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followers: 238.0 following: 368.0 repos: 90.0 gists: 8.0

Name: Sterling G. Baird

Type: User

Company: @AccelerationConsortium (UoT) @sparks-baird (UoU)

Bio: I direct training programs at the @AccelerationConsortium for materials discovery enabled by Bayesian optimization and self-driving labs. Former @sp8rks group

Twitter: SterlingBaird1

Location: Toronto, ON

Blog: https://acceleration.utoronto.ca/researcher/sterling-baird

Hi there 👋

A popular saying goes "give me six hours to chop down a tree and I will spend the first four sharpening the axe." Unlike an axe which dulls with each blow, research skills are often transferable to other "research trees". Eventually, axes are replaced by chainsaws and chainsaws by tigercats, where tasks that once took hours and days now take only minutes and seconds.

🔭 I’m currently working on advanced Bayesian optimization and self-driving labs
🌱 I’m currently learning model deployment via HuggingFace and the Adaptive Experimentation (Ax) Platform
👯 I’m looking to collaborate on advanced Bayesian optimization applied to real-world materials science tasks (see Honegumi) and the creation of low-cost self-driving labs for research and education, such as with AC Microcourses
🤔 I’m looking for help building out the Acceleration Consortium (AC) Training Lab
📫 How to reach me: [email protected]
⚡ Fun fact: I like to breakdance

Sterling G. Baird's Projects

acitve-generative-design

adafruit_circuitpython_pixelbuf

Pure python implementation of adafruit_pixelbuf for smaller boards

amdnet

Code base for AMDNet described in https://doi.org/10.1126/sciadv.abf1754

applications

Mathematica packages that I've developed.

arducam-ov2640-pico-package

Package for Python for the Arducam OV2640

autograding-codespace-python

automatminer

An automatic engine for predicting materials properties.

awesome-materials-informatics

Curated list of known efforts in materials informatics

ax

Adaptive Experimentation Platform

batterygui

baybe

A Bayesian Back End for Design of Experiments

bofire

Experimental design and (multi-objective) bayesian optimization.

botorch

Bayesian optimization in PyTorch

bounded_pool_executor

Bounded Process&Thread Pool Executor

cbfv

Tool to quickly create a composition-based feature vector

cgcnn

Crystal graph convolutional neural networks for predicting material properties.

chemios

Chemios Framework 👨🏾‍🔬: Accelerating Science through Automation

chemos

cif-import

Wolfram Language package for importing crystal structures from CIF files

colabtools

Python libraries for Google Colaboratory

composition-conditioned-crystal-gan

Composition-Conditioned Crystal GAN pytorch code

conda-smithy

The tool for managing conda-forge feedstocks.

crabnet

Predict materials properties using only the composition information!

credit-statement

Generate a CRediT Statement based on a Google Sheets submission form.

cryspy

CrySPY is a crystal structure prediction tool written in Python.

deepparticlenet

A deep neural network for particle size and shape analysis.

discover-supercon-nomad-smact

Composition-based predictions for chemically novel, high-temperature superconductors.

domain-atomistic

Domain ontology for atomistic and electronic modelling

edbo

Experimental Design via Bayesian Optimization

example

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