Picture
...

About Us

In our research group, we develop new data-driven learning and inference algorithms for autonomous perception and decision-making in complex cyber-physical systems with emphasis on health monitoring, decision frameworks, information fusion, resilient control, multi-agent systems, cyber-physical security and human-machine interaction. Our application areas of interest include energy (e.g., smart buildings, building-power grid interaction, power plants and wind turbines), manufacturing (e.g., design for manufacturability), agriculture (e.g., high-throughput phenotyping, multi-scale data assimilation), transportation (e.g., complex traffic networks, bridge health monitoring), aerospace (e.g., gas turbine engines) and industrial (e.g., gearbox, electric motors) systems. The nature of our research is inherently multi-disciplinary involving machine learning, dynamical systems, information theory, theory of computation, topology and statistical mechanics.
What's New

Our new USDA/NSF project on large-scale machine learning for early detection and mitigation of row crop diseases:
https://tinyurl.com/yc797g57

Research Areas

  • Learning And Inference In Cyber-Physical Systems (CPS)
  • Data Analytics and Machine Learning for Plant Science and Agricultural Applications​
  • Pattern Discovery In Large-Scale Simulation Data
  • Decision Support Systems And Human-Machine Interaction (HMI)
  • Machine Learning driven Design for Manufacturability
  • Complex Transportation Networks
  • Monitoring And Supervisory Control Of Integrated Buildings
  • Collaborative Decision-Making In Multi-Agent Systems (MAS)

Opportunities

Check back soon.

Workshops

Upcoming Workshop:

2nd ACM SIGKDD Workshop on Machine Learning for Prognostics and Health Management (ML for PHM 2017):
​
​https://sites.google.com/site/mlforphm2017/

Sponsors

Regents Innovation Funds