Consulting Professor, Information Systems Laboratory
I advise graduate students, teach graduate courses, and lead projects.
My work is in Industrial AI applications for the Industrial Internet of Things (Industrial IoT).
The Industrial IoT is an on-going digital transformation that already touches much of the economy and is expected to transform most of the industries in the next decade or two. Most of end benefits will come from analytical applications using IIoT data.
A seminar class on Industrial IoT Application is taught in Spring 2019; here are links to Spring 2018, Spring 2017, 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 Industrial AI analytics applications for the Industrial IoT (IIoT). These require by Machine Learning using
data from Internet of Things vs. Internet of People.
Examples of IIoT 'things' are aircraft or electrical power systems. Current work extends methods from signal processing, decision & control, optimization, and operations research areas.
This is a fruitful area for research and valuable industrial
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 and Professor Stephen Boyd. The project sponsored by Stanford Bits and Watts Initiative develops tools for integrated resource planning 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 local and regional 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.