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Sarah H. Cen
Researcher at Massachusetts Institute of Technology
Publications - 7
Citations - 249
Sarah H. Cen is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Odometry & Radar. The author has an hindex of 4, co-authored 7 publications receiving 160 citations. Previous affiliations of Sarah H. Cen include University of Oxford.
Papers
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Proceedings ArticleDOI
Precise Ego-Motion Estimation with Millimeter-Wave Radar Under Diverse and Challenging Conditions
Sarah H. Cen,Paul Newman +1 more
TL;DR: This paper presents a reliable and accurate radar-only motion estimation algorithm for mobile autonomous systems, using a frequency-modulated continuous-wave scanning radar to extract landmarks and performs scan matching by greedily adding point correspondences based on unary descriptors and pairwise compatibility scores.
Proceedings ArticleDOI
Radar-only ego-motion estimation in difficult settings via graph matching
Sarah H. Cen,Paul Newman +1 more
TL;DR: In this article, a radar-only odometry pipeline that is highly robust to radar artifacts (e.g., speckle noise and false positives) and requires only one input parameter is proposed.
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Radar-only ego-motion estimation in difficult settings via graph matching
Sarah H. Cen,Paul Newman +1 more
TL;DR: This work proposes a radar-only odometry pipeline that is highly robust to radar artifacts and requires only one input parameter and presents algorithms for key point extraction and data association, framing the latter as a graph matching optimization problem, and provides an in-depth system analysis.
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Regulating algorithmic filtering on social media
Sarah H. Cen,Devavrat Shah +1 more
TL;DR: This work proposes a unifying framework that considers the key stakeholders of AF regulation, and mathematically formalizes this framework, using it to construct a data-driven, statistically sound regulatory procedure that satisfies several important criteria.
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Regret, stability, and fairness in matching markets with bandit learners.
Sarah H. Cen,Devavrat Shah +1 more
TL;DR: In this article, the authors consider the two-sided matching market with bandit learners and prove that it is possible to simultaneously guarantee four desiderata: (1) incentive compatibility, (2) low regret, (3) fairness in the distribution of regret among agents, and (4) high social welfare.