Upcoming Events

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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.

Location: Virtual Seminar
Speaker(s):

Best Practices in Python Packaging

Fri, Oct 1, 2021, 9:00 am

This workshop is for researchers who are already using Python for their work, but who want to distribute their software to the broader scientific community by packaging their code for other researchers to easily use.

Speaker(s):

CITP Seminar: 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...
Location: TBA
Speaker(s):

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...
Location: Zoom
Speaker(s):

CITP Seminar: 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.

Location: Virtual Seminar
Speaker(s):

Machine Predictions and Synthetic Text: A Roundtable

Tue, Oct 26, 2021, 4:30 pm to 6:00 pm
Since it was published in March 2021, "On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?" has sparked impassioned conversations on the unintended consequences and potential harms of prominent natural language processing (NLP) projects. While this groundbreaking paper has been influential in computer and data science—prompting...
Location: Zoom Webinar
Speaker(s):

Machine Learning for Your Research

Wed, Oct 27, 2021, 4:30 pm

This workshop will give an overview of several modern supervised and unsupervised machine learning methods. We will discuss the advantages and limitations of each and explore what types of problems each is best suited to address.

Workshop format: Lecture and discussion

Location: Lewis Library 120
Speaker(s):

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....
Location: Virtual Seminar
Speaker(s):

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...
Location: TBA
Speaker(s):

Scientific Visualization

Tue, Nov 16, 2021, 2:00 pm

Visualization enables insight, allows verification, and enhances presentations and publications. This workshop introduces the VisIt visualization software package, which has a graphical user interface for exploring and displaying data.  It can also produce animation to represent complex behavior of variables over time.

Speaker(s):

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Events Archive

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

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...
Location: Virtual Seminar
Speaker(s):

Robotics Seminar - Contact-Rich robotics: Learning, Impact-Invariant Control, and Tactile Feedback

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...
Speaker(s):

Nonconvex first-order optimization: When can gradient descent escape saddle points in linear time?

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...
Location: Online
Speaker(s):

Introduction to Data Analysis using Python

This workshop will get students started in data analysis using the pandas Python package. It will briefly cover different components of data analysis and connect them with the goal of extracting meaning from data. We will go over an example to illustrate the data analysis process from beginning to end.

Workshop format:...

Location: Firestone A-6-F
Speaker(s):

CITP Seminar: Olga Russakovsky – Fairness in Visual Recognition

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. 
Location: Virtual Seminar
Speaker(s):

Bilevel Learning for Inverse Problems

Variational regularization techniques are dominant in the field of inverse problems. A drawback of these techniques is that they are dependent on a number of parameters which have to be set by the user. This issue can be approached by machine learning where we estimate these parameters from data. This is known as "Bilevel Learning" and has been...
Location: https://zoom.us/j/91342019907
Speaker(s):

Variational models and gradient flows for graph clustering

Discrete graph-based variants of the Allen--Cahn and total variation variational models have proven to be successful tools for clustering and classification on graphs. In this talk we will study these models and the gradient flows that are derived from them. We will see deep connections between the various discrete gradient flows as well as...
Speaker(s):

Real-Time Remote Sensing and Fusion Plasma Control: A Reservoir Computing Approach

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...
Location: Virtual
Speaker(s):

Convergence of Stochastic Gradient Descent for analytic target functions

In this talk we discuss almost sure convergence of Stochastic Gradient Descent in discrete and continuous time for a given twice continuously-differentiable target function F. In a first step we give assumptions on the step-sizes and perturbation size to ensure convergence of the target value F and gradient f=DF assuming that f is locally Hölder-...
Speaker(s):

Accelerate Your Code at the Princeton GPU Hackathon, June 2, 8-10, 2021

Graphics Processing Units (GPUs) offer high performance and massive parallelization, but learning how to program GPUs for scientific applications can be daunting.

Location: Virtual Seminarl

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