Vaibhav Saxena

I am a Ph.D. student in Machine Learning at the Georgia Institute of Technology advised by Prof. Danfei Xu. My research revolves around generative models for decision-making, memory, and computer vision, with the goal of generalizable robot learning from offline demonstrations!

In 2019, I graduated with a Master of Science in Applied Computing from the Department of Computer Science, University of Toronto, where I was advised by Prof. Jimmy Ba and Prof. Frank Rudzicz, and worked closely with Danijar Hafner. My research involved learning state-space models for long-horizon planning, deep reinforcement learning, and multi-modal representation learning.

See more about my professional experience here.

Google Scholar | Linkedin | Github | CV (Dec 2023)

News

[Mar 2024] We will be organizing the “Data Generation for Robotics Workshop" at RSS 2024 in Delft, Netherlands!
[Feb 2023] I will be returning to Autodesk Research as a Research Intern during Summer 2023
[Jan 2023] Our paper on "Generalizable Pose Estimation using Implicit Scene Representations" got accepted at ICRA 2023
[Dec 2022] I will be presenting our paper on "Guiding Exploration Towards Impactful Actions" at the Deep RL Workshop in NeurIPS 2022
[Feb 2022] I will be spending the Summer of 2022 as a Research Intern at Autodesk Research
[Dec 2021] I will be presenting our paper on "Clockwork Variational Autoencoders" at NeurIPS 2021 [tweet]
[Aug 2021] Joining the ML Ph.D. program at Georgia Tech!
[Apr 2021] Clockwork VAEs featured on Towards Data Science!