JUAMI

Collaboration to build research ties between PhD materials science researchers in East Africa, the US, and beyond.

Team

aplymill: Lead Developer, 2019 - present
mspencer: Developer, 2019 - present
Yao Tong: Developer, 2019 - present
jhitt: Developer, 2019 - present
sbillinge: PI, 2010 - present
wzyuan: Developer, 2019 - present

coder

CODER, the committee on diversity and equity in research, is a Billinge group initiative. The committee currently has a mission to increase inclusivity in the research group with a longer term goal of increasing the racial, ethnic and gender diversity of the group. Committee members present a discussion segment on these issues to be had by the whole group every two weeks during group meeting, and also lead other social efforts in the group such as group movie nights and a stress-reducing intentional breathing segment during group meeting every week.

Team

sbillinge: Mentor, 2021 - present
sskjaervoe: postdoc, 2021 - present
jumana: ug, 2021 - present
zthathcher: gra, 2021 - 2022
ashaaban: ug, 2021 - 2021
raiden: ug, 2021 - 2021
llan: gra, 2021 - present
eshen: gra, 2022 - present
sbillinge: Mentor, 2021 - present
sskjaervoe: postdoc, 2021 - present
jumana: ug, 2021 - present
zthathcher: gra, 2021 - 2022
ashaaban: ug, 2021 - 2021
raiden: ug, 2021 - 2021
llan: gra, 2021 - present
eshen: gra, 2022 - present
sbillinge: Mentor, 2021 - present
sskjaervoe: postdoc, 2021 - present
jumana: ug, 2021 - present
zthathcher: gra, 2021 - 2022
ashaaban: ug, 2021 - 2021
raiden: ug, 2021 - 2021
llan: gra, 2021 - present
eshen: gra, 2022 - present
sbillinge: Mentor, 2021 - present
sskjaervoe: postdoc, 2021 - present
jumana: ug, 2021 - present
zthathcher: gra, 2021 - present
ashaaban: ug, 2021 - present
raiden: ug, 2021 - present

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diffpy

software for modeling nanostructures principally from atomic pair distribution function (PDF) data.

Team

sbillinge: Mentor and developer, 2005 - present
pjuhas: lead developer, 2005 - present
chliu: developer, 2017 - present
Long Yang: developer, 2018 - present

[https://github.com/diffpy]

dmref15

Advancing applied mathematics and computational approaches to the study of the nanostructure of materials

Team

sbillinge: PI, 2015 - present
qdu: co-pi, 2015 - present
dhsu: co-pi, 2015 - present
chliu: gra, 2017 - 2018
cwright: gra, 2017 - 2018
jgong: gra, 2017 - 2018
mwaddell: gra, 2018 - 2018
sbanerjee: gra, 2018 - 2018
jgong: gra, 2015 - 2017
rgu: postdoc, 2017 - present
ytao: gra, 2017 - 2017

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dmref19

We recently identified that intrinsic orbital degeneracy lifting can result in broken symmetry states existing in d-electron materials at high temperatures. The group of A. Zunger, Co-PI on this project, recently independently found that standard density functional theory quantum mechanical calculations can sometimes predict symmetry broken ground-states in materials where the most stable structure is lower in energy, and in symmetry, than the observed crystal structure. This project is to try and gain insight into this novel phenomenon through a combined mathematics, AI, theory, and experimental campaign.

Team

sbillinge: PI, 2019 - present
qdu: co-pi, 2019 - present
jowen: co-pi, 2019 - present
azunger: co-pi, 2019 - present

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dmrefcheme16

Apply AI and machine learning to get better gas separation membranes

Team

sbillinge: Co-PI, 2017 - present
skumar: pi, 2017 - present
chliu: gra, 2017 - 2019
cwright: gra, 2017 - 2019
mterban: gra, 2017 - 2017
sbanerjee: gra, 2017 - 2018

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efrc18

A group project led from Stony Brook University. Columbia is a major partner with myself leading one of the thrusts. The goal is to use in-situ synthesis and data analytics to try and understand the science behind materials synthesis

Team

sbillinge: Co-PI, 2018 - present
yrakita: postdoc, 2019 - present
cwright: gra, 2018 - 2019
chliu: gra, 2018 - 2020
sbanerjee: gra, 2018 - 2019
rgu: postdoc, 2019 - present
John Parise: pi, 2018 - present

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fwp17

Searching for local structural effects in strongly correlated electron materials using x-ray and neutron scattering

Team

sbillinge: PI, 2008 - present
ebozin: co-pi, 2017 - present
rkoch: postdoc, 2018 - present

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matsci_colab

This project will build a one-semester accelerated course in machine learning (ML) applied to STEM research problems for graduate and senior undergraduate level students in physical science and engineering. Whilst data analytics, artificial intelligence (AI) and machine learning have revolutionized many aspects of life from commerce to politics and human interactivity, its impacts in the physical sciences have been slower. This is now rapidly changing, with disruptive use of AI and ML to attack research problems that seemed unreachable just a few years ago. There is a strong hunger among physical science and engineering students to attain new research skills enabled by ML and to apply them to problems in their domain. The current training for this cohort of students includes extensive training in calculus, differential equations, a bit of linear algebra, rudimentary statistics and nothing in the way of ML. The students often have an aptitude and some training in basic programming. Our goal with this project is to design a course that will build on the solid math skills of this group to accelerate their understanding and ability to adopt ML in their work. The course will build on this existing knowledge by combining a highly compressed introduction to ML taught by the Statistics department, but with a focus on newly developed hands-on projects (that we call Labs) where real (published) problems that apply different ML methods to materials science problems. The Labs will be developed using the Google Colab platform and use the actual datasets from the published examples. The developments will be made following strict standards for coding and documentation and maintained in a central GitHub repository. As such, it will form a platform that will allow further Labs to be developed in the aread of Materials Science but also more broadly. Indeed, this can turn into a widely used community resource that is under continuous development using standard open-source community development practices.