Devesh Jha

Devesh Jha
  • Position:
    Research / Technical Staff

    Research Scientist
  • Education:
    Ph.D., Pennsylvania State University, 2016
  • Research Area:
  • External Links:
  • Biography

    Devesh's PhD Thesis was on decision & control of autonomous systems. He also got a Master's degree in Mathematics from Penn State. His research interests are in the areas of Machine Learning, Time Series Analytics and Robotics. He was a recipient of the best student paper award at the 1st ACM SIGKDD workshop on Machine Learning for Prognostics and Health Management at KDD 2016, San Francisco.

  • News & Events

    •  NEWS   MERL Researchers Demonstrate Robot Learning Technology at CEATEC'18
      Date: October 15, 2018 - October 19, 2018
      Where: CEATEC'18, Makuhari Messe, Tokyo
      MERL Contacts: Devesh Jha; Daniel Nikovski; Diego Romeres; Alan Sullivan; Jeroen van Baar; William Yerazunis
      Research Areas: Artificial Intelligence, Computer Vision, Data Analytics, Robotics
      Brief
      • MERL's work on robot learning algorithms was demonstrated at CEATEC'18, Japan's largest IT and electronics exhibition and conference held annually at Makuhari Messe near Tokyo. A team of researchers from the Data Analytics Group at MERL and the Artificial Intelligence Department of the Information Technology Center (ITC) of MELCO presented an interactive demonstration of a model-based artificial intelligence algorithm that learns how to control equipment autonomously. The algorithm developed at MERL constructs models of mechanical equipment through repeated trial and error, and then learns control policies based on these models. The demonstration used a circular maze, where the objective is to drive a ball to the center of the maze by tipping and tilting the maze, a task that is difficult even for humans; approximately half of the CEATEC'18 visitors who tried to steer the ball by means of a joystick could not bring it to the center of the maze within one minute. In contrast, MERL's algorithm successfully learned how to drive the ball to the goal within ten seconds without the need for human programming. The demo was at the entrance of MELCO's booth at CEATEC'18, inviting visitors to learn more about MELCO's many other AI technologies on display, and was seen by an estimated more than 50,000 visitors over the five days of the expo.
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    •  NEWS   MERL Researchers Demonstrate New Model-Based AI Learning Technology for Equipment Control
      Date: February 14, 2018
      Where: Tokyo, Japan
      MERL Contacts: Devesh Jha; Daniel Nikovski; Diego Romeres; William Yerazunis; Jeroen van Baar; Alan Sullivan
      Research Areas: Optimization, Computer Vision, Artificial Intelligence, Data Analytics, Robotics
      Brief
      • New technology for model-based AI learning for equipment control was demonstrated by MERL researchers at a recent press release event in Tokyo. The AI learning method constructs predictive models of the equipment through repeated trial and error, and then learns control rules based on these models. The new technology is expected to significantly reduce the cost and time needed to develop control programs in the future. Please see the link below for the full text of the Mitsubishi Electric press release.
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  • Internships with Devesh

    • DA1289: Robot Learning

      MERL is looking for a highly motivated intern to work on developing algorithms for robot learning. Successful candidate will collaborate with MERL researchers to design, analyze, and implement new algorithms, conduct experiments, and prepare results for publication. The candidate should have a strong background in reinforcement learning, Imitation Learning (or Learning from Demonstrations, LfD), machine learning and robotics. Prior experience of working with robotic systems is required. The candidate should be comfortable implementing the developed algorithms in Python and should have prior experience working with ROS. Prior exposure to deep learning and hands-on experience with packages such as Keras, TensorFlow, or Theano is a plus. The candidate is expected to be a PhD student in Computer Science, Electrical Engineering, Operations Research, Statistics, Applied Mathematics, or a related field, with relevant publication record. Expected duration of the internship is at least 3 months. Interested candidates are encouraged to apply with their recent CV with list of related publications and links to GitHub repositories (if any).

    See All Internships at MERL
  • MERL Publications

    See All Publications for Devesh
  • MERL Issued Patents

    • Title: "Vehicle Automated Parking System and Method"
      Inventors: Wang, Yebin; Jha, Devesh
      Patent No.: 9,969,386
      Issue Date: May 15, 2018
    See All Patents for MERL