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Hrusikesha Pradhan

Photo of Hrusikesha Pradhan

Address:

4800 Oak Grove Drive, M/S 300-323

Pasadena, CA 91109

Curriculum Vitae:

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Website:

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Member of:

Sea Level And Ice

JPL Postdoctoral Fellow

Employed By

Caltech/JPL

Biography

Hi! I'm Hrusikesha Pradhan, a dedicated machine learning researcher, I recently completed my PhD journey at IIT Kanpur. During my doctoral studies, my research primarily focused on the development of efficient machine learning algorithms that prioritize resource utilization while achieving high learning accuracy. I concentrated on leveraging kernel learning methods to uncover unknown functions across diverse machine learning applications. My work centered on designing kernel-based learning algorithms that offer richer function approximation, supported by robust theoretical convergence guarantees. These algorithms, complemented by innovative subset selection strategies, were tailored to address challenges in streaming data and large-scale data environments. I am deeply passionate about leveraging machine learning algorithms to address various environmental challenges we face, aiming to contribute towards creating a sustainable and better world for all.

Education

  • PhD: Electrical Engineering, Indian Institute of Technology Kanpur (IIT Kanpur), India 2023
  • M.Tech: Electrical Engineering, Indian Institute of Technology Kanpur (IIT Kanpur), India 2014
  • B.Tech: Electronics and Telecommunication Engineering, Institute of Technical Education and Research, India, 2011

Professional Experience

  • JPL Postdoctoral Fellow, 2024-Present
  • Research Engineer, Centre for Development of Telematics (C-DOT)
  • Ministry of Telecom., Govt. of India 2014-2016

Community Service

Founding Chair, IEEE Signal Proc. Society Student Branch, IIT Kanpur

Organized a semester long IEEE Signal Processing Society Seminar Series on Optimization and Learning

Reviewer: AISTATS, IEEE Transactions on Signal Processing, Journal of Selected Topics in Signal Processing, IEEE Transactions on Wireless Communications, IEEE Transactions on Vehicular Technology, IEEE Communication Letters, and IEEE Signal Processing Letters

Research Interests

Function learning, Bayesian Learning, Kernel based methods, Distributed Learning, Optimization algorithms, Gaussian Processes

Selected Awards

  • Summer Journeyman Fellowship, US Army Research Laboratory, 2018
  • TCS PhD Research Fellowship for doctoral studies, 2018 – 2022
  • Finalist for Early Career Research Forum, IEEE ANTS 2021
  • MHRD Fellowship for doctoral studies 2016 – 2018
  • MHRD Fellowship for Master studies

Selected Publications

  1. H. Pradhan, K. Rajawat, A. Koppel, “Near-optimal Kernel approximation using Submodular Set Cover Theory” (submitted to IEEE Transactions on Signal Processing).
  2. A. Koppel, H. Pradhan, and K. Rajawat, “Consistent Online Gaussian Process Regression without the Sample Complexity Bottleneck,” Statistics and Computing ,Springer 31, 76, 2021.
  3. H. Pradhan, A. S. Bedi, A. Koppel, and K.Rajawat, “Adaptive Kernel Learning in Heterogeneous Networks,” IEEE Transactions on Signal and Information Processing over Networks, vol. 7, pp. 423-437, 2021.
  4. H. Pradhan, R. Budhiraja, and K. Rajawat, “Robust Transceiver Design for AF Asymmetric Two-Way MIMO Relaying,” IEEE Transactions on Signal Processing, vol. 68, no. 1, pp. 5488-5503, 2020.
  5. H. Pradhan, S. S. Kalamkar and A. Banerjee, “Sensing-Throughput Trade-off in Cognitive Radio With Random Arrivals and Departures of Multiple Primary Users”, IEEE Communication Letters, vol. 19, no. 3, pp. 415-418, 2015.
  6. H. Pradhan, K. Rajawat, “A Variance Reduced Nonconvex Stochastic Optimization framework for Online Kernel Learning,” Asilomar Conf. on Signals, Systems, and Computers, Pacific Grove, CA, USA, Nov. 2022.
  7. H. Pradhan, A. Koppel, K. Rajawat, “On Submodular Set Cover Problems for Near-Optimal Online Kernel Basis Selection,” ICASSP, Singapore, May 2022.
  8. H. Pradhan, A. S. Bedi, A. Koppel, K. Rajawat, “Conservative Multi-agent Online Kernel Learning in Heterogeneous Networks,” in Proc. of the Asilomar Conf. on Signals, Systems, and Computers, Pacific Grove, CA, USA, Nov. 2020.
  9. H. Pradhan, A. S. Bedi, A. Koppel, K. Rajawat, “Exact Nonparametric Decentralized Online Optimization,” in Proc. of the IEEE GlobalSIP, Anaheim, CA, USA, Nov. 2018
  10. A.S. Bedi, H. Pradhan, and K. Rajawat, “Decentralized Asynchronous Stochastic Gradient Descent: Convergence Rate Analysis,” in Proc. of the Intl. Conf. on Signal Processing and Communications (SPCOM), Bangalore, India. June 2018.