Dr. Ghaemi received the Ph.D. degree in electrical engineering and the M.S. degree in mathematics from University of Michigan in 2010 and 2009 respectively. He was a visiting scholor at ETH in 2008. From 2010 to 2012 he was a post-doctoral associate in the Mechanical Engineering department at MIT, researching supervisory control of order-preserving systems and stochastic analysis of biological systems.
- GE’s experimental MAGNUS gradient coil installed in a GE 3.0 Tesla clinical MRI scanner delivers an unprecedented combination of slew rate and gradient strength for brain imaging, operating in the 500 – 700 Tesla/meter/second (T/m/s) and 200-300 milliTesla/meter (mT/m) range vs.
Dr. Naresh Iyer is a Principal Scientist in the AI and Machine Learning group at GE Research. He has 20 years of experience in the research and application of machine learning to a variety of industry problems, including asset life prognostics, surrogate modeling, multi-objective optimization and decision making under uncertainty. He has developed solutions for a diverse range of industrial applications using methods in supervised, unsupervised, semi-supervised learning and evolutionary soft computing.
What you Need to Know:
Dr. Yiwei Fu is a Research Scientist in the Machine Learning team at GE Research in Niskayuna, NY. He is a reseracher with a strong and diverse background in machine learning, robotics and control & optimization, etc. His current research interests include deep learning for spatiotemporal data; reinforcement learning for control and numerical methods; applied machine learning for materials, grids and robotics, etc.
From big data to Artificial Intelligent (AI)
"Big data is the mine of gold, and AI is the modern mining machinery." Guang describes himself as a gold miner piloting the modern machines. When viewing his research projects, Guang thinks that some are fun, some are meaningful, and some are interruptive. He thinks that at this emergent stage of AI, a good researcher needs to be creative, bold, and brave. There is a saying that Guang likes the most: Go Big or Go Home.
