Heydar Davoudi is a Biomedical Engineering PhD student at The Johns Hopkins University School of Medicince. He received an Msc and BSc in Electrical Engineering from Sharif University of Technology and Iran University of Science and Technology, respectively. His master thesis described a statistical learning method inspired by the human nervous system. His research interests include neural prosthesis, sensorimotor learning, signal processing, machine learning, and systems neuroscience.
Email: davoudi@jhu.edu
Personal Homepage: http://sites.google.com/site/heydardavoudi
Publication:
H. Davoudi, A. Taalimi, E. Fatemizadeh, "Extracting activated regions of fMRI data using unsupervised learning", The International Joint Conference on Neural Networks (IJCNN'09), Atlanta, USA, June 2009.
H. Davoudi, B. Vosoughi Vahdat, "A biologically plausible learning method for neuro-robotic systems," The 4th International IEEE EMBS Conference on Neural Engineering (EMBS NER'09) Antalia,Turkey, April 2009.
H. Bayati, H. Davoudi, E. Fatemizadeh, "A heuristic method for finding the optimal number of clusters with application in medical data", The 30th IEEE Conference on Engineering in Medicine and Biology Society (IEEE EMBC'08), Vancouver, Canada, August 2008.