I build practical, production-grade AI — from data pipelines and deep learning models to scalable web platforms. Currently CTO at Aimental, AI engineer at Elumind Centres working on EEG and bio-sensor systems, and instructor at Maktabkhooneh teaching applied ML and Python.
I build practical AI systems — from EEG pipelines and computer vision to scalable web platforms — and lead engineering teams that ship them in production.
Hover any skill for detail.
Neural architectures for EEG, vision & sequence tasks.
Robotics, detection & perception at production scale.
EEG, bio-sensors & time-series workflows.
Evolving solutions where gradients don't reach.
Pipelines, dashboards & insight from raw data.
Scalable apps, infrastructure & performance.
Designing and training neural architectures for perception, sequence, and biosensor tasks — PyTorch, TensorFlow, Keras, scikit-learn, CUDA, and ONNX for production inference on health-tech platforms.
Leading engineering and applied-AI strategy for an online mental health consultation platform — architecting scalable infrastructure, guiding product innovation, and shipping AI-driven services from pipeline to deployment.
Building intelligent solutions for brain-health — deep learning architectures for EEG and bio-sensor data, signal-processing workflows, and AI-powered analytical dashboards used in clinical neurotherapeutic practice.
Teaching genetic algorithms, Python programming, and applied machine learning to 1,300+ students — translating dense theory into skills engineers ship with. Previously also taught physics and genetic algorithms in academic settings.
Led technology strategy at a startup studio — transforming early-stage pitches into viable products, overseeing technical architecture, and supporting entrepreneurs across Canada, Iran, and Germany.
AI consulting for startups, specialist roles in computer vision and deep learning, and years competing in international robotics — building real-time image processing systems and reinforcement-learning agents from the ground up.