# Rachel Thomas

Rachel Thomas is a computer scientist and co-founder of fast.ai, an educational platform making deep learning accessible. She previously worked as a machine learning engineer at Uber and is known for her advocacy for ethical and inclusive AI.

Rachel Thomas is a computer scientist, educator, and entrepreneur known for co-founding fast.ai, an organization dedicated to making [deep-learning](https://www.wikiprompt.org/wiki/deep-learning) accessible to a broad audience. Her work focuses on lowering the technical barriers to entry in [artificial-intelligence](https://www.wikiprompt.org/wiki/artificial-intelligence) and promoting ethical considerations within the field. Before her entrepreneurial career, she gained industry experience as a machine learning engineer at Uber, where she worked on applied [machine-learning](https://www.wikiprompt.org/wiki/machine-learning) problems.

Thomas's contributions extend beyond technical development to include significant advocacy for diversity and fairness in AI. She has been a vocal critic of algorithmic bias and has worked to create educational resources that empower individuals from underrepresented groups to participate in the AI community. Her approach emphasizes practical, hands-on learning, often using [neural-network](https://www.wikiprompt.org/wiki/neural-network) frameworks to teach complex concepts.

## Early Career and Education

Thomas pursued graduate studies in mathematics, earning a PhD in the subject. Her academic background provided a strong foundation in the theoretical aspects of computation and statistics, which she later applied to her work in machine learning. Prior to joining Uber, she held positions in academia and the tech industry, where she developed expertise in data analysis and software engineering.

At Uber, Thomas worked as a machine learning engineer, contributing to projects that involved building and deploying models for various operational needs. This role gave her firsthand experience with the challenges of applying [deep-learning](https://www.wikiprompt.org/wiki/deep-learning) techniques in a large-scale production environment, including issues related to data quality, model evaluation, and system reliability.

## Founding fast.ai

In 2016, Thomas co-founded fast.ai with Jeremy Howard, a former president of Kaggle. The organization's mission was to create a free, practical course that would enable anyone with basic programming skills to learn how to build and train [neural-network](https://www.wikiprompt.org/wiki/neural-network) models. The first course, "Practical Deep Learning for Coders," was launched in 2017 and quickly became popular for its top-down teaching approach, which emphasized getting results quickly before diving into underlying theory.

fast.ai's methodology was influential in the [deep-learning](https://www.wikiprompt.org/wiki/deep-learning) community. The platform introduced techniques such as [transfer learning](https://www.wikiprompt.org/wiki/transfer-learning) and [learning-rate-schedule](https://www.wikiprompt.org/wiki/learning-rate-schedule) optimization that allowed students to achieve state-of-the-art results on tasks like image classification with limited computational resources. The accompanying fastai software library, built on top of PyTorch, provided a high-level API that simplified the process of building and training models, making it a widely used tool in both education and research.

## Advocacy and Ethical AI

Thomas has been a prominent voice in discussions about the social implications of [artificial-intelligence](https://www.wikiprompt.org/wiki/artificial-intelligence). She has written and spoken extensively about the dangers of algorithmic bias, particularly in systems that affect marginalized communities. Her critiques often highlight how [machine-learning](https://www.wikiprompt.org/wiki/machine-learning) models can perpetuate existing inequalities if not carefully designed and audited.

She has also been an advocate for increasing diversity within the AI field. Thomas co-organized workshops and initiatives aimed at supporting women and other underrepresented groups in technology. She has argued that a lack of diverse perspectives in AI development leads to systems that fail to serve all users equitably. Her work in this area has been recognized through invitations to speak at major conferences and universities, including [stanford-ai-lab](https://www.wikiprompt.org/wiki/stanford-ai-lab) and [berkeley-ai-research](https://www.wikiprompt.org/wiki/berkeley-ai-research).

## Teaching and Public Engagement

Beyond fast.ai, Thomas has contributed to AI education through various public channels. She has written articles for prominent publications, explaining complex AI concepts to general audiences. Her writing often demystifies technical jargon and provides clear explanations of how [large-language-model](https://www.wikiprompt.org/wiki/large-language-model) and other modern AI systems function.

She has also been involved in creating resources for journalists and policymakers, helping them understand the capabilities and limitations of current AI technologies. This educational outreach is part of her broader goal to foster a more informed public discourse about AI, moving beyond hype to a realistic understanding of both its potential and its risks.

## Legacy and Impact

The fast.ai courses have been used by hundreds of thousands of students worldwide, many of whom have gone on to careers in AI research and engineering. The platform's emphasis on accessibility has been credited with democratizing [deep-learning](https://www.wikiprompt.org/wiki/deep-learning) education, making it possible for people without advanced degrees in computer science to enter the field.

Thomas's work has influenced how AI is taught, with many other educational initiatives adopting similar practical-first approaches. Her advocacy for ethical AI has also contributed to a growing awareness of the need for responsible development practices. As of the early 2020s, she continues to be an active participant in AI research and education, focusing on ensuring that the benefits of AI are shared broadly across society.

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Source: https://www.wikiprompt.org/wiki/rachel-thomas
License: CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0/)
Last updated: 2026-09-09T01:59:04.046208+00:00