RH

Ravi Sadananda Hegde

Also known as: Ravi Hegde, R S Hegde

Indian Institute of Technology, University of Michigan, University of Southern California

ORCID iD 0000-0002-0418-5861

Employment

  • Indian Institute of Technology associate Professor
    2015 - Present

Education

  • University of Michigan PhD
    2002 - 2008
  • University of Southern California MS
    2001 - 2002

Projects & Funding

Projects & funding information is unavailable.

Publications (53)

  • An active machine learning framework for automatic boxing punch recognition and classification using upper limb kinematics
    Plos one 2025
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  • Active learning for efficient nanophotonics inverse design in large and diverse design spaces
    Optics Express 2025
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  • Rapid Multi-Objective Antenna Synthesis Via Deep Neural Network Surrogate-Driven Evolutionary Optimization
    IEEE Journal on Multiscale and Multiphysics Computational Techniques 2025
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  • HyCMAx: Power-Efficient Hybrid CMOS-Memristor Based Approximate Dividers for Error-Resilient Applications
    2025 38th International Conference on VLSI Design and 2024 23rd International Conference on Embedded Systems (VLSID) 2025
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  • 3D Markerless Velocity Based Weight Training System for Athletes: Detection, Estimation and Validation
    2024 IEEE International Workshop on Sport, Technology and Research (STAR) 2024
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  • A Novel Hierarchical Pipeline for Fine-Grained Punch Recognition in Uncontrolled Setting
    International Conference on Computer Vision and Image Processing 2024
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  • AI-based, automated longitudinal performance monitoring of multiple boxers in large scale videos
    Fifth International Conference on Computer Vision and Computational Intelligence (CVCI 2024) 2024
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  • Continuously-tunable, compact, freespace notch-filter design using an all-dielectric metagrating capped with a low-loss phase change material
    Journal of Optics 2024
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  • Deep-learning empowered multi-objective antenna design: A polygon patch antenna case study
    2024 National Conference on Communications (NCC) 2024
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  • Deep-learning enabled photonic nanostructure discovery in arbitrarily large shape sets via linked latent space representation learning
    Digital Discovery 2024
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