Who is Rachel Thomas?

Rachel Thomas is a computer scientist, academic, and entrepreneur who founded the deep learning and AI non-profit Fast.ai with husband Jeremy Howard.

Thomas was also an early engineer at Uber and in 2017, was named by Forbes as one of the top twenty women advancing artificial intelligence research.

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Education and early career

Thomas developed an interest in programming in high school and later earned her Ph.D. in Mathematics from Duke University in 2010.

Her doctoral research focused on stochastic inputs for mathematical representations of biochemical networks, and she also designed Markov models to compare the cost-effectiveness of various HIV treatment protocols.

Thomas then landed her first role at the oil and gas company Exelon Generation. There, she wrote code to scrape data from the web to generate reports for energy traders, and later refined a Monte Carlo model in C++ that incorporated data on energy load, prices, and the weather.

In April 2012, she joined Quantcast on the Technical Projects team and created a web application and database to automate the analysis and processing of financial data.

Uber

Thomas then joined Uber as a software engineer in June 2013. Over her 18 months with the company, she wrote jobs for driver GPS points to improve pickups and also created visualizations of their approach.

Later, Thomas engineered changes to an algorithm used for dynamic surge pricing and also conducted research into neighborhood-based dynamic pricing.

Fast.ai

As noted above, Thomas founded Fast.ai with husband Jeremy Howard in June 2016. Fast.ai is a non-profit research lab that looks at ways deep learning can be made more accessible and applicable. Three of its courses include:

Practical Deep Learning for Coders.
Cutting Edge Deep Learning for Coders, and
Computational Linear Algebra.

When asked by OnlineEducation what sort of problems kept her awake at night, Thomas responded with “making deep learning easier to use and getting it into the hands of people that wouldn’t normally have access.”

She then went on to use the example of one of Fast.ai’s students. The student, a Canadian dairy farmer, wanted to use AI to improve the health of his goats and may not have been able to benefit from the technology without the company’s course.

Deep Learning Indaba

In January 2018, Thomas joined Deep Learning Indaba as an advisor. The non-profit organization has a simple mission: “to strengthen machine learning and artificial intelligence in Africa.”

It does this via programs, conferences, and collaborations that aim to establish a sustainable community of AI expertise in Africa and address chronic shortages of healthcare professionals with machine learning and other technology.

The latter is a topic dear to Thomas’s heart and one she has spoken about often. She once referenced a report released by the World Economic Forum (WEF) and Boston Consulting Group (BCG) stating that to meet OECD levels and address skills shortages, Nigeria, for example, would require an additional 700,000 doctors by 2030.

Deep learning education

More recently, Thomas has sat on the Board of Directors for the Women in Machine Learning group and as a Professor of data science and ethics at the Queensland University of Technology (QUT) in Australia.

One of her more notable achievements, however, was the establishment of the first open-access, university-accredited, in-person deep-learning certificate. The course, which was published on Fast.ai in October 2016, was developed while Thomas was a researcher at The Data Institute – a part of the University of San Francisco.

Key takeaways:

Rachel Thomas is a computer scientist, academic, and entrepreneur who founded the deep learning and AI non-profit Fast.ai. Thomas was also an early engineer at Uber and was named by Forbes as one of the twenty women advancing artificial intelligence research in 2017.
Thomas developed an interest in programming in high school and later earned her Ph.D. in Mathematics from Duke University in 2010. Her interests became apparent at the intersection of mathematics and healthcare, but she also created algorithms for energy traders and pricing mechanisms for Uber.
After launching the Fast.ai course platform with husband Jeremy Howard, Thomas joined the non-profit Deep Learning Indaba to build AI and ML expertise in Africa and address chronic shortages in healthcare staff.

Key Highlights

Rachel Thomas: Rachel Thomas is a computer scientist, academic, and entrepreneur known for her significant contributions to the field of deep learning and artificial intelligence (AI).
Fast.ai: Rachel Thomas co-founded Fast.ai, a non-profit research lab, with her husband Jeremy Howard in June 2016. Fast.ai’s mission is to make deep learning more accessible and applicable by offering practical courses and resources. The organization aims to empower individuals from diverse backgrounds to learn and apply deep learning techniques.
Education and Early Career: Rachel Thomas developed an interest in programming during high school and went on to earn her Ph.D. in Mathematics from Duke University in 2010. Her academic pursuits combined mathematics and healthcare, including the development of mathematical models for biochemical networks and HIV treatment protocols.
Uber Engineer: After her academic journey, Rachel Thomas worked as a software engineer at Uber. During her time at the company, she contributed to improving driver pickups by creating visualizations of driver approaches. She also worked on dynamic surge pricing algorithms and researched neighborhood-based dynamic pricing.
Deep Learning Education: Rachel Thomas’s passion for education and accessibility led her to co-create Fast.ai’s courses, such as “Practical Deep Learning for Coders,” “Cutting Edge Deep Learning for Coders,” and “Computational Linear Algebra.” These courses aim to make deep learning concepts understandable and applicable to a wider audience, enabling individuals without traditional AI backgrounds to benefit from the technology.
AI for Social Impact: Rachel Thomas’s dedication to using AI for social impact is evident through her involvement with initiatives such as Deep Learning Indaba. As an advisor to the organization, she contributes to building AI expertise in Africa and addressing critical shortages of healthcare professionals through AI and machine learning technologies.
Academic Leadership: Rachel Thomas has played a pivotal role in advancing AI education and research. She served on the Board of Directors for the Women in Machine Learning group and held the position of Professor of data science and ethics at the Queensland University of Technology (QUT) in Australia.
Open-Access Deep Learning Education: A notable achievement in Rachel Thomas’s career is the establishment of the first open-access, university-accredited, in-person deep learning certificate. This course, developed while she was a researcher at The Data Institute (University of San Francisco), highlights her commitment to democratizing AI education.