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 |
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