Learning
      • Deep Learning
      • Data Mining
  • 2019–2022
    M.Sc. in Computer Science (Algorithms & Computation)
    University of Tehran, Tehran, Iran
    • GPA: 17.8/20
    • Thesis: Graph-theoretic modeling of 2020 Australian bushfire propagation.
    • Relevant coursework
      • Advanced Algorithms
      • Approximation Algorithms
      • Randomized Algorithms
      • Quantum Algorithms & Computation
      • Graph & Network Algorithms
      • Internet Algorithms
      • Distributed Systems

Honors and Awards

  • 2023
    Graduate Fellowship
    University of Utah, Salt Lake City, Utah, United States
  • 2020–2022
    Top-Talent Scholarship (merit)
    University of Tehran

Contact

Email foad.namjoo@utah.edu
GitHub https://github.com/foadnamjoo
LinkedIn https://www.linkedin.com/in/foadnamjoo
Languages English, Hawrami, Kurdi, Persian