Consulting Professor, Information Systems Laboratory
I advise graduate students, teach graduate courses, and lead projects.
My work is in analytics for the Industrial Internet of Things (Industrial IoT).
Leading industry players predict that the Industrial IoT will have 10-20
trillion dollar impact on the economy in the next decade. Many of the
benefits will come from analytical applications.
A seminar class on Industrial Internet of Things Application is taught in Spring 2018; here are links to Spring 2017 and Spring 2016 series. Seminar on Intelligent Energy Systems: Big Data was taught in 2015, 2014, 2013, 2012, and 2011. Past classes include Fault Diagnostics Systems in Spring 2009 as well as Control Engineering in Industry in Spring 2005 and in Winter 2003.
My current interest is in analytics for the Industrial IoT (IIoT). Data Science and Machine Learning application are moving on, from the Internet of People to the Internet of Things.
Examples of IIoT 'things' are aircraft or electrical power systems. My earlier work was in signal processing and decision & control.
Current work in IIoT analytics integrates data-driven Machine Learning approaches with systems controls, signal processing, and operations research methods.
This is a fruitful area for research and valuable industrial applications.
If you are a graduate student, who is interested in the Industrial Internet of Things application, please feel free to contact me. There might be an opportunity to work in this area.
My company Mitek Analytics LLC works with US Air Force, NASA, electrical utilities, airlines, Fortune 500 industrial companies, and large IT companies. Previously, I spent a decade with Honeywell working on aircraft and space systems, turbomachines (jet engines), and process control applications. Earlier work includes automotive and robotics (force control of robotics systems, legged locomotion) applications.
Planning Reliable Grid with Variable Generation and Storage is a collaboration with Professor Sanjay Lall, Professor Stephen Boyd, Professor Frank Wolak, Professor Steven Chu, and Dian Grueneich. The project sponsored by Stanford Bits and Watts Initiative develops tools for managing outage risks in the future low-carbon grid. The data-driven tools build probabilistic models of grid with renewables and storage and take into account extreme weather risk, reliability, and economics. The goal is to help utilities, RTOs, and NERC to support decision making on regional and national levels.
Condition and Failure Analytics work included collaboration with Professor Stephen Boyd. A NASA project looked at optimization methods for machine learning in application to big data from aircraft fleets. A related Air Force project builds fleet reliability models from maintenance data.
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). 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.