I am actively working at AthenaAgent towards the long-term goal of the development of reliable and scalable ANI (Artificial Narrow Intelligence) agents. My research areas are focused on the development of robust and scalable visual agentic systems for niche and impactful businesses.
My Career Timeline
| Position | Organization | Duration |
|---|---|---|
| Machine Learning Researcher | AthenaAgent | July 2025 - Present |
| Machine Learning Engineer | Roboflow | Apr 2025 - July 2025 |
| Machine Learning Engineer | Weights & Biases | Jan 2022 - Mar 2025 |
| Staff Machine Learning Engineer | Ignitarium | Jul 2021 - Dec 2021 |
| Software Development Engineer | IBM | Jan 2020 - Jul 2021 |
| Software Development Engineer | HighRadius | Jun 2019 - Dec 2019 |
| Failed entrepreneur and Machine Learning Researcher | DeepWrex Technologies | Nov 2018 - Dec 2019 |
Additionally, I’ve been a Google Developer Expert for Machine Learning (JAX) since Jun 2022.
Major Projects
- Inksphere: Transform books into immersive reading experiences (Demo).
- Psynapse: A cross-platform node-based UI editor for building AI workflows using Python.
- Trackers: Trackers is a unified library for object tracking featuring clean room re-implementations of leading multi-object tracking algorithms. I am one of the core maintainers actively implementing SoTA multi-object trackering techniques, Re-ID models, training, and fine-tuning pipelines. Check out the docs for more information.
- WandB Models: WandB Models is the AI developer platform used to train and fine-tune models, and manage models from experimentation to production. I have contributed mojor integrations of WandB Models with open-source ML libraries like Hugging Face Diffusers, Hugging Face AutoTrain, Keras, MMEngine, PyTorch Geometric, Ultralytics, YOLOv5, MONAI, etc. I have also authored numerous technical reports and developed experimental tooling for ML practitioners.
- WandB Weave: WandB Weave is a toolkit for developing Generative AI applications, built by Weights & Biases. I have contributed mojor integrations of Weave with LLM SDKs like Groq, Hugging Face Hub, Google; and LLMOps frameworks like DSPy, Instructor, and SmolAgents.
- Hemm: Holistic Evaluation of Multi-modal Generative Models: Hemm is a library for performing comprehensive benchmark of text-to-image diffusion models on image quality and prompt comprehension integrated with Weights & Biases and Weave.I am currently actively working on this project. Check out the docs for more information.
- Weights & Biases Addons: Weights & Biases Addons is a repository that provides of integrations and utilities that will supercharge your Weights & Biases workflows. Its a repositpry built and maintained by WandB users for WandB users. The library hosts experimental utilities and integrations built using Weights & Biases. I am currently actively working on this project. Check out the docs for more information.
- Restorers: Restorers is a library provide out-of-the-box TensorFlow implementations of SoTA image and video restoration models for tasks such as low-light enhancement, denoising, deblurring, super-resolution, etc. You can read more about it in this WandB report.
Deep Learning examples published on keras.io
- Multiclass semantic segmentation using DeepLabV3+
- Low-light image enhancement using MIRNet
- Zero-DCE for low-light image enhancement
- Large-scale multi-label text classification in colaboration with Sayak Paul
- Point cloud segmentation with PointNet in colaboration with Sayak Paul
- GauGAN for conditional image generation in colaboration with Sayak Paul
Other Interesting Projects
- Syntia: Syntia brings modern IDE features to your terminal. Built with Textual, Syntia combines the power of a traditional code editor with the convenience of an integrated terminal environment.
- Radium: A small and lightweight Ray Tracing Engine written in C++ that runs on the CPU using shared-memory multiprocessing.
- Colorization using Optimization: Python and C++ implementations of a user-guided image/video colorization technique as proposed by the paper Colorization Using Optimization. The algorithm is based on a simple premise; neighboring pixels in space-time that have similar intensities should have similar colors. This premise is formalized using a quadratic cost function that obtains an optimization problem that can be solved efficiently using standard techniques. While using this alogorithm, an artist only needs to annotate the image with a few color scribbles or visual clues, and the indicated colors are automatically propagated in both space and time to produce a fully colorized image or sequence. The annotation can be done using any drawing tool such as JSPaint or Gimp.
- Deep Deterministic Policy Gradients: Pytorch implementation of the Deep Deterministic Policy Gradients Algorithm for Continuous Control as described by the paper Continuous control with deep reinforcement learning.
- Twin Delayed DDGP: Pytorch Implementation of Twin Delayed Deep Deterministic Policy Gradients Algorithm for Continuous Control as described by the paper Addressing Function Approximation Error in Actor-Critic Methods.
- Arxiv2Kindle: Arxiv2Kindle is a simple script written in python that converts LaTeX source downloaded from Arxiv and recompiles it to better fit a reading device (such as a Kindle).
- Manga Scraper: A a python package that downloads Manga into chapterwise PDF files or a single PDF file from various sources. It basically adds a post-processing layer on top of the basic functionality by
mangadl-bashcreated by Akianonymus in order to convert the downloaded manga into chapter-wise PDF files or a single giant PDF file.