danOSU
Phd candidate in Theoretical Physics. Studying Quark Gluon Plasma (QGP) made in relativistic heavy ion collisions. Has a background in Electrical Engineering.
Ohio State UniversityColumbus
Pinned Repositories
Taweret
Python package for Bayesian Model Mixing
approxposterior
A Python package for approximate Bayesian inference and optimization using Gaussian processes
bayesian_net_emu
Bayesian_parameter_inferece_for_VAH
emulator-validation
Bayesian parameter estimation for relativistic heavy-ion collisions
multifidelity_emulation
Building multi-fidelity emulators for Relativistic Heavy Ion Collisions.
QGP_Bayes
Bayesian parameter estimation code for Relativistic Heavy Ion Collisions
superMC
This is a version of the superMC for generating hydrodynamic initial conditions.
trento
Heavy-ion collision initial condition model
vah_argonne
danOSU's Repositories
danOSU/QGP_Bayes
Bayesian parameter estimation code for Relativistic Heavy Ion Collisions
danOSU/Bayesian_parameter_inferece_for_VAH
danOSU/approxposterior
A Python package for approximate Bayesian inference and optimization using Gaussian processes
danOSU/bayesian_net_emu
danOSU/emulator-validation
Bayesian parameter estimation for relativistic heavy-ion collisions
danOSU/multifidelity_emulation
Building multi-fidelity emulators for Relativistic Heavy Ion Collisions.
danOSU/trento
Heavy-ion collision initial condition model
danOSU/vah_argonne
danOSU/bandframework
This contains the public repository for the BAND framework project.
danOSU/bandframework.github.io
danOSU/Causal_ML
Learning materials and examples on Causal ML
danOSU/cvmfs-singularity-sync
Scripts to synchronize Singularity images to a CVMFS repository.
danOSU/cython_osc
simple cython parallel script to run in the Ohio State Super Computing Cluster Owens
danOSU/danOSU.github.io
Use this template if you need a quick developer / data science portfolio! Based on a Minimal Jekyll theme for GitHub Pages.
danOSU/danOSU.webpage
My personal Website
danOSU/einsteinpy
Repository for the EinsteinPy core package :rocket:
danOSU/emukit
A Python-based toolbox of various methods in uncertainty quantification and statistical emulation: multi-fidelity, experimental design, Bayesian optimisation, Bayesian quadrature, etc.
danOSU/Isnet_2021_TL
Notebooks and Presentation material for Transfer Learning Emulation at ISNET 2021
danOSU/KTIso
danOSU/MAP_5020
Calculations for MAP observable of PbPb collisions at 5.02 TeV with Grad viscous corrections.
danOSU/observables
Python scripts to calculate observables for relativistic heavy ion collisions
danOSU/SummerSchool2021
danOSU/surmise
A python package for surrogate models that interface with calibration and other tools
danOSU/Taweret
Python package for Bayesian Model Mixing
danOSU/taweret.github.io
danOSU/tl_trento
In this repo we will develop new transfer learning emulation methods that can be used when source and target have different model parameters. We will use Trento-2D and Trento-3D models with pseudo observables as our test cases for new TL methods.
danOSU/TransferLearningEmulation
Efficient emulation with transfer learning techniques.
danOSU/uncertainty_in_ML
How much do you trust the predictions from your machine learning model? Depending on where you apply the machine learning model this question might be a matter of life and death. Can we identify the possible sources that contribute to make your machine learning model predictions inaccurate? If we can not completely get rid of these uncertainties can we at least quantify it? What are the ways that we can quantify the uncertainty in machine learning model predictions? These are the questions that we will try to find answers in "Uncertainty in ML" working group in Erdos boot camp in the fall of 2021.
danOSU/visualization_vah
This is a streamlit widget made to visualize the initial simulation data from the VAH project. This is largely based on a widget developed by Derek Everett.
danOSU/Winter-2023