ayan@website $ ./project --list _

[1] anyx: Build vector animations from programmatic descriptions

Author(s): Ayan Das, Spandan Ghosh
Link: https://ayandas.me/anyx
Dated: 01 May 2021

Project anyx (pronounced as “anix”) is a python library designed for easily producing high-quality (vector) graphics animations with ease. Although anyx is built with no assumption about its downstream area of application, it is mostly targeted towards scientific community for creating beautiful scientific/technical illustrations. anyx is created as a programmatic alternative to heavyweight and (sometimes) proprietary graphical software. Unlike low-level libraries like pygame, anyx allows users to simply write a description of a target scene and compile it down to the required modality (Video, Animated GIFs etc). The development of anyx is motivated largely by a similar project called manim.

[2] rlx: A modular Deep RL library for research

Author(s): Ayan Das
Dated: 27 Jun 2020

rlx is a Deep RL library written on top of PyTorch & built for educational and research purpose. Majority of the libraries/codebases for Deep RL are geared more towards reproduction of state-of-the-art algorithms on very specific tasks (e.g. Atari games etc.), but rlx is NOT. It is supposed to be more expressive and modular. Rather than making RL algorithms as black-boxes, rlx adopts an API that tries to expose more granular operation to the users which makes writing new algorithms easier. It is also useful for implementing task specific engineering into a known algorithm (as we know RL is very sensitive to small implementation engineerings).

[3] Project MIRIAD: Intel India Pvt. Ltd.

Author(s): Ayan Das, Debdoot Sheet, Rachana Sathish
Dated: 14 Feb 2018

Providing quality health services and screening to rural populations in a nation as large as India can be extremely challenging. For example, India has only three accredited radiologists per million people. Using AI technology to provide more extensive, effective radiological screening has the potential for saving lives and providing overall improvements to health across the country. A unified approach to handling diverse medical images that span modalities presents a distinct challenge to researchers and developers, one requiring a compute-intensive processing platform and an innovative approach to the deep neural network model.