This webpage is run by Professor Philip Thomas. The views presented on this webpage are those of Prof. Thomas, and may or may not be shared by his co-authors, collaborators, and funding agencies.
Stephen is a doctoral candidate in the Autonomous Learning Laboratory at UMass Amherst, and a co-author of the November 22 paper in Science. He created the interface for the Seldonian classification algorithm in that paper, and the interface for the Seldonian contextual bandit algorithm presented at NeurIPS 2019 [link]. He is currently working with Susmit Jha from the Stanford Reseach Institute (SRI) to create Seldonian algorithms that are robust do distributional shift in general, and particularly demographic shift when ensuring fairness.
Blossom is a doctoral student in the Autonomous Learning Laboratory at UMass Amherst, and a the first author of a NeurIPS 2019 paper that presents a Seldonian contextual bandit algorithm [link]. In this work, Blossom and her co-authors show how a Seldonian contextual bandit algorithm can predict who will repay a loan, who will commit a crime in the future, and how to change an online tutorial to improve overall student performance, all while avoiding various types of unfair behavior.
Yash is a doctoral student in the Autonomous Learning Laboratory at UMass Amherst. Yash's research focuses on understanding the challenges preventing real-world applications of reinforcement learning, and developing princpled algorithms to bridge this gap. He is currently working to create Seldonian reinforcement learning algorithms that provide high-confidence fairness and safety guarantees even in the presence of nonstationarity.
Pinar is a doctoral candidate at UMass Amherst, advised by Professor Brian Levine. Working under Prof. Levine, Pinar's research focuses on computer security. She has shown that Seldonian algorithms, and particularly Seldonian reinforcement learning algorithms, are not robust to attacks. If someone can maliciously change some of the data provided to the algorithm, or if data is unintentionally corrupted, the algorithm's safety and fairness guarantees can be violated significantly. She is currently working on developing Seldonian reinforcement learning algorithms that are secure—algorithms that are robust to accidental data corruption and even malicious attacks.
James is a doctoral student in the Autonomous Learning Laboratory at UMass Amherst. His research focuses on improving generalization in reinforcement learning. He is currently working to create a Seldonian reinforcement learning algorithm that can guarantee with high probability that it will be safe and fair on a new problem based on its experience solving similar, but different, previous problems.
Sarah is a masters student in the Laboratory for Advanced Software Engineering Research at UMass Amherst, advised by Professor Yuriy Brun. In her undergraduate honors thesis, Sarah worked to create a Seldonian contextual bandit algorithm that she deployed in a user study to automatically improve online tutorials (intelligent tutoring systems), while ensuring that the algorithm does not change the tutorials in a way that benefits the majority of the students at the expense of a protexted minority. She is a co-author of the paper presenting this work that will appear at NeurIPS 2019 in December [link]. She continues to work in the area of ensuring fairness.
In this collaborative project between Professors Shannon Roberts and Philip Thomas, we study how Seldonian reinforcement learning algorithms can automatically improve automated warning systems for autonomous vehicles. Human trials are currently underway (UMass IRB Protocol Number 1191).