talismanbrandi
Research Scientist at EAI, Northeastern University and Staff at Harvard Medical School. Focus: Core AI, RNA Biology, Theoretical Particle Physics
Northeastern UniversityBoston, USA
Pinned Repositories
HEPfit
A tool to combine indirect and direct searches for new physics
AI-ML-Tools
Regression, Interpolation and classifications with GSL, TensorFlow and scikit-learn
bat
Bayesian analysis toolkit http://mpp.mpg.de/bat (MPI parallelized version modified for HEPfit)
binding-graph-neural-networks
Neural networks for RBP binding graphs
Causal-Inference-IML-C19
Causal Inference with Interpretable Machine Learning and Shapley values to study the disparities in the spread of COVID-19 in the USA
covid-19-USA-SE
Use of Interpretable Machine Learning for Analyzing Socio-economic disparities and COVID-19 in the USA
eCLIP-ENCODE-GRCH38
ENCODE eCLIP data preprocessing
HEP-ML-AI
Collective efforts to build ML/AI tools for Particle Physics
Human-Mobility-COVID-19
Analysis of human mobility data
Interpretable-ML-bbh
A repository for application of BDTs and Shapley values to associated bbh production at HL-LHC and FCC-hh
talismanbrandi's Repositories
talismanbrandi/DNABERT
DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome
talismanbrandi/sustainableHEP
Codes for Sustainable HEP initiative
talismanbrandi/react-native-in-app-review
The Google Play In-App Review API, App store rating API lets you prompt users to submit Play Store or App store ratings and reviews without the inconvenience of leaving your app or game.
talismanbrandi/HEP-ASTRO-COSMO
HEP/Astroparticle/Astrophysics/Cosmology open source packages. Community effort. Physics people, unite!
talismanbrandi/Diversity-in-Academia
repository for material related to diversity and equity in academia from discussions at DESY
talismanbrandi/JUNE
June is a framework for agent based modelling in an epidemiological and geographical context.
talismanbrandi/bbh-NLO
Simulation for bbh computation with MG5/aMCNLO at LO & NLO
talismanbrandi/HEP-ML-AI
Collective efforts to build ML/AI tools for Particle Physics
talismanbrandi/HEPML-LivingReview
Living Review of Machine Learning for Particle Physics
talismanbrandi/Travel-Advisory-UNWFP
International Travel Advisory
talismanbrandi/q-forever
a la q
talismanbrandi/nngp
Deep neural network kernel for Gaussian process
talismanbrandi/populartimes
talismanbrandi/data-wrangling
For ML/AI and data wrangling.
talismanbrandi/COVID-19
Novel Coronavirus (COVID-19) Cases, provided by JHU CSSE
talismanbrandi/MCFM-8.1-ggWW
Modifications of MCFM-8.1 for a ggWW analysis
talismanbrandi/HEPfit-web
Source code for the HEPfit Website
talismanbrandi/github-calendar
:bar_chart: Embed your GitHub calendar everywhere.
talismanbrandi/HEPfit
A tool to combine indirect and direct searches for new physics
talismanbrandi/Belle_1809.03290
MCMC code to unfold Belle data from https://arxiv.org/abs/1809.03290v3
talismanbrandi/Time-Series-Analysis
This is the first topic as I start my quest to learn Machine Learning. This Repository contains Time series Analysis and Forecasting
talismanbrandi/Small-Business-Loan-project
The files for the Data Incubator project
talismanbrandi/Kalman-Filter-for-Sensor-Fusion
A Sensor Fusion Algorithm that can predict a State Estimate and Update if it is uncertain
talismanbrandi/Unscented-Kalman-Filter-for-Sensor-Fusion
Unscented Kalman Filter Project.The project "unscented Kalman filter" is based on the same structure as the extended Kalman filter. It uses a main file that calls a function called Process Measurement. Anything important happens in this function. The function is part of the class ukf.
talismanbrandi/flavio
A Python package for flavour physics phenomenology in the Standard model and beyond
talismanbrandi/MCFM-v8.0-ggZZ
MCFM-v8.0 modified for ggZZ analysis done in arXiv:1406.6338 and arXiv:1608.00977
talismanbrandi/MCFM-v6.8-ggZZ
MCFM-v6.8 modified for ggZZ analysis done in arXiv:1406.6338
talismanbrandi/HintonNowlan1987_replication
Replication of simulations and results from Hinton and Nowlan (1987) "How learning can guide evolution", Complex Systems, 1 (3), 495-502
talismanbrandi/Machine-learning-templates
talismanbrandi/GR1D
General Relativistic, Spherically Symmetry, Neutrino Transport Code for Stellar Collapse