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.

  • Recent News & Events

    •  NEWS   Invited talk at University of Leeds
      Date: April 7, 2021
      Where: Online
      MERL Contact: Devesh Jha
      Research Areas: Artificial Intelligence, Machine Learning, Robotics
      Brief
      • Devesh Jha, a Principal Research Scientist in MERL's Data Analytics group, gave an invited talk at the robotics seminar series at the University of Leeds. The talk presented some of the recent work done at MERL in the areas of robotic manipulation and robot learning.
    •  
    •  NEWS   New robotics benchmark system
      Date: November 16, 2020
      MERL Contacts: Devesh Jha; Daniel Nikovski; Diego Romeres; Alan Sullivan; Jeroen van Baar
      Research Areas: Artificial Intelligence, Machine Learning, Robotics
      Brief
      • MERL researchers, in collaboration with researchers from MELCO and the Department of Brain and Cognitive Science at MIT, have released simulation software Circular Maze Environment (CME). This system could be used as a new benchmark for evaluating different control and robot learning algorithms. The control objective in this system is to tip and the tilt the maze so as to drive one (or multiple) marble(s) to the innermost ring of the circular maze. Although the system is very intuitive for humans to control, it is very challenging for artificial intelligence agents to learn efficiently. It poses several challenges for both model-based as well as model-free methods, due to its non-smooth dynamics, long planning horizon, and non-linear dynamics. The released Python package provides the simulation environment for the circular maze, where movement of multiple marbles could be simulated simultaneously. The package also provides a trajectory optimization algorithm to design a model-based controller in simulation.
    •  

    See All News & Events for Devesh
  • Awards

    •  AWARD   MERL Researcher Devesh Jha Wins the Rudolf Kalman Best Paper Award 2019
      Date: October 10, 2019
      Awarded to: Devesh Jha, Nurali Virani, Zhenyuan Yuan, Ishana Shekhawat and Asok Ray
      MERL Contact: Devesh Jha
      Research Areas: Artificial Intelligence, Control, Data Analytics, Machine Learning, Robotics
      Brief
      • MERL researcher Devesh Jha has won the Rudolf Kalman Best Paper Award 2019 for the paper entitled "Imitation of Demonstrations Using Bayesian Filtering With Nonparametric Data-Driven Models". This paper, published in a Special Commemorative Issue for Rudolf E. Kalman in the ASME JDSMC in March 2018, uses Bayesian filtering for imitation learning in Hidden Mode Hybrid Systems. This award is given annually by the Dynamic Systems and Control Division of ASME to the authors of the best paper published in the ASME Journal of Dynamic Systems Measurement and Control during the preceding year.
    •  
    See All Awards for MERL
  • MERL Publications

    •  Kojima, K., Tang, Y., Koike-Akino, T., Wang, Y., Jha, D., TaherSima, M., Parsons, K., "Application of Deep Learning for Nanophotonic Device Design", SPIE Photonics West, Bahram Jalali and Ken-ichi Kitayama, Eds., DOI: 10.1117/​12.2579104, March 2021.
      BibTeX TR2020-182 PDF Video
      • @inproceedings{Kojima2021mar,
      • author = {Kojima, Keisuke and Tang, Yingheng and Koike-Akino, Toshiaki and Wang, Ye and Jha, Devesh and TaherSima, Mohammad and Parsons, Kieran},
      • title = {Application of Deep Learning for Nanophotonic Device Design},
      • booktitle = {SPIE Photonics West},
      • year = 2021,
      • editor = {Bahram Jalali and Ken-ichi Kitayama},
      • month = mar,
      • publisher = {SPIE},
      • doi = {10.1117/12.2579104},
      • url = {https://www.merl.com/publications/TR2020-182}
      • }
    •  Kojima, K., TaherSima, M., Koike-Akino, T., Jha, D., Tang, Y., Wang, Y., Parsons, K., "Deep Neural Networks for Inverse Design of Nanophotonic Devices", IEEE Journal of Lightwave Technology, DOI: 10.1109/​JLT.2021.3050083, January 2021.
      BibTeX TR2021-001 PDF
      • @article{Kojima2021jan,
      • author = {Kojima, Keisuke and TaherSima, Mohammad and Koike-Akino, Toshiaki and Jha, Devesh and Tang, Yingheng and Wang, Ye and Parsons, Kieran},
      • title = {Deep Neural Networks for Inverse Design of Nanophotonic Devices},
      • journal = {IEEE Journal of Lightwave Technology},
      • year = 2021,
      • month = jan,
      • doi = {10.1109/JLT.2021.3050083},
      • issn = {1558-2213},
      • url = {https://www.merl.com/publications/TR2021-001}
      • }
    •  Jha, D., Wang, Y., Zhu, M., "Sampling-based Algorithms for Feedback Motion Planning", IEEE/CAA Journal of Automatica Sinica, December 2020.
      BibTeX
      • @article{Jha2020dec,
      • author = {Jha, Devesh and Wang, Yebin and Zhu, Minghui},
      • title = {Sampling-based Algorithms for Feedback Motion Planning},
      • journal = {IEEE/CAA Journal of Automatica Sinica},
      • year = 2020,
      • month = dec
      • }
    •  Ota, K., Jha, D., Romeres, D., van Baar, J., Smith, K., Semistsu, T., Oiki, T., Sullivan, A., Nikovski, D.N., Tenanbaum, J., "Towards Human-Level Learning of Complex Physical Puzzles", arXiv, December 2020.
      BibTeX
      • @article{Ota2020dec,
      • author = {Ota, Kei and Jha, Devesh and Romeres, Diego and van Baar, Jeroen and Smith, Kevin and Semistsu, Takayuki and Oiki, Tomoaki and Sullivan, Alan and Nikovski, Daniel N. and Tenanbaum, Joshua},
      • title = {Towards Human-Level Learning of Complex Physical Puzzles},
      • journal = {arXiv},
      • year = 2020,
      • month = dec
      • }
    •  Ota, K., Jha, D., Onishi, T., Kanezaki, A., Yoshiyasu, Y., Mariyama, T., Nikovski, D.N., "Deep Reactive Planning in Dynamic Environments", Conference on Robot Learning (CoRL), November 2020.
      BibTeX TR2020-144 PDF
      • @inproceedings{Ota2020nov2,
      • author = {Ota, Kei and Jha, Devesh and Onishi, Tadashi and Kanezaki, Asako and Yoshiyasu, Yusuke and Mariyama, Toshisada and Nikovski, Daniel N.},
      • title = {Deep Reactive Planning in Dynamic Environments},
      • booktitle = {Conference on Robot Learning (CoRL)},
      • year = 2020,
      • month = nov,
      • url = {https://www.merl.com/publications/TR2020-144}
      • }
    See All Publications for Devesh
  • Software Downloads

  • Videos

  • MERL Issued Patents

    • Title: "System and Method for Control Constrained Operation of Machine with Partially Unmodeled Dynamics"
      Inventors: Chakrabarty, Ankush; Jha, Devesh; Wang, Yebin
      Patent No.: 10,895,854
      Issue Date: Jan 19, 2021
    • Title: "Compact Photonic Devices"
      Inventors: Kojima, Keisuke; Tahersima, Mohammad; Koike-Akino, Toshiaki; Jha, Devesh; Wang, Bingnan; Lin, Chungwei; Parsons, Kieran
      Patent No.: 10,859,769
      Issue Date: Dec 8, 2020
    • 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