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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
About
About me
Contact
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Posts
Blog Post number 4
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 2
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 1
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
publications
Agent-aware State Estimation in Autonomous Vehicles
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
ADCNet: End-to-end perception with raw radar ADC data
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
ZeroFlow: Fast Zero Label Scene Flow via Distillation
Published in International Conference on Learning Representations (ICLR), 2024
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
I Can’t Believe It’s Not Scene Flow!
Published in European Conference on Computer Vision (ECCV), 2024
Current scene flow methods broadly fail to describe motion on small objects, and current scene flow evaluation protocols hide this failure by averaging over many points, with most drawn larger objects. To fix this evaluation failure we propose a new evaluation protocol: Bucket Normalized EPE, and a frustratingly simple supervised scene flow baseline to achieve SOTA: TrackFlow.
Download here
Neural Eulerian Scene Flow Fields
Published in Submission, 2024
We reframe scene flow as the task of estimating a continuous space-time ODE that describes motion for an entire observation sequence, represented with a neural prior. Our method, EulerFlow, optimizes this neural prior estimate against several multi-observation reconstruction objectives, enabling high quality scene flow estimation via pure self-supervision on real-world data.
Download here
talks
Introduction to Deep Learning for Computer Vision
Published:
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.
Introduction to Machine Learning
Published:
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.
Agent Aware State Estimation in Autonomous Vehicles
Published:
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.
teaching
CS 326 Web Programming
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.