Aditya Wagh

๐Ÿ‘‹ Hi. Iโ€™m Aditya, a Computer Vision Engineer & Researcher in the AI4CE Lab headed by Prof. Chen Feng.

I am interested in multimodal perception, localization & mapping algorithms for autonomous systems.

I did my MS at New York University (one of the 35 best universities in the world) and BEng at BITS Pilani (one of the 6 best engineering institutions in India).

During my undergrad, I worked with Dr. Sumeet Saurav in the Intelligent Systems Research Group at CEERI, Pilani, a prominent federal research institute.

In my free time, I watch science fiction, cook delicious food and contribute to open-source projects! To get a better insight on my work, have a look at my ๐Ÿ“ƒResume

News

05/2023 Graduated with a Master of Science in Electrical Engineering from New York University!
I specialized in Robotics, Computer Vision and Artificial Intelligence.
05/2022 Excited to join the A14CE Lab at New York University!
I will be working on perception problems in self driving with Dr. Chen Feng.
08/2021 I am excited to announce that I have started my MS in Mechatronics and Robotics at New York University!
05/2021 I have been offered a place in the Cybernetics and Robotics graduate program at Czech Technical University in Prague!
03/2021 I have been offered a place in the Mechatronics and Robotics graduate program at New York University!
08/2019 I've joined my family business and will be working on it's expansion!
07/2019 Graduated with a bachelors degree in electronics engineering from BITS Pilani!
07/2018 Started my undergraduate research internship @ Central Electronics Engineering Research Institute, Pilani under Dr. Sumeet Saurav!
08/2017 Joined the Oyster Lab (OLAB) at BITS Pilani as an undergraduate research assistant to Dr. Anu Gupta. I will be working on the design of robust Static RAM cells.

Projects

Below is a list of my notable projects. Feel free to have a look at whatever you find interesting!

A visualization in reduced dimension to show how autoencoders learn features in the Fashion MNIST dataset.

Dimensionality Reduction using Convolutional Autoencoders

A visualization in reduced dimension to show how autoencoders learn features in the Fashion MNIST dataset.

Docs Code
Fast, Parallel implementation of the RANSAC algorithm to segment a plane in a point cloud.

Plane Extraction from a 3D Point Cloud

Fast, Parallel implementation of the RANSAC algorithm to segment a plane in a point cloud.

Docs Code
A convolutional neural network for multi-task visual damage assesment.

Post-Earthquake damage assesment using FCNs

A convolutional neural network for multi-task visual damage assesment.

Docs Code
Relative Pose estimation using OpenCV

Two-View geometry based pose estimation

Relative Pose estimation using OpenCV

Docs Code
A Mask R-CNN model for detecting power cables.

Power Cable detection

A Mask R-CNN model for detecting power cables.

Docs Code
A modification in a RNN unit that reduces redundant computation.

Variable Computation in RNNs

A modification in a RNN unit that reduces redundant computation.

Docs Code
A tango inspired dark theme for Visual Studio Code.

Tango+

A tango inspired dark theme for Visual Studio Code.

Docs Code
A simple and elegant Jekyll theme for portfolio pages

Clean Portfolio

A simple and elegant Jekyll theme for portfolio pages

Docs Code