I’m a Final-year Electronics and Computer Engineering undergraduate student at Thapar University, Patiala, India. My research focus is broadly centered around Computer Vision and Deep Learning. I work on research projects dealing with Generative Adversarial Networks, Knowledge Distillation, and zero-shot classification. I intend to explore the neural networks under the low-resource training data scenario using Knowledge Distillation and the Efficient Subset selection paradigm.
I am currently working as a Research Intern at UiT Norway. In the past, I have also worked in NVIDIA and SketchX as a Research Intern. I have also worked as a part-time Research Engineer at a startup, Minus Zero, where I worked on an autonomous electric car with level 5 autonomy.
[July 2022]Adaptive Fine-Grained Sketch-Based Image Retrieval is accpeted in the ECCV 2022.
[May 2022]Joined UiT Norway as a Research Intern.
[March 2022]Joined NVIDIA as a Research Intern.
[July 2021]Joined SketchX lab as a Research Intern.
[June 2021]Recieved Grant by Weights & Biases for ML Reproducibility Challenge, Spring 2021.
[May 2021]Volunteered for the ICLR Conference 2021.
[September 2020]Won Bronze Metal for Kaggle Notebook in I’m Something of a Painter Myself challenge.
[July 2020]Top 42% Worldwide in Google Landmark Recognition 2020.
Bachelor's in Electronics and Computer Engineering, 2023
Comparing the performance of ResNet and ViT models when trained using the subset of the dataset with different percentages of entire dataset (CIFAR10, CIFAR100, Medical Imaging dataset) (1%, 5%, 10%, and so on).
Experimented with latest Real-Time Lane Detection work and vision transformers for an improved solution for DRIVE-Perceptron platform with faster inference and performance. Responsibilities include:
Worked on Fine-Grained Sketch Based Image Retrieval and Category-Level Sketch Based Image Retrieval. Responsibilities include:
Worked on the Road Segmentation problem for autonomous cars in India. Responsibilities include:
Worked on scene text detection problem with state of the art model. Responsibilities include:
Developed a GAN that generates 7,000 to 10,000 Monet-style images.
Developed a classification model for predicting seven emotions like (angry, disgusted,fearful, happy, neutral, sad and surprised) using FER-2013 dataset.