Hi! I'm Atharva Chandak
My research interests include
I'm a software engineer at Wells Fargo, working across full-stack development and AI-driven solutions. My background includes research in autonomous systems, continual learning, and robotics at places like Mila, Harvard, and IISc.
I focus on building wide range of research and industrially scalable, real-world AI applications - whether it's optimizing machine learning models, designing intelligent chatbots, or improving object detection for robotics. This site is a collection of my work, projects, and research.
If you're into Deep Learning, AI, software, or just solving interesting problems, let's connect and build something remarkable!
Experience
- Full-stack developer using Spring Boot and React to support 18k+ merchants offering Private Label Credit Cards & Loans.
- Building API services for Point of Sale EMIs to enable convenient payment for customers.
- Prototyping a secure and scalable personalized chatbot for merchants to help simplify UX and increase engagement.
- Pursued autonomous driving focused Continual Object Detection as part of undergraduate thesis.
- Proposed new approaches for on-device Domain-Incremental Object Detection for robotic applications.
- Achieved a domain incremental avg mAP50 of 68.63 and mAP of 43.89 on the CLAD-D dataset.
- Worked on long-range detection and tracking of aerial vehicles to autonomously avoid collisions.
- Analysed a new dataset for allowing building better models robust to out of distribution climatic conditions.
- Designed optimized models for real-time usage on the drone capturing high resolution feed from multiple cameras.
- Conceptualized and created an end-to-end AI enabled speech chatbot for answering user home lending queries.
- Integrated speech-to-text and text-to-speech APIs for allowing speech interaction with the bot.
- Implemented NLP pipeline for intent and entity extraction and achieved an F1 scores of 93.4% and 83.5% respectively.
- Authored the action server to process the user request demands and response curation for replying back to user.
- Contributed to the Pytorch Connectomics package adding cellpose model for neuron instance segmentation.
- Explored semi-supervised methods to improve upon the performance of 3D segmentation.
- Designed an end-to-end pipeline using long range affinity learning and transformers for improving model accuracy.
- Investigating various Vision-Transformer networks for fine-grain human action recognition.
- Reviewed the existing works on fine-grained action recognition and summarised the major research gaps.
- Exploring better temporal modelling techniques for improving performance on RGB frames directly instead of using optical flow.
- Worked on Generalized Continual Zero Shot Learning for various Computer Vision tasks.
- Integrated the incremental learning setup with zero shot learning for more realistic adaption of DL methods in everyday scenario.
- Extended the work to generalized, out of distribution tasks and also enable task free learning.
- Worked on texture classification of images using both traditional Computer vision with ML algorithms and Deep Learning based methods.
- Used traditional computer vision algorithms like FAST, ORB & BRISK combined with ML classifiers like SVMs, KNNs, etc.
- Extended the project to also implement simple image segmentation networks for performing texture segmentation useful for detecting cracks and faults in leather.
Projects
A GAN based model to convert low resolution images to high resolution.
A simulated drone which delivers multiple packages to their destinations, optimizing for time and quantity.
A jekyll template for easy creation of course websites.
Skills
Languages
Libraries & Frameworks
