Vincent Casser

About Me

I’m a Staff Research Scientist at Waymo, where I’m developing new technologies for autonomous vehicles in areas such as neural rendering, sensor simulation, sensor fusion, multi-task learning and foundational models.

I enjoy interdisciplinary work, and have broad experience in machine learning, deep learning and computer vision, with specific expertise in developing and deploying autonomous and safety-critical technology at scale. Before joining Waymo, I pursued research in domains such as computational perception, UAV navigation and biomedical imaging. More specifically, some of my previous projects were related to the study of human memory (at MIT), machine learning applications in healthcare (with Massachusetts General Hospital), astronomy (with the Harvard-Smithsonian Center for Astrophysics) and electron microscopy (with the Harvard Lichtman Lab).

I hold a Master’s degree in Computational Science and Engineering from Harvard University. I spent one year as a Research Affiliate in Aude Oliva’s lab at MIT CSAIL, where I computationally modeled human visual memory as part of the Memento project. During my time at Harvard, I also interned in the Google Brain Robotics team under Anelia Angelova. Previously, I earned a BSc in Computer Science from the University of Bonn, Germany in 2016, where I minored in physics and astronomy, and worked as a research intern at King Abdullah University of Science and Technology (KAUST).

News

01/29/2024 New paper it ICRA’24: “LET-3D-AP: Longitudinal Error Tolerant 3D Average Precision for Camera-Only 3D Detection”
01/01/2024 I’m organizing the Workshop on Autonomous Driving at CVPR’24 in Seattle, WA
06/29/2023 Recordings of the CVPR WAD 2023 workshop are available now.
01/01/2023 I’m organizing the Workshop on Autonomous Driving at CVPR’23 in Vancouver, Canada
06/20/2022 New paper at IROS’22: “Instance Segmentation with Cross-Modal Consistency”
06/20/2022 Organized the Workshop on Autonomous Driving at CVPR’22
06/14/2022 Our Block-NeRF dataset is now available.
03/01/2022 New paper at CVPR’22: “Block-NeRF: Scalable Large Scene Neural View Synthesis” (oral presentation)
01/16/2022 New preprint: “GradTail: Learning Long-Tailed Data Using Gradient-based Sample Weighting”
07/22/2021 New paper at ICCV’21: “4D-Net for Learned Multi-Modal Alignment”
03/01/2021 New paper at CVPR’21: “Taskology: Utilizing Task Relations at Scale” (oral presentation)
10/14/2020 New paper at CoRL’20: “Unsupervised Monocular Depth Learning in Dynamic Scenes”
07/02/2020 New paper at ECCV’20: “Multimodal Memorability: Modeling Effects of Semantics and Decay on Video Memorability”
07/01/2020 New paper at UIST’20: “Predicting Visual Importance Across Graphic Design Types“
04/10/2020 New paper at Medical Imaging with Deep Learning (MIDL’20): “Fast Mitochondria Segmentation For Connectomics”
02/10/2020 I am co-organizing the 4D-VISION workshop at ECCV’20
01/22/2020 I co-organized the ComputeFest Transfer Learning workshop at Harvard
10/02/2019 New paper at SVRHM, NeurIPS’19: “To Decay or not to Decay: Modeling Video Memorability Over Time”
08/19/2019 I’m joining Waymo as a Research Scientist
05/30/2019 Graduated from Harvard University with a Master’s degree in Computational Science and Engineering
04/30/2019 New paper at Robotics: Science and Systems (RSS’19): “OIL: Observational Imitation Learning”
04/16/2019 New paper at VOCVALC, CVPR’19: “Unsupervised Monocular Depth and Ego-motion Learning with Structure and Semantics”
04/06/2019 New paper at UAVISION, CVPR’19: “Learning a Controller Fusion Network by Online Trajectory Filtering”
01/23/2019 Gave a workshop on “Convolutional Autoencoders for Image Manipulation” at ComputeFest 2019
11/28/2018 New project released: OIL: Observational Imitation Learning
11/27/2018 New blog post on our struct2depth work on Google’s AI blog
11/19/2018 The code for our struct2depth paper is now part of the TensorFlow models repository
11/01/2018 New paper at AAAI’19: “Depth Prediction Without The Sensors: Leveraging Structure For Unsupervised Learning From Monocular Videos”
10/06/2018 Joined the MIT Computational Perception & Cognition Lab, led by Aude Olivia
09/08/2018 We won the best paper award at UAVISION 2018
08/03/2018 We are presenting our work on autonomous drone racing on Sept 8 at UAVISION, ECCV’18
05/29/2018 Started internship in the Google Brain Robotics group
05/22/2018 Our new datasets for connectomics research are now publicly available: Kasthuri++ and Lucchi++
05/21/2018 Release of new tutorial for Bayesian GAN
02/08/2018 Started new project on Connectomics with the Visual Computing Group (VCG)
01/22/2018 Started new collaboration with the Center for Clinical Data Science (CCDS)
11/23/2017 New paper in IJCV: “Sim4CV: A Photo-Realistic Simulator for Computer Vision Applications” (full text)
10/21/2017 Official release of Sim4CV, our simulation environment for Computer Vision
09/01/2017 Started Master’s program in Computational Science and Engineering
08/19/2017 New paper at UAVision, ECCV’18: “Teaching UAVs to Race: End-to-End Regression of Agile Controls in Simulation“
05/24/2017 Recipient of German Academic Scholarship Foundation US-Scholarship (Studienstiftung)
03/28/2017 Recipient of DAAD Graduate Scholarship