Summary
Mohammad Malekzadeh is a Principal Applied Scientist with a PhD in Computer Science and a decade of experience building privacy-preserving, data-efficient on-device AI for wearables and mobile systems. He specializes in multimodal foundation models and self-supervised pre-training for physiological and time-series data, delivering low-compute, personalized inference that balances privacy and utility. His work spans top-tier publications and patents, and he has led device-intelligence teams at Nokia Bell Labs where research translated into open-source releases and spin-out activity. Now at Microsoft Applied Sciences, he focuses on making edge ML more efficient and generalizable, while serving on an advisory network that advances smartphone- and wearable-based health research. An unexpected throughline in his career is repeated success turning academic datasets and privacy discoveries (e.g., MotionSense and federated-DP methods) into widely adopted benchmarks and production-ready systems.
10 years of coding experience
15 years of employment as a software developer
Bachelor's Degree, Computer Software Engineering, Bachelor's Degree, Computer Software Engineering at Shahid Chamran University of Ahvaz (Jundi Shapur)
Doctor of Philosophy (Ph.D.), Computer Science, Doctor of Philosophy (Ph.D.), Computer Science at Queen Mary University of London
Master's Degree, Computer Systems Networking and Telecommunications, Master's Degree, Computer Systems Networking and Telecommunications at Sharif University of Technology
English, Persian, Italian