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

DataX Workshop Series: Synthetic Control Methods | Day 1

Fri, Sep 11, 2020, 2:00 pm

Synthetic controls are widely applied to estimate the effects of policy interventions and other treatments of interests.

DataX Workshop Series: Synthetic Control Methods | Day 2

Sat, Sep 12, 2020, 8:00 am

With the support of a major gift from Eric Schmidt ’76, his wife Wendy, and the Schmidt Futures Foundation the Center for Statistics and Machine Learning (CSML) is pleased to sponsor the Data Science at Princeton Workshop Series.  These innovative and collaborative workshops in data science and machine learning are aimed at accelerating...

Events Archive

Reimagining Digitized Newspapers with Machine Learning

The 16 million digitized historic newspaper pages within Chronicling America, a joint initiative by the Library of Congress and the NEH, represent an incredibly rich resource for a wide range of users. Historians, journalists, genealogists, students, and members of the American public explore...

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2020 CSML Virtual Undergraduate Poster Session

Independent work posters and papers are due on May 12th at 12pm EST. Please check your email for details

Check out an article on 2019's poster session here.

Princeton Research Day

To keep everyone safe and support social distancing, Princeton Research Day will not be held in person this year. We are exploring other possibilities with input from the campus community, and expect to announce those decisions by early April.

Dinner with a Professor

More information forthcoming on this event.

For an article on the previous year's dinner, read here.

Location: Prospect House

FPGA Training with Intel

Research Computing recently installed four Intel FPGAs on the Della cluster. After attending this workshop you should have the skills needed to start using these devices.
Location: 120 Lewis Science Library
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NVIDIA Workshop: Fundamentals of Deep Learning for Computer Vision

Learn deep learning techniques for a range of computer vision tasks, including training and deploying neural networks.
Location: 120 Lewis Science Library

Running and Analyzing Large-scale Psychology Experiments

Psychology has traditionally been a laboratory discipline, focused on small-scale experiments conducted in person. However, recent technological innovations have made it possible to collect far more data from far more people than ever before.

Location: 399 Julis Romo Rabinowitz
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Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control

We introduce Symplectic ODE-Net (SymODEN), a deep learning framework which can infer the dynamics of a physical system, given by an ordinary differential equation (ODE), from observed state trajectories.
Location: CSML Classroom 103
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Foundations of Deep Learning with PyTorch

Of the many deep learning frameworks, PyTorch has largely emerged as the first choice for researchers. This workshop will show participants how to implement and train common network architectures in PyTorch. Special topics will be included as time permits. Participants should have some knowledge of Python, NumPy and deep learning theory.

Location: 138 Lewis Science Library
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Preference Modeling with Context-Dependent Salient Features

This talk considers the preference modeling problem and addresses the fact that pairwise comparison data often reflects irrational choice, e.g. intransitivity. Our key observation is that two items compared in isolation from other items may be compared based on only a salient subset of features.

Location: 214 Fine Hall
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