Aarti Singh
Professor | Carnegie Mellon University
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Aarti Singh researches machine learning, statistics and signal processing, focusing on algorithms for learning and decision making.
Biography
Aarti Singh received her Ph.D. degree in Electrical and Computer Engineering from the University of Wisconsin-Madison in 2008. She was a Postdoctoral Research Associate at the Program in Applied and Computational Mathematics at Princeton University from 2008-2009, before joining the School of Computer Science at Carnegie Mellon.
Her research lies at the intersection of machine learning, statistics and signal processing, and focuses on designing principled interactive algorithms for learning and decision making.
Her work is recognized by an NSF Career Award, a United States Air Force Young Investigator Award, A. Nico Habermann Faculty Chair Award, Harold A. Peterson Best Dissertation Award, and multiple paper awards. She has served on the National Academy of Sciences (NAS) committee on Applied and Theoretical Statistics, World Economic Forum expert network, lead expert on multiple NAS and ONR/NIST study committees, Program Chair for the International Conference on Machine Learning (ICML) 2020 and Artificial Intelligence and Statistics (AISTATS) 2017 conference, Associate Editor for IEEE Transactions on Information Theory, and Action Editor for Journal of Machine Learning Research.
Areas of Expertise (7)
Digital Twins
Human Factors in Decision Making
Autonomous Decision Making
Machine Learning
Signal Processing
Decision Making
Deep Learning
Media Appearances (4)
Carnegie Mellon University receives $20M funding to establish AI institute
Coingeek online
2023-05-26
“We need to develop AI technology that works for the people,” said Aarti Singh, a Machine Learning professor tapped to be the institute’s first director. “It’s actually built on data that is vetted, algorithms that are vetted, with feedback from all the stakeholders and participatory design.”
Carnegie Mellon artificial intelligence institute to receive $20M in federal funding
TribLIVE.com online
2023-05-11
Aarti Singh, a professor in the Machine Learning Department of CMU’s School of Computer Science, will serve as the institute’s director. She said it’s important to “have social scientists and AI researchers collaborate to come up with solutions that will leverage AI capability while ensuring social acceptance.”
CMU won $20M to create a new institute focused on ‘human-centric’ AI solutions
Technical.ly online
2023-05-09
The new institute’s co-director, Aarti Singh, told Technical.ly the institute is going to bring social sciences and tech research together to figure out how humans and the technology can better interact. “For [the] maximal impact of these technologies, we need to have social scientists and AI researchers collaborate to come up with solutions that will leverage AI capability while ensuring social acceptance,” Singh said.
Carnegie Mellon leads NSF AI Institute for Societal Decision Making
EurekAlert! online
2023-05-04
"The best applications of artificial intelligence in societal domains will come when we not only advance AI for decision-making, but also better understand human decision-making, and when we can bring the two together," said Aarti Singh, a professor in the Machine Learning Department of CMU's School of Computer Science, who will serve as the institute's director.
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Education (3)
University of Wisconsin, Madison: Ph.D., Electrical Engineering
University of Wisconsin, Madison: M.S., Electrical Engineering
University of Delhi, India: B.E., Electronics & Communication Engineering
Links (4)
Articles (5)
Two-Sample Testing on Ranked Preference Data and the Role of Modeling Assumptions
Journal of Machine Learning Research2022 A number of applications require two-sample testing on ranked preference data. For instance, in crowdsourcing, there is a long-standing question of whether pairwise-comparison data provided by people is distributed identically to ratings-converted-to-comparisons. Other applications include sports data analysis and peer grading. In this paper, we design two-sample tests for pairwise-comparison data and ranking data. For our two-sample test for pairwise-comparison data, we establish an upper bound on the sample complexity required to correctly test whether the distributions of the two sets of samples are identical.
Local signal adaptivity: Provable feature learning in neural networks beyond kernels
Advances in Neural Information Processing Systems2021 Neural networks have been shown to outperform kernel methods in practice (including neural tangent kernels). Most theoretical explanations of this performance gap focus on learning a complex hypothesis class; in some cases, it is unclear whether this hypothesis class captures realistic data. In this work, we propose a related, but alternative, explanation for this performance gap in the image classification setting, based on finding a sparse signal in the presence of noise. Specifically, we prove that, for a simple data distribution with sparse signal amidst high-variance noise, a simple convolutional neural network trained using stochastic gradient descent learns to threshold out the noise and find the signal.
Prior and Prejudice: The Novice Reviewers' Bias against Resubmissions in Conference Peer Review
Proceedings of the ACM on Human-Computer Interaction2021 Modern machine learning and computer science conferences are experiencing a surge in the number of submissions that challenges the quality of peer review as the number of competent reviewers is growing at a much slower rate. To curb this trend and reduce the burden on reviewers, several conferences have started encouraging or even requiring authors to declare the previous submission history of their papers. Such initiatives have been met with skepticism among authors, who raise the concern about a potential bias in reviewers' recommendations induced by this information.
Best Arm Identification under Additive Transfer Bandits
55th Asilomar Conference on Signals, Systems, and Computers2021 We consider a variant of the best arm identification (BAI) problem in multi-armed bandits (MAB) in which there are two sets of arms (source and target), and the objective is to determine the best target arm while only pulling source arms. In this paper, we study the setting when, despite the means being unknown, there is a known additive relationship between the source and target MAB instances. We show how our framework covers a range of previously studied pure exploration problems and additionally captures new problems. We propose and theoretically analyze an LUCB-style algorithm to identify an e-optimal target arm with high probability.
PeerReview4All: fair and accurate reviewer assignment in peer review
Journal of Machine Learning Research2021 We consider the problem of automated assignment of papers to reviewers in conference peer review, with a focus on fairness and statistical accuracy. Our fairness objective is to maximize the review quality of the most disadvantaged paper, in contrast to the commonly used objective of maximizing the total quality over all papers. We design an assignment algorithm based on an incremental max-ow procedure that we prove is near-optimally fair. Our statistical accuracy objective is to ensure correct recovery of the papers that should be accepted. We provide a sharp minimax analysis of the accuracy of the peer-review process for a popular objective-score model as well as for a novel subjective-score model that we propose in the paper.
