Devesh Jha

Devesh Jha
  • 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.
    •  
    •  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.
    •  
  • MERL Publications

    •  TaherSima, M., Kojima, K., Koike-Akino, T., Jha, D., Wang, B., Lin, C., Parsons, K., "Deep Neural Network Inverse Modeling for Integrated Photonics", Tech. Rep. TR2018-183, Mitsubishi Electric Research Laboratories, Cambridge, MA, December 2018.
      BibTeX Download PDFAbout TR2018-183
      • @techreport{MERL_TR2018-183,
      • author = {TaherSima, M. and Kojima, K. and Koike-Akino, T. and Jha, D. and Wang, B. and Lin, C. and Parsons, K.},
      • title = {Deep Neural Network Inverse Modeling for Integrated Photonics},
      • institution = {MERL - Mitsubishi Electric Research Laboratories},
      • address = {Cambridge, MA 02139},
      • number = {TR2018-183},
      • month = dec,
      • year = 2018,
      • url = {http://www.merl.com/publications/TR2018-183/}
      • }
    •  TaherSima, M., Kojima, K., Koike-Akino, T., Jha, D., Wang, B., Lin, C., Parsons, K., "Deep Neural Network Inverse Design of Integrated Photonic Power Splitters", Tech. Rep. TR2018-180, Mitsubishi Electric Research Laboratories, Cambridge, MA, December 2018.
      BibTeX Download PDFAbout TR2018-180
      • @techreport{MERL_TR2018-180,
      • author = {TaherSima, M. and Kojima, K. and Koike-Akino, T. and Jha, D. and Wang, B. and Lin, C. and Parsons, K.},
      • title = {Deep Neural Network Inverse Design of Integrated Photonic Power Splitters},
      • institution = {MERL - Mitsubishi Electric Research Laboratories},
      • address = {Cambridge, MA 02139},
      • number = {TR2018-180},
      • month = dec,
      • year = 2018,
      • url = {http://www.merl.com/publications/TR2018-180/}
      • }
    •  Jha, D., Romeres, D., van Baar, J., Sullivan, A., Nikovski, D.N., "Learning Tasks in a Complex Circular Maze Environment", Modeling the Physical World: Perception, Learning, and Control, NIPS Workshop, December 2018.
    •  Romeres, D.., Jha, D., Dalla Libera, A., Chiuso, A., Nikovski, D.N., "Derivative-Free Semiparametric Bayesian Models for Robot Learning", Advances in Neural Information Processing Systems (NIPS), December 2018.
    •  Jha, D., "Algorithms for Task Allocation in Homogeneous Swarm of Robots", IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), October 2018.
    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