About
I am a machine learning researcher and software engineer with major interests in visual computing. My previous research was mostly related to computer vision: in domains such as computational perception, autonomous navigation and biomedical imaging. I have high interests in interdisciplinary applications, with my previous projects relating to the study of human memory (with 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 my year of research 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 |
 |
 |
 |
 |
|---|