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.
Prior to joining GE in 2001, Dr. Iyer graduated with a Ph. D in AI from the Ohio State University, while working as a graduate researcher for DARPA’s RaDEO program, targeting rapid exploration of large design spaces. In addition to his Ph. D. in Computer Science from the Ohio State University, he has a Master’s in Computer Science from the University of South Florida and a Bachelor’s degree in Electrical Engineering from the University of Bombay.
His recent research interests include robust machine learning, sequential decision making, active learning, adversarial machine learning and generative design. He is currently engaged in research dealing with the application of epistemology to studying “knowability”, interpretability as well as general and adversarial robustness of machine learning and AI models. He is deeply interested in the full potential of additive manufacturing and the ability of machine learning to realizing that potential. He has over 30 peer-reviewed publications and 45 filed patents.
Previously, Dr. Iyer was PI on a 4-year Shared Vision program between GE Research and Lockheed Martin leading the development of human-in-the-loop machine learning analyses for automated anomaly detection and condition analyses of military aircraft and other assets. Dr. Iyer was also the PI on an effort where he successfully conceptualized, implemented and enabled onboard testing of a belief-calculus based approach for evidence fusion to assess the condition of gas-path components in the JSF engine. He has been a technical lead on an internal program targeting the development of cloud-based platform for automated and scalable hyperparameter optimization of machine learning models. He has also lead a team targeting the transparent forecasting and justification of capital reserve requirements under federally mandated stress scenarios.
More recently, Dr. Iyer has been the technical lead on a program targeting the development of large scale, onboard deep learning solution for automated defect characterization and in situ control in Additive manufacturing machine. Presently, he is a lead contributor on an ARPA-E program targeting generative design to improve manufacturability of an additively manufactured part.
To doubt everything or to believe everything are two equally convenient solutions; both dispense with the necessity of reflection.