Vincent Casser

About Me

I am a research scientist at Waymo working on making transportation safer and easier.

I have a high interest in visual computing and its interdisciplinary applications. Before joining Waymo, I did research in domains such as computational perception, autonomous navigation and biomedical imaging. More specifically, some of my previous projects were related to the study of human memory (at MIT), applications in healthcare (with Massachusetts General Hospital), astronomy (with Harvard-Smithsonian Center for Astrophysics) and microscopy (with Harvard Lichtman Lab). I’ve been an avid programmer for many years now, with extensive experience in a variety of technologies and languages. Several years ago, I also taught several programming courses in college as a teaching fellow. Before doing computer science research, I developed various software applications for non-scientific purposes.

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). I use this webspace for hosting personal projects, and plan to publish some of them publicly here.

News

08/19/2019 I’m joining Waymo
05/30/2019 Graduated from Harvard University with a Master’s degree in Computational Science and Engineering
04/30/2019 One paper accepted at Robotics: Science and Systems (RSS’19): “OIL: Observational Imitation Learning”
04/16/2019 We will present a paper at VOCVALC, CVPR’19: Unsupervised Monocular Depth and Ego-motion Learning with Structure and Semantics
04/06/2019 We will present a paper at UAVISION, CVPR’19: Learning a Controller Fusion Network by Online Trajectory Filtering
02/01/2019 Presented our struct2depth work at AAAI-19 in Hawaii
01/23/2019 Gave a workshop on “Convolutional Autoencoders for Image Manipulation” at ComputeFest 2019
12/16/2018 Preprint of our work on Connectomics now on arXiv
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: Link
11/01/2018 One paper accepted to AAAI’19: “Depth Prediction Without the Sensors: Leveraging Structure for Unsupervised Learning from Monocular Videos” (full text)
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 One paper accepted to 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 released on arXiv: “Autonomous UAV Racing Using Deep Learning
05/24/2017 Recipient of German Academic Scholarship Foundation US-Scholarship (Studienstiftung)
03/28/2017 Recipient of DAAD Graduate Scholarship