Name: Ayan Paul
Type: User
Company: Northeastern University
Bio: Research Scientist at EAI, Northeastern University and Staff at Harvard Medical School.
Focus: Core AI, RNA Biology, Theoretical Particle Physics
Twitter: ayanpaul
Location: Boston, USA
Blog: http://www.desy.de/~apaul
Ayan Paul's Projects
Regression, Interpolation and classifications with GSL, TensorFlow and scikit-learn
Bertini 2.0: The redevelopment of Bertini in C++.
Bayesian analysis toolkit http://mpp.mpg.de/bat (MPI parallelized version modified for HEPfit)
Simulation for bbh computation with MG5/aMCNLO at LO & NLO
MCMC code to unfold Belle data from https://arxiv.org/abs/1809.03290v3
Neural networks for RBP binding graphs
Causal Inference with Interpretable Machine Learning and Shapley values to study the disparities in the spread of COVID-19 in the USA
Novel Coronavirus (COVID-19) Cases, provided by JHU CSSE
Use of Interpretable Machine Learning for Analyzing Socio-economic disparities and COVID-19 in the USA
COVID-19 Mobility Data Aggregator. Scraper of Google, Apple, Waze and TomTom COVID-19 Mobility ReportsπΆππ
For ML/AI and data wrangling.
repository for material related to diversity and equity in academia from discussions at DESY
DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome
ENCODE eCLIP data preprocessing
A HEP Program for Flavor Observables
a place to crosscheck the EOS and HEPfit
A Python package for flavour physics phenomenology in the Standard model and beyond
:bar_chart: Embed your GitHub calendar everywhere.
General Relativistic, Spherically Symmetry, Neutrino Transport Code for Stellar Collapse
Graphormer is a deep learning package that allows researchers and developers to train custom models for molecule modeling tasks. It aims to accelerate the research and application in AI for molecule science, such as material design, drug discovery, etc.
HEP/Astroparticle/Astrophysics/Cosmology open source packages. Community effort. Physics people, unite!
Collective efforts to build ML/AI tools for Particle Physics
A tool to combine indirect and direct searches for new physics
Source code for the HEPfit Website
Living Review of Machine Learning for Particle Physics
Replication of simulations and results from Hinton and Nowlan (1987) "How learning can guide evolution", Complex Systems, 1 (3), 495-502
Analysis of human mobility data