Rachel Thomas
Researcher at AnswerDotAI
Queensland, Australia
Summary
🤩
Rockstar🎓
Top SchoolRachel Thomas is a researcher and co-founder of fast.ai with 13+ years of experience building and teaching practical deep learning, having helped create the world’s most popular deep learning course and accompanying open-source library. With a mathematics PhD from Duke and early engineering experience at Uber, she blends rigorous theory, production data science, and pedagogy to make ML accessible and ethically informed. She founded the University of San Francisco’s Center for Applied Data Ethics and has repeatedly brought technical writing to wide audiences—her pieces have hit Hacker News front page over ten times. An active open-source educator, her contributions to fastai courses and a computational linear algebra textbook demonstrate a focus on hands-on notebooks and reproducible learning. Now based in Queensland and researching at AnswerDotAI, she pairs global keynote experience with a practical knack for translating complex models into teachable, socially conscious tools.
13 years of coding experience
11 years of employment as a software developer
PhD Mathematics, PhD Mathematics at Duke UniversityMaster of Science - MS Microbiology and Immunology, Master of Science - MS Microbiology and Immunology at Colorado State UniversityBA Mathematics Computer Science and Linguistics, BA Mathematics Computer Science and Linguistics at Swarthmore College
Stackoverflow
1reputation
1kreached
0answers
1question
Github Skills (22)
batch-normalization10
python10
data-science10
scikit10
machine-learning10
vggnet10
numpy10
keras10
fastai10
deep-learning10
scikit-learn10
jupyter-notebook10
topic-modeling10
nlp10
linear-algebra10
Programming languages (2)
Github contributions (5)
fastai/numerical-linear-algebra
May 2017 - Jul 2017
Free online textbook of Jupyter notebooks for fast.ai Computational Linear Algebra course
Role in this project:
Data Scientist
Data ScientistContributions:50 commits, 3 PRs, 26 pushes in 2 months
Contributions summary:Rachel contributed to a computational linear algebra course by adding and updating Jupyter notebooks. Their work includes implementing and exploring concepts such as gradient descent, matrix decompositions (SVD and NMF), and linear regression. The user demonstrates an understanding of machine learning concepts, including data analysis and model implementation. The user also appears to be involved in educational content creation.
pythonlinear-algebracomputational-linear-algebradata-sciencetextbook
fastai/course-nlp
May 2019 - Jul 2019
A Code-First Introduction to NLP course
Role in this project:Data Scientist
Data ScientistContributions:27 commits, 24 pushes, 1 branch in 1 month
Contributions summary:Rachel initiated the project by creating an initial commit. Subsequent commits demonstrate the user's focus on NLP tasks by importing fastai libraries and working with tokenization. The user worked through the use of matrix factorization techniques using SVD, NMF for topic modeling. The user appears to be building a machine learning course, focusing on NLP using techniques of sklearn and fastai.
nlppythondata-sciencemachine-learningcode-first
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