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.
- Project a collaboration with the Human Biomolecular Atlas Program (HuBMAP) and Lung Molecular Atlas Program (LungMAP), which are leading an unprecedented effort **funded by the National Institutes of Health (NIH) to map the tens of trillions of cells in the body and the organs, including the lungs
- Study could promote a greater understanding of the cellular response triggered in the lungs by COVID-19 that often lead to patients being placed on ventilators due to swelling that makes it diff
- Mobile platform intended to produce >1,000s of ready-to-use doses at the site of need in under 3 days
- Project leverages GE’s expertise regarding synthetic method for producing industrial amounts of DNA
- GE’s DNA-based approach could be compatible with new, recently approved RNA-based COVID-19 vaccines
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.