Consulting Professor, Information Systems Laboratory
I advise graduate students, teach graduate courses, and lead projects.
I coordinate Stanford Industrial AI Initiative, which is a part of
Stanford SystemX Alliance.
Industrial AI provides advanced analytical applications for the Industrial Internet of Things (Industrial IoT).
The digital transformation driven by Industrial IoT touches much of the economy. Most of its benefits come from applications using IIoT data.
A seminar class on Industrial AI is taught in Spring 2021. Its predcessor, a seminar on Industrial IoT Applications, was taught in Spring 2019, 2018, 2017, and 2016. 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 are in Industrial AI applications for the Industrial IoT (IIoT). Examples include aerospace and electrical power systems, as well as supply chain processes. Current work extends methods from machine learning, signal processing, decision & control, optimization, and operations research.
This is a fruitful area for research and valuable industrial
If you are a graduate student, who is interested in the Industrial AI, 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.