Sander Tonkens
Email: stonkens (at) ucsd.edu
Sander Tonkens is a Ph.D. student in Mechanical and Aerospace Engineering at UCSD. Prior to joining UCSD, Sander received an M.S. from Stanford University (doing research as an RA in ASL under Prof. Marco Pavone) and a B.Sc. from EPFL, in Lausanne, Switzerland (both in Mechanical Engineering). Sander is the recipient of the Netherlands-America Foundation Graduate Fellowship and is a Fulbright Graduate Scholar.
Sander's research interests lie at the intersection of control theory, machine learning, and applied robotics. In particular, his current research focuses on providing system-level safety assurances for learning-based systems that adapt readily to changes in the problem setup.
In his free time, Sander enjoys backpacking, backcountry skiing, climbing, surfing, tennis, and all other things outdoors.
Related Papers
Chen, Yuxiao, Sander Tonkens, and Marco Pavone. "Categorical Traffic Transformer: Interpretable and Diverse Behavior Prediction with Tokenized Latent." arXiv preprint arXiv:2311.18307 (2023).
Tonkens, Sander, et al. "Scalable safe long-horizon planning in dynamic environments leveraging conformal prediction and temporal correlations." Long-Term Human Motion Prediction Workshop, International Conference on Robotics and Automation. 2023.
Tonkens, Sander, et al. "Patching Neural Barrier Functions Using Hamilton-Jacobi Reachability." arXiv preprint arXiv:2304.09850 (2023).
Sander Tonkens and Sylvia Herbert. "Refining Control Barrier Functions through Hamilton-Jacobi Reachability." International Conference on Intelligent Robots and Systems (IROS), 2022. [link to code]
Tonkens, Sander, Paul De Klaver, and Mauro Salazar. "Optimizing vaccine allocation strategies in pandemic outbreaks: An optimal control approach." 2022 European Control Conference (ECC). IEEE, 2022.
Tonkens, Sander, Joseph Lorenzetti, and Marco Pavone. "Soft robot optimal control via reduced order finite element models." 2021 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2021.