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.

  • Internships with Devesh

    • DA1103: Anomaly Detection in Time Series

      MERL is looking for a highly motivated intern to work on developing anomaly detection algorithms for time series analysis. Successful candidate will collaborate with MERL researchers to design, analyze, and implement new algorithms, conduct experiments, and prepare results for publication. The ideal candidate is expected to have a strong background in time series analytics with experience in algorithms for time-series representation, subsequence matching, pattern matching and time-series clustering. The candidate is expected to have strong programming skills in C++ and Python. Candidates who hold a PhD or in their senior years of a Ph.D. program in Electrical Engineering, Computer Science, Statistics or a related field are encouraged to apply. Interested candidates are encouraged to apply with their full CV with list of related publications and links to github code repositories (if any). [The duration of the internship is 12 weeks.] The position is available starting September 2017.

    • DA1104: 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, 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. [The duration of the internship is 12 weeks.] The position is available as early as September 2017. 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

    •  Jha, D.; Zhu, M.; Wang, Y.; Ray, A., "Data-Driven Anytime Algorithms for Motion Planning with Safety Guarantees", American Control Conference (ACC), DOI: 10.1109/ACC.2016.7526565, July 2016, pp. 5716-5721.
      BibTeX Download PDFRead TR2016-041
      • @inproceedings{Jha2016jul,
      • author = {Jha, D. and Zhu, M. and Wang, Y. and Ray, A.},
      • title = {Data-Driven Anytime Algorithms for Motion Planning with Safety Guarantees},
      • booktitle = {American Control Conference (ACC)},
      • year = 2016,
      • pages = {5716--5721},
      • month = jul,
      • doi = {10.1109/ACC.2016.7526565},
      • url = {http://www.merl.com/publications/TR2016-041}
      • }