Group | DML | |
Entrance | 2000 |
Hamid R. Rabiee received his BS and MS degrees (with Great Distinction) in Electrical Engineering from CSULB, Long Beach, CA (1987, 1989), his EEE degree in Electrical and Computer Engineering from USC, Los Angeles, CA (1993), and his Ph.D. in Electrical and Computer Engineering from Purdue University, West Lafayette, IN, in 1996. From 1993 to 1996, he was a Member of Technical Staff at AT&T Bell Laboratories. From 1996 to 1999, he worked as a Senior Software Engineer at Intel Corporation. He was also an Electrical and Computer Engineering adjunct professor with PSU, OGI, and OSU universities from 1996 to 2000. Since September 2000, he has joined the Sharif University of Technology, Tehran, Iran. He was also a visiting professor at the Imperial College of London for the 2017-2018 academic year. He is the founder of Sharif University Advanced Information and Communication Technology Research Institute (AICT), ICT Innovation Center, Advanced Technologies Incubator (SATI), Data Science & Machine Learning Laboratory (DML), Mobile Value Added Services Laboratory (M-VASL), Bioinformatics and Computational Biology Laboratory (BCB) and Cognitive Neuroengineering Research Center (CNRC). He has also been the founder of many successful High-Tech start-up companies in the field of ICT as an entrepreneur. Currently, he is a Distinguished Professor of Computer Engineering and director of the Data Science & AI Innovation Center and DML at Sharif University of Technology. He has been the initiator and director of many national and international level projects in the context of Iran's National ICT Development Plan and UNDP International Open Source Network. He is also a consultant and member of AI in the Health Expert Group at WHO. He has received numerous awards and honors for his industrial, scientific, and academic contributions and holds three patents. He is also a Senior Member of IEEE, Iran's Academy of Science, and Iran's Academy of Medical Sciences Digital Health Group. His research interests include statistical machine learning, Bayesian statistics, data analytics and complex networks with applications in social networks, multimedia systems, cloud and IoT privacy, bioinformatics, and brain networks.
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