Currently, Idirect the training programs at Acceleration Consortium (AC) including theAC Training Lab,AC Microcourses,workshops,AC Hackathons, seminars, and outreach. Here, I help build solutions for deployment to the AC's core labs (~30 full-time staff scientists) and the broader ecosystem.

I obtained my Ph.D. in Materials Science and Engineering from the University of Utah in 2023 in Dr. Taylor Sparks' materials informatics group, where I used machine learning to discover new materials for energy and structural applications. I obtained my M.Sc. in Mechanical Engineering from Brigham Young University in 2021, where I conducted hydrogen diffusivity experiments in metals and developed grain boundary property prediction models. I obtained my B.Sc. in Applied Physics from Brigham Young University in 2018, where I conducted experimental research on lithium-sulfur batteries using structurally modified carbon-nanotubes.

A popular saying that resonates with me is: "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 bytigercats, where tasks that once took hours and days now take only minutes and seconds. I've witnessed this as I've invested time in learning hardware and software automation skills and leveraging state-of-the-art algorithms in data science.

• 🔭 I’m currently working onHonegumi, a template generator for Bayesian optimization scripts andmicrocourses for self-driving labs.
• 🌱 I’m currently learning authentication protocols (temporary access credentials via JWT and HiveMQ) for access to remote equipment
• 👯 I’m looking to collaborate on advanced Bayesian optimization topics such as high-dimensional, multi-objective, and multi-task Bayesian optimization with a focus towards materials applications
• 🤔 I’m looking for help -come to the Acceleration Consortium and build out hardware and software solutions for autonomous experiments
• 📫 How to reach me:sterling.baird@utoronto.ca
• ⚡ Fun fact: I like to breakdance

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1. honegumihonegumiPublic

   Honegumi (骨組み) is an interactive "skeleton code" generator for API tutorials focusing on optimization packages.

   Jupyter Notebook 48 7

2. AccelerationConsortium/awesome-self-driving-labsAccelerationConsortium/awesome-self-driving-labsPublic

   A community-curated list of resources related to self-driving labs which combine hardware automation and artificial intelligence to accelerate scientific discovery.

   TeX 150 25

3. AccelerationConsortium/ac-microcoursesAccelerationConsortium/ac-microcoursesPublic

   Microcourses hosted by the Acceleration Consortium for self-driving lab topics.

   Jupyter Notebook 29 3

4. AccelerationConsortium/ac-training-labAccelerationConsortium/ac-training-labPublic

   Codebase for controlling and managing the Acceleration Consortium (AC) Training Lab.

   G-code 9 4

5. sparks-baird/self-driving-lab-demosparks-baird/self-driving-lab-demoPublic

   Software and instructions for setting up and running a self-driving lab (autonomous experimentation) demo using dimmable RGB LEDs, an 8-channel spectrophotometer, a microcontroller, and an adaptive…

   Jupyter Notebook 75 12

6. faith-family-sciencefaith-family-sciencePublic

   Repository hosting the content for my views and journey as a member of the Church of Jesus Christ of Latter-day Saints, a husband and father, and a scientist.

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