Chethan Parameshwara

I work as a researcher at Amazon, focusing on Multimodal Generative AI, with a specific emphasis on Diffusion Generative Models and 3D Computer Vision.

Before joining Amazon, I graduated with a Ph.D. in Neuroscience and Artificial Intelligence (NACS) from the University of Maryland, College Park (UMD), where I developed neuroscience-inspired computer vision algorithms for autonomous vehicles.

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My selected publications are listed here. The complete list of publications can be seen from my Google Scholar page.

We propose a novel differentiable programming technique for camera pose estimation in autonomous driving and flying scenarios.

We propose a bio-inspired neural network for the motion detection problem using an asynchronous event camera.

We present a novel approach to segment never-seen objects by physically interacting with them and observing them from different views.

We propose a hybrid solution to multi-object motion segmentation using a combination of model-based and deep learning approaches with minimal prior knowledge.

We present a deep learning-based solution for detecting propellers (to detect drones) for mid-air landing and following.

We present the first deep learning based solution for dodging multiple dynamic obstacles on a quadrotor with a single event camera and onboard computation.

We present a novel motion compensation approach for moving object tracking with an asynchronous event camera.

Amazon
Aug, 2022 -- Present
Applied Scientist II

Research on Diffusion Generative Models and 3D Computer Vision (NeRF, Gaussian Splatting, Neural Rendering, and SLAM).

University of Maryland, College Park (UMD)
Aug, 2017 -- Aug, 2022
Graduate Research Assistant with Prof. Yiannis Aloimonos and Dr. Cornelia Fermüller

Developed neuromorphic motion segmentation and tracking algorithms for high-speed and challenging lighting scenarios and also developed differentiable optimization layers (a combination of model-based and data-driven) for camera pose estimation to improve robustness and generalization across scenes.

SRI International (formerly Stanford Research Institute)
Summer, 2021
Research Intern with Dr. David Zhang and Michael Piacentino

Developed a neuro-inspired learning approach for few-shot image classification with a faster convergence rate (10x) and consumes low memory (20x) than existing image classification algorithms.

Neurala
Summer, 2019
Research Intern with Dr. Anatoli Gorchet and Dr. Matthew Luciw

Developed a lifelong machine learning approach to improve few-shot learning capabilities for object detection tasks and deployed the proposed approach on Neurala's Brain Builder software.

Robot Training Academy
Fall, 2016
Software Engineering Intern

Developed hand gesture tracking software for human-robot interaction in kitchen environments and performed testing of perception software modules on Rethink Baxter robot.

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