Serena Booth
Robotics Research Scientist - MIT
Research Interests
About
Publications
Piggybacking Robots
Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction / Mar 06, 2017
Booth, S., Tompkin, J., Pfister, H., Waldo, J., Gajos, K., & Nagpal, R. (2017). Piggybacking Robots. Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction. https://doi.org/10.1145/2909824.3020211
Do Feature Attribution Methods Correctly Attribute Features?
Proceedings of the AAAI Conference on Artificial Intelligence / Jun 28, 2022
Zhou, Y., Booth, S., Ribeiro, M. T., & Shah, J. (2022). Do Feature Attribution Methods Correctly Attribute Features? Proceedings of the AAAI Conference on Artificial Intelligence, 36(9), 9623–9633. https://doi.org/10.1609/aaai.v36i9.21196
IEEE P7001: A Proposed Standard on Transparency
Frontiers in Robotics and AI / Jul 26, 2021
Winfield, A. F. T., Booth, S., Dennis, L. A., Egawa, T., Hastie, H., Jacobs, N., Muttram, R. I., Olszewska, J. I., Rajabiyazdi, F., Theodorou, A., Underwood, M. A., Wortham, R. H., & Watson, E. (2021). IEEE P7001: A Proposed Standard on Transparency. Frontiers in Robotics and AI, 8. https://doi.org/10.3389/frobt.2021.665729
Virtual, Augmented, and Mixed Reality for Human-Robot Interaction (VAM-HRI)
Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction / Mar 23, 2020
Williams, T., Szafir, D., Chakraborti, T., Soh Khim, O., Rosen, E., Booth, S., & Groechel, T. (2020). Virtual, Augmented, and Mixed Reality for Human-Robot Interaction (VAM-HRI). Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction. https://doi.org/10.1145/3371382.3374850
Bayes-TrEx: a Bayesian Sampling Approach to Model Transparency by Example
Proceedings of the AAAI Conference on Artificial Intelligence / May 18, 2021
Booth, S., Zhou, Y., Shah, A., & Shah, J. (2021). Bayes-TrEx: a Bayesian Sampling Approach to Model Transparency by Example. Proceedings of the AAAI Conference on Artificial Intelligence, 35(13), 11423–11432. https://doi.org/10.1609/aaai.v35i13.17361
Machine Learning Practices Outside Big Tech: How Resource Constraints Challenge Responsible Development
Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society / Jul 21, 2021
Hopkins, A., & Booth, S. (2021). Machine Learning Practices Outside Big Tech: How Resource Constraints Challenge Responsible Development. Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society. https://doi.org/10.1145/3461702.3462527
Evaluating the Interpretability of the Knowledge Compilation Map: Communicating Logical Statements Effectively
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence / Aug 01, 2019
Booth, S., Muise, C., & Shah, J. (2019). Evaluating the Interpretability of the Knowledge Compilation Map: Communicating Logical Statements Effectively. Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence. https://doi.org/10.24963/ijcai.2019/804
The Irrationality of Neural Rationale Models
Proceedings of the 2nd Workshop on Trustworthy Natural Language Processing (TrustNLP 2022) / Jan 01, 2022
Zheng, Y., Booth, S., Shah, J., & Zhou, Y. (2022). The Irrationality of Neural Rationale Models. Proceedings of the 2nd Workshop on Trustworthy Natural Language Processing (TrustNLP 2022). https://doi.org/10.18653/v1/2022.trustnlp-1.6
Demonstration of the EMPATHIC Framework for Task Learning from Implicit Human Feedback
Proceedings of the AAAI Conference on Artificial Intelligence / May 18, 2021
Cui, Y., Zhang, Q., Jain, S., Allievi, A., Stone, P., Niekum, S., & Knox, W. B. (2021). Demonstration of the EMPATHIC Framework for Task Learning from Implicit Human Feedback. Proceedings of the AAAI Conference on Artificial Intelligence, 35(18), 16017–16019. https://doi.org/10.1609/aaai.v35i18.17998
Efficient Robot Motion Planning via Sampling and Optimization
2021 American Control Conference (ACC) / May 25, 2021
Leu, J., Zhang, G., Sun, L., & Tomizuka, M. (2021). Efficient Robot Motion Planning via Sampling and Optimization. 2021 American Control Conference (ACC). https://doi.org/10.23919/acc50511.2021.9483146
Revisiting Human-Robot Teaching and Learning Through the Lens of Human Concept Learning
2022 17th ACM/IEEE International Conference on Human-Robot Interaction (HRI) / Mar 07, 2022
Booth, S., Sharma, S., Chung, S., Shah, J., & Glassman, E. L. (2022). Revisiting Human-Robot Teaching and Learning Through the Lens of Human Concept Learning. 2022 17th ACM/IEEE International Conference on Human-Robot Interaction (HRI). https://doi.org/10.1109/hri53351.2022.9889398
Reward (Mis)design for autonomous driving
Artificial Intelligence / Mar 01, 2023
Knox, W. B., Allievi, A., Banzhaf, H., Schmitt, F., & Stone, P. (2023). Reward (Mis)design for autonomous driving. Artificial Intelligence, 316, 103829. https://doi.org/10.1016/j.artint.2022.103829
Reward (Mis)design for autonomous driving
Artificial Intelligence / Mar 01, 2023
Knox, W. B., Allievi, A., Banzhaf, H., Schmitt, F., & Stone, P. (2023). Reward (Mis)design for autonomous driving. Artificial Intelligence, 316, 103829. https://doi.org/10.1016/j.artint.2022.103829
Explainability of Intelligent Transportation Systems using Knowledge Compilation: a Traffic Light Controller Case
2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) / Sep 20, 2020
Wollenstein-Betech, S., Muise, C., Cassandras, C. G., Paschalidis, I. Ch., & Khazaeni, Y. (2020). Explainability of Intelligent Transportation Systems using Knowledge Compilation: a Traffic Light Controller Case. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC). https://doi.org/10.1109/itsc45102.2020.9294213
Build confidence and acceptance of AI-based decision support systems - Explainable and liable AI
2020 13th International Conference on Human System Interaction (HSI) / Jun 01, 2020
Nicodeme, C. (2020). Build confidence and acceptance of AI-based decision support systems - Explainable and liable AI. 2020 13th International Conference on Human System Interaction (HSI). https://doi.org/10.1109/hsi49210.2020.9142668
FINDING VALUE WHERE NONE EXISTS: PITFALLS IN USING ADJUSTED PRESENT VALUE
Journal of Applied Corporate Finance / Mar 01, 2002
Booth, L. (2002). FINDING VALUE WHERE NONE EXISTS: PITFALLS IN USING ADJUSTED PRESENT VALUE. Journal of Applied Corporate Finance, 15(1), 95–104. https://doi.org/10.1111/j.1745-6622.2002.tb00344.x
Fly motion vision: from optic flow to visual course control
e-Neuroforum / Sep 01, 2012
Borst, A. (2012). Fly motion vision: from optic flow to visual course control. E-Neuroforum, 18(3), 59–66. https://doi.org/10.1007/s13295-012-0031-z
Education
Massachusetts Institute of Technology
PhD, Robotics / 2024 (anticipated)
Harvard University
BS, Computer Science / 2016
Experience
MIT
Research Scientist / 2018 — Present
Links & Social Media
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