Samuel Dooley

Samuel Dooley is a research scientist specializing in human-centered machine learning, currently working at Meta. His research areas include large language models (LLMs), neural architecture search (NAS), hyperparameter optimization (HPO), and predictive analytics. He holds a Ph.D. in Computer Science from the University of Maryland, along with a Master’s in Statistics from George Washington University and a Bachelor’s in Mathematics from the University of Chicago. As an interdisciplinary researcher, his work has been published at top conferences such as NeurIPS, ICLR, CHI, and IJCAI, earning multiple Best Paper Awards. His research focuses on machine learning fairness, privacy preservation, and the societal impact of technology, and has been featured in prominent outlets like Scientific American, WIRED, and VentureBeat.

• 2020–Present·Meta·Senior Research Scientist
  • Responsible for developing and researching large-scale production systems, focusing on large language models (LLMs), neural architecture search (NAS), and predictive analytics.

• 2018–2023·University of Maryland·Ph.D. in Computer Science
  • Specialized in machine learning fairness and privacy preservation.
• 2016–2018·George Washington University·Master’s in Statistics
  • Majored in statistics, focusing on data analysis and model optimization.
• 2012–2016·University of Chicago·Bachelor’s in Mathematics
  • Studied advanced mathematical theories, providing a solid foundation for future research.