My current research:

Automatic pathological speech assessment can provide reliable and cost-effective computer-aided tools to assist the clinical diagnosis and management of speech disorders. Among such automatic analyses are speech intelligibility assessments, pathology detection, i.e., discriminating between normal and pathological speech, and classification of different speech pathologies. For my Ph.D. research, I am working on developing reliable and novel machine learning and signal processing techniques for analysing continuous speech for healthcare applicable to different pathological conditions and languages.

Audio Signal Processing, Machine Learning, Deep Learning

My previous research:

During my Master's studies, I worked on biological signal processing with my Master's thesis especially being focused on the extraction of respiratory information from ECG signals and investigating its application for sleep apnea detection. I worked on Gaussian Process modeling and phase space reconstruction methods to extract respiration estimation from ECG, and also I developed a sleep apnea detection system based on ECG information. For my Bachelor's thesis, I worked on designing and implementing a medical device for preventing Varicose and DVT conditions based on prolonged immobility detection in legs and applying neuromuscular electrical stimulation when needed (patented in Iran). During a research internship for a neuroscience project, I also worked on investigating the phase-amplitude coupling in the extracellular neural activity recordings.

Biological Signal Processing, Machine Learning, Neuroscience, Hardware Design