During my Ph.D., I have interned at NVIDIA in the Autonomous Vehicle Research Group,
working on endowing learned prediction and planning autonomy stacks with interpretable latent representations for explainability and LLM-based steering.
I am currently doing a research stay at Stanford University, where I am working
on automated interpretability of failures in robot foundation models for red-teaming.
My research interests lie at the intersection of robotics, control theory, and machine learning. Specifically, I focus on
(i) providing probabilistic safety assurances for autonomous systems in unknown, dynamic environments via uncertainty quantification,
(ii) infusing interpretability into learned policies for transparent decision-making, and
(iii) developing safety filters for OOD runtime monitoring and failure prevention.
Scalable Safe Long-Horizon Planning in Dynamic Environments Leveraging Conformal Prediction and Temporal Correlations
Sander Tonkens*, Sophia Sun*, Rose Yu, Sylvia Herbert Long Term Human Motion Prediction workshop at IEEE International Conference on Robotics and Automation, 2023.
AA203: Optimal and Learning-based Control Stanford University
Teaching Assistant (Spring 2020), under Prof. Marco Pavone
Course website
Analysis II, III & IV, Dynamics I, Physics I École Polytechnique Fédérale de Lausanne
Teaching Assistant (2015 — 2017) for various undergraduate courses
EPFL ME B.Sc.
Syllabus
Education
Doctor of Philosophy (Ph.D.) in Mechanical and Aerospace Engineering University of California, San Diego
September 2021 — April 2026 (expected)
Master of Science (M.S.) in Mechanical Engineering Stanford University
September 2018 — December 2020
Bachelor of Science (B.Sc.) in Mechanical Engineering École Polytechnique Fédérale de Lausanne
September 2014 — July 2017
Contact
Feel free to contact me regarding my research—I am always happy to brainstorm and share my ideas. I can be contacted directly at sandertonkens [at] gmail [dot] com.
You can also schedule a meeting directly through my calendar.