MedARC | Open Source Researcher

Nov, 2022 - Present

Working on projects related to neuro AI. 

My work on fMRI-to-image reconstruction was published in Neurips 2023. Also worked on the Algonauts 2023 challenge for predicting fMRI responses from a given image. Currently working on EEG-to-image reconstruction.

Morphle Labs | ML Engineer

Sep, 2021 - Oct, 2022

Morphle is a biomedical robotics company that builds automated microscopes.  

My work was centered around computer vision for pathology. I built two sets of AI models that integrate with the microscope. The first set enables the robot to optimally capture critical areas of a slide. This is achieved by rapidly taking high FoV images at low resolution and detecting important areas using deep learning models. This is followed by optimal path calculation using algorithms like DFS and rescanning these important areas at high resolution. The second set of models works on top of high resolution images to detect cells and other inclusions. Some examples include detection of malarial parasites, blast cells (leukemia) and brain tumours.

Visual One | ML Intern

Jan, 2021 - Jul, 2022

VisualOne was a Computer Vision startup building a few-shot learning framework, specifically for event detection using security cameras.

I worked on increasing the robustness of object detection. The final approach consisted of 3 modifications to the training step:

• Distillation to transfer knowledge from a larger and more robust model, to a smaller deployable model.
  
• Mixup to smoothen the decision boundaries and reduce strong variations in output due to small changes in input.
  
• A variant of focal loss with a specialized gamma schedule that helps in model calibration.

Pixxel | AI Research Intern

Jan, 2020 - Aug, 2020

Pixxel is a Remote Sensing startup working towards building a health monitor for the planet. It aims to launch a constellation of nanosatellites for real time satellite imagery and analytics. After Pixxel became a startup I was formally employed as an intern, working alongside my coursework.

• Trained a model to segment objects of interest from satellite imagery using few-shot learning. The classes in the XView dataset were used.
  
• Trained an image2image model to synthesize multispectral imagery using radar satellite data. Achieved a validation PSNR of 28.9
  

Aalto University | Research Assistant

June, 2021 - July, 2022

My research was in the domain of extreme classification — classification problems with millions of labels.

My research led to 3 publications:

• CascadeXML: A novel Tree-In-Transfomer model that first clusters the label space and successively refines it. Published in Neurips, 2022 this approach is the current state-of-the-art for extreme classification of text.
  
• InceptionXML: A lightweight CNN model that applies convolutions in a novel way to outperform transformers like Bert for short-text (text queries with ≤ 10 words) extreme classification.
  
• Gandalf: A data augmentation technique that improves performance of other extreme classification approaches by leveraging label features in a novel way.

Visual Computing Group, Harvard University | Research Intern/Assistant

July, 2020 - November, 2021

Worked under the guidance of Prof. Hanspeter Pfister. Worked on Instance Segmentation of natural images using metric learning, Connectomic Segmentation from 3D electron microscope imagery, and Single Image Super Resolution.

Pixxel | AI Team Lead

May, 2018 - Dec, 2019

Before it was a startup, Pixxel started off as a student team in BITS Pilani. I was a member of the Pixxel AI Team during its inception, and a few months later became the AI lead. During my time at Pixxel I worked on computer vision models for extracting information from satellite imagery. The primary focus of my work was  to build a high accuracy model for extracting Indian road networks as graphs from satellite imagery. I also worked on models for crop yield prediction and building segmentation.

MapMyIndia | Study Project

September, 2018 - November, 2018

I worked on a project for detecting and classifying various kinds of Road Signs appearing on Indian Roads(advertisements, traffic signs, etc.). I trained various single-shot and region based object detection algorithms on a dataset provided by MapMyIndia with around 16,000 annotated images across 30 classes and compared their speed and accuracy. For the final submission I trained a network based on YOLO-v3 that achieved a mAP score of 89.71 and F1-score of 0.94

BITS Pilani Coding Club | Apogee Joint Coordinator

April, 2019 - July, 2020

I was a member of the Game Development and ML Teams of BITS Pilani Coding Club from August 2017 till my graduation in 2021. I was the club's Joint Coordinator for Apogee, BITS Pilani's Tech Fest, between from 2019 to 2020.

As the Joint Coordinator for Apogee 2020, I co-led the creation of a gamified (Pokemon Go like) AR app for crowd control during the fest.

Prior to this, as a part of the club's game development team, I was responsible for designing and building original games to be played by students attending the various fests at BITS. I have worked on Kinect and VR based games.

Apart from this, I helped organize various workshops and hackathons. I helped organize a workshop for teaching 3D modelling and game programming. As a member of the ML team, I organised a Machine Learning hackathon, sponsored by Yes Bank, during Apogee 2019.