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Devesh Jha

Devesh Jha
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  • 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   MERL researchers presenting three papers at ICML 2020
      Date: July 12, 2020 - July 18, 2020
      Where: Vienna, Austria (virtual this year)
      MERL Contacts: Mouhacine Benosman; Anoop Cherian; Devesh Jha; Daniel Nikovski
      Research Areas: Artificial Intelligence, Computer Vision, Data Analytics, Dynamical Systems, Machine Learning, Optimization, Robotics
      Brief
      • MERL researchers are presenting three papers at the International Conference on Machine Learning (ICML 2020), which is virtually held this year from 12-18th July. ICML is one of the top-tier conferences in machine learning with an acceptance rate of 22%. The MERL papers are:

        1) "Finite-time convergence in Continuous-Time Optimization" by Orlando Romero and Mouhacine Benosman.

        2) "Can Increasing Input Dimensionality Improve Deep Reinforcement Learning?" by Kei Ota, Tomoaki Oiki, Devesh Jha, Toshisada Mariyama, and Daniel Nikovski.

        3) "Representation Learning Using Adversarially-Contrastive Optimal Transport" by Anoop Cherian and Shuchin Aeron.
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    •  NEWS   MERL Scientists Presented 5 Papers Including 2 Invited Talks at Optical Fiber Communications Conference (OFC) 2020
      Date: March 8, 2020 - March 13, 2020
      MERL Contacts: Devesh Jha; Toshiaki Koike-Akino; Keisuke Kojima; David Millar; Kieran Parsons; Ye Wang
      Research Areas: Communications, Electronic and Photonic Devices, Machine Learning, Signal Processing
      Brief
      • Due to COVID-19, MERL Optical Team scientists remotely presented 5 papers including 2 invited talks at the Optical Fiber Communications Conference (OFC) 2020, that was held in San Diego from March 8-13, 2020. Topics presented include recent advances in quantum signal processing, channel coding design, nano-optic power splitter, and deep learning-based integrated photonics. In addition, Dr. Kojima gave an invited workshop talk on deep learning-based nano-photonic device optimization.

        OFC is the largest global conference and exhibition for optical communications and networking professionals. The program is comprehensive from research to marketplace, from components to systems and networks and from technical sessions to the exhibition. For over 40 years, OFC has drawn attendees from all corners of the globe to meet and greet, teach and learn, make connections and move the industry forward. The five-day technical conference features peer reviewed presentations and more than 180 invited speakers, the thought leaders in the industry presenting the highlights of emerging technologies. Additional technical programming throughout the week includes special symposia, special sessions, in-depth tutorials, workshops, panels and the thought-provoking rump session.
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  • 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.
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  • MERL Publications

    •  Chakrabarty, A., Jha, D., Buzzard, G.T., Wang, Y., Vamvoudakis, K., "Safe Approximate Dynamic Programming via Kernelized Lipschitz Estimation", IEEE Transactions on Neural Networks and Learning Systems, July 2020.
      BibTeX TR2020-108 PDF
      • @article{Chakrabarty2020jul2,
      • author = {Chakrabarty, Ankush and Jha, Devesh and Buzzard, Gregery T. and Wang, Yebin and Vamvoudakis, Kyriakos},
      • title = {Safe Approximate Dynamic Programming via Kernelized Lipschitz Estimation},
      • journal = {IEEE Transactions on Neural Networks and Learning Systems},
      • year = 2020,
      • month = jul,
      • url = {https://www.merl.com/publications/TR2020-108}
      • }
    •  Romeres, D., Liu, Y., Jha, D., Nikovski, D.N., "Understanding Multi-Modal Perception Using Behavioral Cloning for Peg-In-a-Hole Insertion Tasks", Robotics: Science and Systems, July 2020.
      BibTeX TR2020-110 PDF
      • @inproceedings{Romeres2020jul,
      • author = {Romeres, Diego and Liu, Yifang and Jha, Devesh and Nikovski, Daniel N.},
      • title = {Understanding Multi-Modal Perception Using Behavioral Cloning for Peg-In-a-Hole Insertion Tasks},
      • booktitle = {Robotics: Science and Systems},
      • year = 2020,
      • month = jul,
      • url = {https://www.merl.com/publications/TR2020-110}
      • }
    •  Jha, D., Wang, Y., Zhu, M., "Sampling-based Algorithms for Feedback Motion Planning", arXiv, June 2020.
      BibTeX
      • @article{Jha2020jun,
      • author = {Jha, Devesh and Wang, Yebin and Zhu, Minghui},
      • title = {Sampling-based Algorithms for Feedback Motion Planning},
      • journal = {arXiv},
      • year = 2020,
      • month = jun
      • }
    •  Ota, K., Oiki, T., Jha, D., Mariyama, T., Nikovski, D.N., "Can Increasing Input Dimensionality Improve Deep Reinforcement Learning?", International Conference on Machine Learning (ICML), June 2020.
      BibTeX TR2020-083 PDF Software
      • @inproceedings{Ota2020jun,
      • author = {Ota, Kei and Oiki, Tomoaki and Jha, Devesh and Mariyama, Toshisada and Nikovski, Daniel N.},
      • title = {Can Increasing Input Dimensionality Improve Deep Reinforcement Learning?},
      • booktitle = {International Conference on Machine Learning (ICML)},
      • year = 2020,
      • month = jun,
      • url = {https://www.merl.com/publications/TR2020-083}
      • }
    •  Romeres, D., Dalla Libera, A., Jha, D., Yerazunis, W.S., Nikovski, D.N., "Model-Based Reinforcement Learning for Physical Systems Without Velocity and Acceleration Measurements", Robotics and Automation Letters, DOI: 10.1109/LRA.2020.2977255, Vol. 5, No. 2, pp. 3548-3555, May 2020.
      BibTeX TR2020-063 PDF
      • @article{Romeres2020may,
      • author = {Romeres, Diego and Dalla Libera, Alberto and Jha, Devesh and Yerazunis, William S. and Nikovski, Daniel N.},
      • title = {Model-Based Reinforcement Learning for Physical Systems Without Velocity and Acceleration Measurements},
      • journal = {Robotics and Automation Letters},
      • year = 2020,
      • volume = 5,
      • number = 2,
      • pages = {3548--3555},
      • month = may,
      • doi = {10.1109/LRA.2020.2977255},
      • issn = {2377-3766},
      • url = {https://www.merl.com/publications/TR2020-063}
      • }
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  • Software Downloads

  • 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
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