Nandan Tumu

PhD Student @ UPenn


I am a 5th-year PhD student at the University of Pennsylvania, passionate about advancing the frontiers of machine learning and robotics. My research spans three key areas: uncertainty quantification using Conformal Prediction, physics-informed machine learning, and the application of these techniques to multi-agent robotics and game environments. By integrating these approaches, I aim to develop learning methodologies for robotics which are more trustworthy, and have lower sample complexity. I am fortunate to be supported by the NSF Graduate Research Fellowship.


Mar 2024 Our paper on designing practical non-conformity scores for planning and control was accepted to L4DC 2024!
Mar 2023 Our paper on Physics-constrained learning for trajectory prediction with Conformal Preduction was accepted to Intelligent Vehicles 2023!
Jan 2022 We won the Japan Automotive AI Challenge, against teams from Honda, Nissan, Toyota, and more!

latest posts

selected publications

  1. multi_modal_preview.png
    Multi-Modal Conformal Prediction Regions by Optimizing Convex Shape Templates
    Renukanandan Tumu, Matthew Cleaveland, Rahul Mangharam, and 2 more authors
    Dec 2023
  2. pcmp_preview.png
    Physics Constrained Motion Prediction with Uncertainty Quantification
    Renukanandan Tumu, Lars Lindemann, Truong Nghiem, and 1 more author
    In 2023 IEEE Intelligent Vehicles Symposium (IV) , Jun 2023