Receives >$1.3 million ARPA-E project award, aiming to reduce the timeline for designing and validating 3D- printed turbomachinery components from 2-5 yrs to 1-2 yrs.

Chen, A., Darbon, J. and Morel, J.M., 2014. Landscape evolution models: A review of their fundamental equations. Geomorphology, 219, pp.68-86.

Putting optimal back on the table for real applications using Quantum as an accelerator

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. 

Rodrigo Lopez Negrete obtained his BS and MSc from the Universidad Iberoamericana in Mexico City in 2005 and 2007, respectively. He obtained his PhD from Carnegie Mellon University in Pittsburgh, PA in 2011. All his degrees are in chemical engineering, specializing in process systems engineering, nonlinear programming, and controls.

Dan is a Senior Operation Researcher in Control & Optimization at GE Research. She joined GE in 1998.

Marissa Brennan is an Edison Engineer at GE Research that specializes in creating new materials and material processing capabilities. The durable innovations Marissa has been a part of creating contribute to substantial impacts in the aviation industry enabling safer, energy efficient, advanced technologies for flight.

Tari, L., Mulwad, V. and von Reden, A., 2016, June. Interactive online learning for clinical entity recognition. In Proceedings of the Workshop on Human-In-the-Loop Data Analytics (p. 8). ACM.

Yan, W., 2012. Toward automatic time-series forecasting using neural networks. IEEE Transactions on Neural Networks and Learning Systems, 23(7), pp.1028-1039.

Xin Li is a research scientist in the Radiation Imaging lab. Xin's work aims at pushing the limits of industrial inspection and metrology using X-ray and CT. The work is important to products and services in aviation, power, oil & gas, and advanced manufacturing.

Xin holds degrees in Engineering Physics and Electrical and Computer Engineering and has broad research experiences in imaging system design and optimization, image quality evaluation, image quality improvement, and image analysis.