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PEOPLE:

 

Greg Burton

  • controls, robotics, planning
  • data visualization, CAD and solid modeling, GPU accelerated algorithms
  • was research engineer at Lockheed Martin Space Systems Company's Advanced Technology Center
  • M.S., Mechatronics and Control, U.C. Berkeley; B.S., Mechanical Engineering, U.C. San Diego

 

Lara Crawford
  • interface between planning and control, as well as the control and coordination of large-scale distributed, networked, embedded systems
  • simulation, robotics, learning control, biologically inspired control
  • Ph.D. in Biophysics & M.S. in Electrical Engineering, U.C. Berkeley; A.B. in Physics, Harvard

 

Tim Curley
Tim Curley
Director of Business Development

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  • focusing on drug delivery, contextual intelligence, and more
  • develops commercial partnerships and creates routes to revenue opportunities
  • developed partner and go-to-market strategies for startup and Fortune 1000 companies
  • MBA, Strategy, Pepperdine University

 

Dan Davies
  • software optimization in commercialization of research
  • novel technologies in context of traditional AI problems and data mining
  • 18 patents
  • Ph.D. and M.S. in Electrical Engineering from Stanford University; B.S. in Engineering from John Hopkins University

 

Christian Fritz
  • applies AI planning & KR to real-world problems
  • semantic workflows, cognitive robotics, ubicomp
  • specializes highly dynamic, open-world environments
  • Ph.D., University of Toronto

 

 

David Garcia
  • software developer on the Innovation, Design & Engineering Analytics (IDEA) team
  • current focus on data analytics and software solutions for healthcare
  • B.S., Mathematics, Santa Clara University; working towards M.S., Computer Science, Stanford University

 

 

 

John Hanley
  • modeling complex business processes for decision support system
  • relevance-based document retrieval from personal library
  • usability of large software systems
  • M.S., Software Engineering, Carnegie Mellon University

 

Tomonori Honda
  • broad experience in engineering design and big data modeling, specializing in formalizing design theory for complex systems
  • researches design for system reliability, prognosis, and maintenance scheduling; design synthesis for complex multidisciplinary systems; and behavioral design theory
  • Ph.D. and M.S. in Mechanical Engineering, California Institute of Technology; B.S. in Mechanical Engineering and Nuclear Engineering, University of California, Berkeley

 

Eric Huang
  • heuristic search, packing, constraint satisfaction, planning, scheduling
  • Ph.D., Computer Science, UCLA
  • former Micro Fellow and EGSA Angels Fellow

 

 

 

Matthew Klenk
  • qualitative reasoning, machine learning, planning, intelligent agents, spatial reasoning
  • worked in U.S. Naval Research Laboratory
  • Ph.D., Northwestern University EECS

 

Tolga Kurtoglu
  • design and development of complex systems
  • engineering design automation and optimization
  • prognostic and health management, model-based systems, automated reasoning, knowledge and information management, risk and reliability-based design
  • previously at NASA Ames Research Center and Dell Corporation
  • PhD, UT Austin, MS, Carnegie Mellon University, Mechanical Engineering

 

Linxia Liao
  • predictive and prescriptive analytics; system and components fault diagnostics and prognostics; signal processing; and machine learning algorithms
  • developing device fleet health management solutions for intelligent transportation systems
  • Ph.D., Industrial Engineering, University of Cincinnati; M.S. and B.S., Mechanical Science and Engineering, Huazhong University of Science and Technology (HUST) in China
  • one issued patent and seven pending patents

 

Julia Liu
  • signal processing, statistical modeling and inference, distributed sensor networks
  • applications such as intelligent transportation systems
  • Ph.D., M.S. Electrical Engineering from the University of Illinois at Urbana-Champaign

 

Saigopal Nelaturi

 

  • intelligent automation, digital manufacturing, geometric modeling, computational design, robotics, spatial computing
  • applications included reconfigurable manufacturing, spatial planning, non-destructive inspection
  • Ph.D., M.S., Mechanical Engineering, University of Wisconsin-Madison; M.Sc., Manufacturing, University of Bath, U.K.

 

 

 

Bob Price
  • inference, tracking, learning, and planning applications for government and industry clients
  • model-based control on a system for improving the diagnostic information generated from automatically constructed plans; machine learning of rules to diagnose problems in printing engines from fault code sequences; and optimization of power loads on aircraft to minimize power and cost and maximize utility
  • Ph.D. in Computer Science from the University of British Columbia

 

Ajay Raghavan
  • Manages the ACES area, focused on systems health and condition management technologies
  • Interests span sensing, modeling, diagnostics, and prognostics
  • PI on ARPA-E AMPED SENSOR project on fiber optic battery management systems
  • Ph.D., M.S., Aerospace Engineering, University of Michigan, Ann Arbor; B.S., Mechanical Engineering, IIT Bombay 

 

Bhaskar Saha

 

 

  • applies Bayesian inference techniques to classification, state estimation, and prediction problems
  • intelligent system design and health management
  • former scientist at NASA Ames Research Center's Prognostics Center of Excellence
  • formulated metrics for evaluating performance of prognostic algorithms
  • Ph.D., M.S., EECS, Georgia Institute of Technology; B.Tech, Indian Institute of Technology