Kundan Kumar
Ahmedabad University, Aalto University, Indian Institute of Technology Patna Department of Electrical Engineering, Muzaffarpur Institute of Technology, VIT Bhopal University
0000-0002-2977-8815
About
Kundan Kumar is currently serving as an Assistant Professor at the School of Engineering and Applied Science, Ahmedabad University. Before joining Ahmedabad University, he worked as a Postdoctoral Researcher at the Department of Electrical Engineering and Automation, Aalto University, Finland, with Prof. Simo Särkkä. He has completed his PhD in Electrical Engineering from the Indian Institute of Technology Patna, under the supervision of Dr Shovan Bhaumik.
His research lies at the intersection of statistical signal processing, stochastic estimation, machine learning, and control systems, with a strong focus on developing model-based and data-driven algorithms for complex dynamical systems. He is particularly interested in advancing estimation and learning methods for applications such as battery management systems, electric vehicles, target tracking, and networked control systems.
-Skilled in:
Statistical signal processing
Stochastic estimation & modeling
Gaussian & Gaussian-Approximated filtering
Machine learning, deep learning, and neural networks
Physics-informed machine learning, deep learning, and neural network
Distributed filtering
Target tracking
Interacting multiple model filtering
Battery management system (BMS) algorithm
-Programming languages for implementing algorithms: MATLAB, Python, and C.
- Machine learning skill (Pytorch, pandas, NumPy)
Employment
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Ahmedabad University Assistant Professor2026 - Present
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Aalto University Postdoctoral Researcher2023 - 2026
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Aalto University Visiting Doctoral Researcher2022 - 2023
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VIT Bhopal University Teaching Fellow2022 - 2022
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Aalto University
Education
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Indian Institute of Technology Patna Department of Electrical Engineering PHD2016 - 2022
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Muzaffarpur Institute of Technology Bachelor of Technology2012 - 2016
Projects & Funding
Projects & funding information is unavailable.
Publications (17)
- Event-Triggered Particle Filter with One-Step Randomly Delayed Measurement Save
- Statistical Linear Regression Approach to Kalman Filtering and Smoothing under Cyber-Attacks Save
- Communication-Efficient Distributed Kalman Filtering Using ADMM Save
- Passive underwater tracking with unknown measurement noise statistics using variational Bayesian approximation Save
- Polynomial Chaos Expansion Based Rauch-Tung-Striebel Smoothers Save
- Risk-Sensitive Filtering under False Data Injection Attacks Save
- Polynomial chaos Kalman filter for target tracking applications Save
- Bearings-Only Tracking With Speed and Range Constraints Save
- Improved Gaussian Approximated Filters for Nonlinear State Estimation Save
- Bearing-only tracking using range-parameterized shifted Rayleigh filter Save