Upcoming Events

Princeton University is actively monitoring the situation around coronavirus (COVID-19) and the evolving guidance from government and health authorities. The latest guidance for Princeton members and visitors is available on the University’s Emergency Management website

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

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

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

Learn about CSML’s graduate certificate program at info session

Wed, Oct 27, 2021, 4:30 pm

Graduate students from all departments are invited to attend an informal information session on the Center for Statistics and Machine Learning’s (CSML) graduate certificate program.

Location: Louis A. Simpson Lawn

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

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

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.


CSML Poster Session Event

Mon, May 2, 2022, 8:00 am
The annual CSML Poster Session event will be held in person or virtually. Watch this space for further details.

Events Archive

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

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....
Location: Equad B205

Quantitative Social Science Colloquium: Measuring Housing Activeness from Multi-source Big Data and Machine Learning

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...
Location: Corwin 127

Optimization Theory on Model-Agnostic Meta-Learning

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

Anjalie Field – Building Language Technologies for Analyzing Online Activism

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

Best Practices in Python Packaging

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.


JARROD MCCLEAN - Google Quantum Artificial Intelligence Lab


Google Quantum Artificial Intelligence Lab

Website Jarrod McClean Website

Thursday, Sep. 30, 2021

Zoom Meeting

Host - Haw Yang

More information and abstract forthcoming.

Location: Virtual Seminar
Tags: Seminars

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

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...

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

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