Hi! I'm Serena.
I'm an incoming CS Prof at Brown University.
I was a AAAS AI Policy Fellow in the U.S. Senate.

The Perils of Trial-and-Error Reward Design: Misdesign through Overfitting and Invalid Task Specifications

Trial-and-error reward design is unsanctioned, but the implications of this widespread practice have not been studied. We conduct empirical computational and user study experiments, and we find that trial and error leads to the design of reward functions which are overfit and otherwise misdesigned. Even in a trivial setting, we find that reward function misdesign is rampant. Published at AAAI Conference on Artificial Intelligence, 2023.

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Learning Optimal Advantage from Preferences and Mistaking it for Reward

Typical reinforcement learning from human preferences (RLHF) approaches assume that human preferences arise only from trajectory segments' sums of reward. In past work, we showed that regret is a better model of human preferences (published at TMLR 2024). In this work, we consider the consequences if preferences arise instead from this better-supported regret preference model. Published at AAAI Conference on Artificial Intelligence, 2024.

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Bayes-TrEx: A Bayesian Sampling Approach to Model Transparency by Example

Looking at expressive examples can help us better understand neural network behaviors and design better models. Bayes-TrEx is a tool to find these expressive examples. Published at AAAI Conference on Artificial Intelligence, 2021.

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RoCUS: Robot Controller Understanding via Sampling (An Extension to Bayes-TrEx)

We sample representative robot behaviors. We show how exposing these representative behaviors can help with the revision of a dynamical system robot controller's specifications. Published at Conference on Robot Learning (CoRL), 2021.

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Do Feature Attribution Methods Work?

We design a principled evaluation mechanism for assessing priority attribution methods, and contribute to the growing body of literature suggesting these methods cannot be trusted in the wild. Published at AAAI Conference on Artificial Intelligence, 2022.

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Communicating Logical Statements Effectively

How should we best present logical sentences to a human? I study whether different logical forms are easier or harder for people to parse. I find that people are more resilient than anticipated! Published at International Joint Conference on AI (IJCAI), 2019.

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Revisiting Human-Robot Teaching and Learning Through the Lens of Human Concept Learning Theory

We look at how cognitive theories of human concept learning should inform human-robot interaction interfaces, especially for teaching and learning tasks. Published at ACM/IEEE International Conference on Human-Robot Interaction (HRI), 2022.

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Varying How We Teach: Adding Contrast Helps Humans Learn about Robot Motions

In this preliminary work, we test the consequences of applying some of the insights from the Variation Theory of Learning to assist humans in learning about robot motions. Published at HRI Workshop on Human-Interactive Robot Learning, 2023.

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Machine Learning Practice Outside Big Tech: Resource Constraints Challenge Responsible Development

We interviewed industry practitioners from startups, government, and non-tech companies about their use of machine learning in developing products. We analyze these interviews with thematic analysis. Published at AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AIES), 2021.

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Piggybacking Robots: Human-robot Overtrust in University Dormitory Security

My award-winning undergraduate senior thesis, a project which set out to answer the question of whether we place too much trust in robotic systems, specifically in the physical security domain. Published at ACM/IEEE International Conference on Human-Robot Interaction (HRI), 2017.

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PhD Comics

Science Policy

In 2021-2022, I served as President and in 2020-2021 as Vice President of MIT's Science Policy Initiative. I advocate for using science to inform policy, and for using policy to make science just and equitable. Pictured above with colleague Willie Boag. Not an endorsement for Senator Alexander.