Seminars

Upcoming Seminars

Pranay Anchuri – Insights into Predictability of Life Outcomes: A Data-Driven Approach

Tue, Oct 26, 2021, 12:30 pm to 1:30 pm

Predicting life outcomes is a challenging task even for advanced machine learning (ML) algorithms. At the same time, accurately predicting these outcomes has important implications in providing targeted assistance and in improving policy making.

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CITP Seminar: Alex Hanna – Beyond Bias: Algorithmic Unfairness, Infrastructure, and Genealogies of Data

Tue, Nov 9, 2021, 12:30 pm
Problems of algorithmic bias are often framed in terms of lack of representative data or formal fairness optimization constraints to be applied to automated decision-making systems. However, these discussions sidestep deeper issues with data used in AI, including problematic categorizations and the extractive logics of crowdwork and data mining....
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CITP Seminar: Matt Weinberg

Tue, Nov 16, 2021, 12:30 pm
Matt is an assistant professor at Princeton University in the Department of Computer Science. His primary research interest is in Algorithmic Mechanism Design: algorithm design in settings where users have their own incentives. He is also interested more broadly in Algorithmic Game Theory, Algorithms Under Uncertainty, and Theoretical Computer...
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Previous Seminars

Bridging the Gap Between AI and Clinical Neuroscience via Deep-Generative Fusion Models

Thu, Oct 14, 2021, 4:30 pm
Deep learning has disrupted nearly every major field of study from computer vision to genomics. The unparalleled success of these models has, in many cases, been fueled by an explosion of data. Millions of labeled images, thousands of annotated ICU admissions, and hundreds of hours of transcribed speech are common standards in the literature....
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Quantitative Social Science Colloquium: Measuring Housing Activeness from Multi-source Big Data and Machine Learning

Fri, Oct 8, 2021, 11:30 am
Measuring timely high-resolution socioeconomic outcomes is critical for policy-making and evaluation, but hard to reliably obtain. With the help of machine learning and cheaply available data such as social media and nightlight, it is now possible to predict such indices in fine granularity. This paper demonstrates an adaptive way to measure the...
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Optimization Theory on Model-Agnostic Meta-Learning

Wed, Oct 6, 2021, 4:30 pm to 6:00 pm
Meta-learning or learning to learn has been shown to be a powerful tool for fast learning over unseen tasks by efficiently extracting the knowledge from a range of observed tasks. Such empirical success thus highly motivates theoretical understanding of the performance guarantee of meta-learning, which will serve to guide the better design of meta...
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Anjalie Field – Building Language Technologies for Analyzing Online Activism

Tue, Oct 5, 2021, 12:30 pm
While recent advances in natural language processing (NLP) have greatly enhanced our ability to analyze online text, distilling broad social-oriented research questions into tasks concrete enough for NLP models remains challenging. In this work, we develop state-of-the-art NLP models grounded in frameworks from social theory in order to analyze...
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JARROD MCCLEAN - Google Quantum Artificial Intelligence Lab

Thu, Sep 30, 2021, 4:00 pm
JARROD MCCLEAN

Google Quantum Artificial Intelligence Lab

Website Jarrod McClean Website

Thursday, Sep. 30, 2021
4:00pm

Zoom Meeting

Host - Haw Yang

More information and abstract forthcoming.

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CITP Seminar: Elizabeth Anne Watkins – Introducing Dialogues in AI and Work: Three Works-in-Progress and a Call to Action

Tue, Sep 21, 2021, 12:30 pm
The Princeton Dialogues in AI and Work is a research agenda investigating what algorithmic and predictive data-driven tools mean to stakeholders across society. Building on prior work in the Dialogues in AI and Ethics case study series, the current phase of research takes an empirical, sociotechnical focus on how the different communities will...
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Robotics Seminar - Contact-Rich robotics: Learning, Impact-Invariant Control, and Tactile Feedback

Fri, Sep 17, 2021, 3:00 pm
Whether operating in a manufacturing plant or assisting within the home, many robotic tasks require safe and controlled interaction with a complex and changing world. However, state-of-the-art approaches to both learning and control are most effective when this interaction either occurs in highly structured settings or at slow speeds unsuitable...
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Nonconvex first-order optimization: When can gradient descent escape saddle points in linear time?

Wed, Sep 15, 2021, 4:30 pm
Many data-driven problems in the modern world involve solving nonconvex optimization problems. The large-scale nature of many of these problems also necessitates the use of first-order optimization methods, i.e., methods that rely only on the gradient information, for computational purposes. But the first-order optimization methods, which include...
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CITP Seminar: Olga Russakovsky – Fairness in Visual Recognition

Tue, Sep 14, 2021, 12:30 pm
Computer vision models trained on unparalleled amounts of data have revolutionized many applications. However, more and more historical societal biases are making their way into these seemingly innocuous systems. 
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Real-Time Remote Sensing and Fusion Plasma Control: A Reservoir Computing Approach

Thu, Jun 17, 2021, 11:00 am
Nuclear fusion power is a potential source of safe, non-carbon-emitting and virtually limitless energy. The tokamak is a promising approach to fusion based on magnetic plasma confinement, constituting a complex physical system with many control challenges. However, plasma instabilities pose an existential threat to a reactor, which has not yet...
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