Dimitry Gorinevsky is a Consulting Professor in Electrical Engineering with Information Systems Laboratory at Stanford University since 2003. His interests are in analytics applications for the Industrial Internet of Things (IIoT). He is a founder of Mitek Analytics, an IIoT company in Palo Alto, CA. Over the last decade, he has been working on data analytics applications in aerospace systems, energy, and other industries. Before that, he worked on decision and control applications in industry and academia, including a decade at Honeywell. He has authored a book, 180+ papers, and many patents. He is a recipient of Control Systems Technology Award of the IEEE Control Systems Society, Transactions on Control Systems Technology Outstanding Paper Award of the IEEE Control Systems Society, Best Paper Award (Senior Award) of the IEEE Signal Processing Society, and other awards. He is a Fellow of IEEE.

Academic Appointments

  • Adjunct Professor, Electrical Engineering

Honors & Awards

  • Best Paper Award (Senior Award), IEEE Signal Processing Society (2013)
  • Elected Fellow of IEEE, IEEE Control Systems Society (2006)
  • IEEE Transactions on Control Systems Technology Outstanding Paper Award, IEEE Control Systems Society (2004)
  • Control Systems Technology Award, IEEE Control Systems Society (2002)
  • Alexander von Humboldt International Research Fellowship, Alexander von Humboldt Foundation, Bonn, Germany (1990)
  • Award for Young Scientist Achievements in Mathematics, Computing, Mechanics, and Control, The USSR Academy of Sciences, Moscow, USSR (1987)

Current Research and Scholarly Interests

My current interest is in analytics for the Industrial IoT (IIoT). Most of past application of Data Science and Machine Learning were to Internet of People data. New applications are to machine data, e.g., aircraft or electrical power system data. My earlier work was in decision & control and signal processing. Current work is in IIoT analytics that integrate Data Science and Machine Learning methods with systems controls and operations research methods.

Example Projects:

Condition and Failure Analytics work includes collaboration with Professor Stephen Boyd. A NASA project looked at optimization methods for machine learning that scale engineering models for airline big data collected for fleets of assets such as aircraft and jet engines. A related Air Force project builds fleet reliability models from maintenance data. Earlier work included condition monitoring and fault isolation for Air Force jet engines in collaboration with GE Aviation as well as NSF project on distributed sensor monitoring in collaboration with Honeywell.

Data-driven Risk Analytics work includes collaboration with Professor Steven Chu. Big data sets can include enough extreme events to afford detailed statistical modeling based on the extreme value theory (EVT). One application is to trending the risk of extreme weather events in the changing climate using high-resolution geo-spatial historical weather. As one example, the risk of 100-year extreme hot weather events in the continental US was found to increase over 200% in the last 4 decades. Another application is to data analytics for computing resource demand in the cloud in collaboration with Yahoo. The peak demand events can result in violations of service level agreement (SLA). Data-driven optimization allowed achieving 70% improvement in the data center performance.

Smart Grid Analytics project was a collaboration with Professor Sanjay Lall. It was sponsored by Precourt Institute for Energy, where I am an Affiliated Faculty Member, and analyzed electrical distribution systems. Other related work includes probabilistic forecasting of operational grid loads and analyzing transmission planning requirements for regional power systems operator.

All Publications