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Published in International Confderence on Intelligent Robots and Systems (IROS), 2021
A framework for calculating indirect estimations of state given observations of the behavior of other agents in the environment. We model traffic light estimation as such a problem and apply our framework to recover light state with over 70% accuracy using only the motion of other vehicles in the scene.
Download here
Published in arXiv pre-print, 2023
We propose a method to perform end-to-end learning on raw radar analog-to-digital (ADC) data from imaging radars. Specifically, we design a learnable signal processing module inside the neural network, and a pre-training method guided by traditional signal processing algorithms. The combination of these techniques allows us to achieve state of the art results on the RADIal raw radar dataset.
Download here
Published in arXiv pre-print, 2023
We propose Scene Flow via Distillation, a simple distillation framework that uses a label-free optimization method to produce pseudo-labels to supervise a feed forward model. Our technique yields state of the art results for endpoint error while still running 100x faster than the previously best performing methods.
Download here
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A workshop I co-taught at HackUMass to explain the fundementals of using deep convolutional neural networks for computer vision tasks. You can find the slides here and a video recording of here.
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A workshop I created and co-taught at HackUMass to introduce the basics of machine learning. You can find the slides here and a video recording of here. Additionally you can find the Flask starter code which was created by Christopher Rybicki here.
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A conference talk for our paper titled Agent-aware State Estimation in Autonomous Vehicles. You can find the slides here and a video recording of here.
Undergraduate course, University of Masachusetts Amherst, College of Information and Computer Sciences, 2020
In the spring of 2020 I was a graduate teaching assistant for CS 326 at UMass Amherst taught by Professor Emory Berger. CS 326 is a course in which students work in groups to complete a full web development project from start to finish. The teaching materials cover HTML, CSS and JavaScript and the students have to create a web based application to do anything of their choosing as the final project. As a graduate TA I worked on developing course materials and assignments, all of which can be found on Professor Berger’s GitHub repo for the course here.