Upcoming Events

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CITP Distinguished Lecture Series: Jon Kleinberg – The Challenge of Understanding What Users Want: Inconsistent Preferences and Engagement Optimization
Wed, Feb 15, 2023, 4:30 pm

Please register here to attend in person.
 

Co-sponsored by the Department of Computer Science and the Department of Electrical and Computer Engineering

Location
Friend Center Convocation Room

Speaker

CITP Distinguished Lecture Series: Thomas Ristenpart – Mitigating Technology Abuse in Intimate Partner Violence and Encrypted Messaging
Wed, Feb 22, 2023, 4:30 pm

Please register here to attend in person.

Co-sponsored by the Department of Computer Science and the Department of Electrical and Computer Engineering

Computer security is traditionally about the protection of technology, whereas trust and safety…

Location
Friend Center Convocation Room

Speaker

CITP Distinguished Lecture Series: Alessandro Acquisti
Wed, Mar 1, 2023, 4:30 pm

Co-sponsored by the Department of Computer Science and the Department of Electrical and Computer Engineering

Details: TBA

 

Bio:

Alessandro Acquisti is the Trustees Professor of Information Technology and Public Policy at the Heinz College, Carnegie Mellon University…

Location
Friend Center Convocation Room

Speaker

CITP Distinguished Lecture Series: Ed Felten – Scaling Arbitrum, from Lab to Product
Wed, Mar 8, 2023, 4:30 pm

Co-sponsored by the Department of Computer Science and the Department of Electrical and Computer Engineering

The Arbitrum blockchain protocol started as a Princeton University research project, and has grown into a robust community hosting hundred of applications and over 600,000 monthly users. Along the way, the system has…

Location
105 Computer Science

Speaker

CITP Distinguished Lecture Series: Lorrie Cranor – Designing Usable and Useful Privacy Choice Interfaces
Thu, Mar 30, 2023, 4:30 pm

Co-sponsored by the Department of Computer Science and the Department of Electrical and Computer Engineering

Users who wish to exercise privacy rights or make privacy choices must often rely on website or app user interfaces. However, too often, these user interfaces suffer from usability deficiencies ranging from being…

Location
105 Computer Science

Speaker

JOINT PACM / CSML Colloquium, Leon Bottou, Facebook AI Research
Mon, Apr 17, 2023, 4:30 pm

Title: TBA

Abstract: TBA

Bio: TBA

Location
214 Fine Hall

Speaker

Princeton Research Day
Thu, May 11, 2023

Princeton Research Day celebrates the research and creative endeavors of the campus-wide community. The event serves as an opportunity for researchers and creators to reach across disciplines by communicating in non-specialist language about their research or creative work.

Now in its eighth consecutive year, the event highlights work…

Events Archive

Bridging the Gap Between Your Laptop and Cloud Computing

Part 1:  Introduction to some tools that computer programmers typically use to write and debug code in an
 effective manner.

Part 2:  Introduction to cloud computing (create and manage cloud computing resources) How to use some tools that offer the possibility of writing code locally while seamlessly executing/running it on powerful cloud computing.

Bridging the Gap Between Your Laptop and Cloud Computing

Part 1:  Introduction to some tools that computer programmers typically use to write and debug code in an
 effective manner.

Part 2:  Introduction to cloud computing (create and manage cloud computing resources) How to use some tools that offer the possibility of writing code locally while seamlessly executing/running it on powerful cloud computing.

What is Machine Learning, and Can It Aid My Research

The Center for Statistics and Machine Learning (CSML) is offering a three-hour Wintersession workshop, which aims to increase awareness of how machine learning could aid faculty, postdoc, and student research.

No detailed prior knowledge of machine learning is assumed. The workshop will begin with an overview of crucial…

Speakers

Introduction to Machine Learning (5 Day Mini-Course)

This mini-course will provide a comprehensive introduction to machine learning. Part 1 will briefly overview the full machine learning process and cover introductory concepts such as what is machine learning and why is it used. Popular software libraries will be discussed. Attendees will begin working hands-on in Part 2 to train simple machine learning models. Part 3 covers model evaluation and refinement. Artificial neural networks are introduced during Part 4. The mini-course concludes with a hackathon during Part 5 where participants will work on a small, end-to-end machine learning project chosen from one of multiple domains.

Location
Lewis Library 138

Speakers

Addressing Challenging and Rewarding Problems in Health and Wellbeing while Developing Novel Computer Vision and Machine Learning

In this talk I will first describe our work on developing new tools for screening and intervention in developmental disorders, autism spectrum disorder and eating disorders in particular. I will show how equipped with computer vision and machine learning, we deployed scalable, phone/tablet-based tools in pediatric clinics and homes in the US and Africa.

Location
B205 Engineering Quadrangle

Speaker

Machine Learning and the Future of Philology

What will philology become in the wake of the digital revolution? How can computer vision, handwritten text recognition, natural language processing, deep neural networks and/or other forms of machine learning refine the arsenal of techniques for studying premodern evidence?

This works-in-progress symposium will feature six teams of Princeton scholars who are applying machine learning to manuscripts, rare books, archives, inscriptions, coins and other pre-1600 texts. Presentations will include projects on materials in Syriac, Hebrew, Latin, Greek, Chinese and English. 

Location
Firestone Library, Floor B
Data Science for the Humanities and Social Sciences

Are you curious about how machine learning can be used to study fragments of medieval Egyptian letters? Or how quantitative methods can help trace the monetization of misinformation on the web? Intrigued by AI but don’t know what it is? Not sure how to work with your complex collection of texts, images, and other media? Want to learn coding but don’t know where to start? 

Robustness for Models and Algorithms in Machine Learning

Risk-averse optimization plays a major role in the design of safety for machine learning applications. In this talk, we will present a set of tools to enhance the robustness of models and algorithms to potentially harmful data shifts.

Lunch from 12:15 p.m., RSVP to [email protected]

Location
26 Prospect Ave. Classroom 103

Speaker

Robust and Risk-Averse Accelerated Gradient Methods

In the context of first-order algorithms subject to random gradient noise, we study the trade-offs between the convergence rate (which quantifies how fast the initial conditions are forgotten) and the "risk" of suboptimality, i.e., deviations from the expected suboptimality.

Lunch from 12:15 p.m., RSVP to [email protected]

Location
26 Prospect Ave. Classroom 103
Sparse Estimation: Closing the Gap Between L0 and L1 Models

Sparse statistical estimators are increasingly prevalent due to their ease of interpretability and superior out-of-sample performance. However, sparse estimation problems with an L0 constraint, restricting the support of the estimators, are challenging (typically NP-hard, but not always) non-convex optimization problems.

Location
101 Sherrerd Hall