Summary
Reza Keshtkaran is a Machine Learning Scientist focused on health, combining 12 years of experience in ML, deep learning, and adaptive signal processing with a PhD in Electrical and Computer Engineering from NUS. Based in the San Francisco Bay Area and currently at Apple, he applies advanced time-series modeling and neural population dynamics to biomedical signals such as ECG, EEG, and intracortical recordings. His work spans both algorithm and hardware co-design for wearable and implantable devices, and he’s tackled large-scale distributed hyperparameter optimization for training deep models. Reza also develops robust noise and artifact removal pipelines and deployable supervised/unsupervised models for medical assistive technology. Beyond biosignals, he has applied speech processing techniques to emotion recognition and voice conversion, reflecting a broad signal-processing toolkit. His background in electrical engineering and hands-on research-to-product experience enables him to translate complex neural data into practical health solutions.
12 years of coding experience
Bachelor of Science (B.Sc.), Electrical, Electronics and Communications Engineering, Bachelor of Science (B.Sc.), Electrical, Electronics and Communications Engineering at Shiraz University
High School, Mathematics and Physics, High School, Mathematics and Physics at Tohid High School
Doctor of Philosophy (Ph.D.), Electrical and Computer Engineering, Doctor of Philosophy (Ph.D.), Electrical and Computer Engineering at National University of Singapore