Boris Ivanovic
Alumni · now at NVIDIA Research
Boris Ivanovic is a Ph.D. student in the Aeronautics and Astronautics Department. He obtained a Master's in Computer Science from Stanford in 2018, specializing in Artificial Intelligence (AI). Prior to joining Stanford, he obtained a Bachelor’s of Applied Science with high honors in 2016 from the University of Toronto's rigorous Engineering Science program, majoring in Electrical and Computer Engineering with a Robotics/Mechatronics minor.
His previous research work spans computer vision, machine learning, data science, and robotics. He has also conducted numerous internships, from Amazon’s Prime Air drone delivery team to a summer at ETH Zurich in Professor Raffaello d’Andrea’s Flying Machine Arena.
Boris’ research interests are rooted in trajectory forecasting for autonomous vehicles, combining deep learning with principled methods from robotics, optimization, and control theory to create performant, safe, and reliable learning-based algorithms that can be deployed in the real world.
In his spare time, Boris enjoys playing tennis, skiing, hiking, traveling, watching movies, and cooking.
Awards:
- Natural Sciences and Engineering Research Council of Canada (NSERC) Doctoral Scholarship
Publications
16 publications · full lab bibliography
- B. Ivanovic, K. H. Lee, P. Tokmakov, B. Wulfe, R. McAllister, A. Gaidon, and M. Pavone, “Heterogeneous-Agent Trajectory Forecasting Incorporating Class Uncertainty,” IEEE/RSJ Int. Conf. on Intelligent Robots & Systems, 2022.
- B. Ivanovic, Y. Lin, S. Shrivastava, P. Chakravarty, and M. Pavone, “Propagating State Uncertainty Through Trajectory Forecasting,” Proc. IEEE Conf. on Robotics and Automation, 2022.
- R. Luo, S. Zhao, J. Kuck, B. Ivanovic, S. Savarese, E. Schmerling, and M. Pavone, “Sample-Efficient Safety Assurances using Conformal Prediction,” Workshop on Algorithmic Foundations of Robotics, 2022.
- B. Ivanovic, “Trajectory Forecasting in the Modern Robotic Autonomy Stack,” Ph.D. dissertation, Stanford University, Dept. of Aeronautics and Astronautics, 2021.
- S. Schaefer, K. Leung, B. Ivanovic, and M. Pavone, “Leveraging Neural Network Gradients within Trajectory Optimization for Proactive Human-Robot Interactions,” Proc. IEEE Conf. on Robotics and Automation, 2021.
- B. Ivanovic, K. Leung, E. Schmerling, and M. Pavone, “Multimodal Deep Generative Models for Trajectory Prediction: A Conditional Variational Autoencoder Approach,” IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 295--302, 2021.
- M. Itkina, B. Ivanovic, R. Senanayake, M. J. Kochenderfer, and M. Pavone, “Evidential Sparsification of Multimodal Latent Spaces in Conditional Variational Autoencoders,” Conf. on Neural Information Processing Systems, 2020.
- B. Ivanovic, A. Elhafsi, G. Rosman, A. Gaidon, and M. Pavone, “MATS: An Interpretable Trajectory Forecasting Representation for Planning and Control,” Conf. on Robot Learning, 2020.
- H. Nishimura, B. Ivanovic, A. Gaidon, M. Pavone, and M. Schwager, “Risk-Sensitive Sequential Action Control with Multi-Modal Human Trajectory Forecasting for Safe Crowd-Robot Interaction,” IEEE/RSJ Int. Conf. on Intelligent Robots & Systems, 2020.
- T. Salzmann, B. Ivanovic, P. Chakravarty, and M. Pavone, “Trajectron++: Dynamically-Feasible Trajectory Forecasting With Heterogeneous Data,” European Conf. on Computer Vision, 2020.
- A. Elhafsi, B. Ivanovic, L. Janson, and M. Pavone, “Map-Predictive Motion Planning in Unknown Environments,” Proc. IEEE Conf. on Robotics and Automation, 2020.
- B. Ivanovic and M. Pavone, “The Trajectron: Probabilistic Multi-Agent Trajectory Modeling with Dynamic Spatiotemporal Graphs,” IEEE Int. Conf. on Computer Vision, 2019.
- B. Ivanovic, J. Harrison, A. Sharma, M. Chen, and M. Pavone, “BaRC: Backward Reachability Curriculum for Robotic Reinforcement Learning,” Proc. IEEE Conf. on Robotics and Automation, 2019.