People - Devesh Jha | Mitsubishi Electric Research Laboratories

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 Scientists Presenting 5 Papers including 2 Invited Talks at European Conference on Optical Communication (ECOC) 2019
  Date: September 22, 2019 - September 26, 2019
  MERL Contacts: Devesh Jha; Toshiaki Koike-Akino; Keisuke Kojima; David Millar; Kieran Parsons; Ye Wang
  Research Areas: Artificial Intelligence, Communications, Electronic and Photonic Devices, Optimization, Signal Processing
  Brief
  - MERL Optical Team scientists will be presenting 5 papers including 2 invited talks at the 45th European Conference on Optical Communication (ECOC) 2019, which is being held in Dublin from September 22-26, 2019. Topics to be presented include recent advances in sophisticated constellation shaping schemes, lattice coding, and deep learning-based turbo equalization to mitigate fiber nonlinearity. Dr. Kojima is giving an invited workshop talk on deep learning-based nano-photonic device optimization. Dr. Tobias Fehenberger, a former Visiting Scientist is giving an invited talk related to our joint paper "Mapping Strategies for Short-Length Probabilistic Shaping"
    
    ECOC is the largest optical communications event in Europe and a key meeting place for more than 1,500 scientists and researchers from institutions and companies across the world. The conference features more than 400 oral and poster presentations from various major telecoms industries and universities. As well as being one of the largest scientific conferences globally, ECOC also features Europe’s largest optical communications exhibition.
- NEWS MERL researchers presented 8 papers at American Control Conference
  Date: July 10, 2019 - July 12, 2019
  Where: Philadelphia
  MERL Contacts: Mouhacine Benosman; Karl Berntorp; Ankush Chakrabarty; Claus Danielson; Stefano Di Cairano; Devesh Jha; Rien Quirynen; Yebin Wang; Avishai Weiss
  Research Areas: Control, Machine Learning, Optimization
  Brief
  - At the American Control Conference, MERL presented 8 papers on subjects including model predictive control applications, estimation and motion planning for vehicles, modular control architectures, and adaptation and learning.
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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
- Jha, D., Kolaric, P., Romeres, D., Raghunathan, A., Benosman, M., Nikovski, D.N., "Robust Optimization for Trajectory-Centric Model-based Reinforcement Learning", NeurIPS Workshop on Safety and Robustness in Decision Making, December 2019.
  BibTeX Download PDFAbout TR2019-156
  - @inproceedings{Jha2019dec2,
  - author = {Jha, Devesh and Kolaric, Patrik and Romeres, Diego and Raghunathan, Arvind and Benosman, Mouhacine and Nikovski, Daniel N.},
  - title = {Robust Optimization for Trajectory-Centric Model-based Reinforcement Learning},
  - booktitle = {NeurIPS Workshop on Safety and Robustness in Decision Making},
  - year = 2019,
  - month = dec,
  - url = {https://www.merl.com/publications/TR2019-156}
  - }
- Jha, D., Raghunathan, A., Romeres, D., "QNTRPO: Including Curvature in TRPO", Optimization Foundations for Reinforcement Learning Workshop at NeurIPS, December 2019.
  BibTeX Download PDFAbout TR2019-154
  - @inproceedings{Jha2019dec,
  - author = {Jha, Devesh and Raghunathan, Arvind and Romeres, Diego},
  - title = {QNTRPO: Including Curvature in TRPO},
  - booktitle = {Optimization Foundations for Reinforcement Learning Workshop at NeurIPS},
  - year = 2019,
  - month = dec,
  - url = {https://www.merl.com/publications/TR2019-154}
  - }
- Ota, K., Jha, D.K., Oiki, T., Miura, M., Nammoto, T., Nikovski, D., Mariyama, T., "Trajectory Optimization for Unknown Constrained Systems using Reinforcement Learning", IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), DOI: 10.1109/IROS40897.2019.8968010, ISSN: 2153-0866, ISBN: 978-1-7281-4004-9, November 2019, pp. 3487-3494.
  BibTeX Download PDFAbout TR2019-129
  - @inproceedings{Ota2019nov,
  - author = {Ota, Kei and Jha, Devesh K. and Oiki, Tomohiro and Miura, Mamoru and Nammoto, Takashi and Nikovski, Daniel and Mariyama, Toshisada},
  - title = {Trajectory Optimization for Unknown Constrained Systems using Reinforcement Learning},
  - booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  - year = 2019,
  - pages = {3487--3494},
  - month = nov,
  - publisher = {IEEE},
  - doi = {10.1109/IROS40897.2019.8968010},
  - issn = {2153-0866},
  - isbn = {978-1-7281-4004-9},
  - url = {https://www.merl.com/publications/TR2019-129}
  - }
- Jha, D., Raghunathan, A., Romeres, D., "Quasi-Newton Trust Region Policy Optimization", Conference on Robot Learning (CoRL), October 2019.
  BibTeX Download PDFAbout TR2019-120
  - @inproceedings{Jha2019oct,
  - author = {Jha, Devesh and Raghunathan, Arvind and Romeres, Diego},
  - title = {Quasi-Newton Trust Region Policy Optimization},
  - booktitle = {Conference on Robot Learning (CoRL)},
  - year = 2019,
  - month = oct,
  - url = {https://www.merl.com/publications/TR2019-120}
  - }
- Zhang, W., Jha, D., Laftchiev, E., Nikovski, D.N., "Multi-label Prediction in Time Series Data using Deep Neural Networks", International Journal for Prognostics and Health Management Special Issue on Applications of Deep Learning and Emerging Analytics, ISSN: 2153-2648, Vol. 10, pp. 0-12, September 2019.
  BibTeX Download PDFAbout TR2019-110
  - @article{Zhang2019sep2,
  - author = {Zhang, Wenyu and Jha, Devesh and Laftchiev, Emil and Nikovski, Daniel N.},
  - title = {Multi-label Prediction in Time Series Data using Deep Neural Networks},
  - journal = {International Journal for Prognostics and Health Management Special Issue on Applications of Deep Learning and Emerging Analytics},
  - year = 2019,
  - volume = 10,
  - pages = {0--12},
  - month = sep,
  - note = {Special Issue on Deep Learning and Emerging Analytics},
  - issn = {2153-2648},
  - url = {https://www.merl.com/publications/TR2019-110}
  - }
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Free Downloads
- Gradient-based Nikaido-Isoda
- Quasi-Newton Trust Region Policy Optimization
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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Devesh Jha

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Biography

Recent News & Events

Awards

MERL Publications

Free Downloads

Gradient-based Nikaido-Isoda

Quasi-Newton Trust Region Policy Optimization

MERL Issued Patents