Speaker: Rachel Thomas

fast.ai founder & USF assistant professor

Rachel Thomas has a math PhD from Duke and was selected by Forbes as one of “20 Incredible Women Advancing AI Research.” She is co-founder of fast.ai and a researcher-in-residence at the University of San Francisco Data Institute, where she teaches in the Masters in Data Science program. Her background includes working as a quant in energy trading, a data scientist + backend engineer at Uber, and a full-stack software instructor at Hackbright.

Find Rachel Thomas at

Keynote : Analyzing & Preventing Unconscious Bias in Machine Learning

Tracks

  • Deep Learning Applications & Practices

    Deep learning lessons using tooling such as Tensorflow & PyTorch, across domains like large-scale cloud-native apps and fintech, and tacking concerns around interpretability of ML models.

  • Predictive Data Pipelines & Architectures

    Best practices for building real-world data pipelines doing interesting things like predictions, recommender systems, fraud prevention, ranking systems, and more.

  • ML in Action

    Applied track demonstrating how to train, score, and handle common machine learning use cases, including heavy concentration in the space of security and fraud

  • Real-world Data Engineering

    Showcasing DataEng tech and highlighting the strengths of each in real-world applications.