Felipe Felix Arias

I work on Machine Learning/AI at Uber, where I focus on AI4Ops and safety. Previously, I was an MS student and NSF Graduate Research Fellow at the University of Illinois at Urbana-Champaign (UIUC), where I worked on applied machine learning for robotics. I received my Bachelor of Science in Computer Science with Honors from UIUC in 2019 and have been fortunate enough to work on great projects at Uber, UIUC, Stanford, and the University of California Berkeley.

Currently, I am working on my next big project. Please check out Marovi, an open-source project dedicated to translating AI academic and technical writing into various languages, with a primary focus on Spanish. Please reach out if you're in the Bay Area and interested in translation and bringing AI resources to underrepresented languages.

In my free time, I enjoy making music and taking pictures/videos.

felipefelixarias AT gmail.com  /  Google Scholar  /  CV  /  Github  /  LinkedIn

We propose a self-supervised methodology for learning to identify regions critical to obstacle avoidance and leverage it to construct probabilistic roadmaps well-suited for multi-agent motion planning.

Mutiple viewpoints should provide additional information for reinforcement learning-based navigation, this work is the first step towards determining the best way to leverage the cameras of all the agents in the scene.

Risky events are subjective and hard to detect due to the lack of real-world training data. We use weak-supervision and heuristics to track human behavior in the roadways, assess risk, and detect areas that may need more law enforcement or traffic signs.

Snorkel combines heuristics to train a neural network that can classify semantic relationships from a single sentence as well as one trained with hand-labeled data. We develop a metholodogy and novel heuristics for extracting relationships across multiple sentences.

A big thank you to Jon Barron for the website template.