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…

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…

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…

Please register here to attend in person.
 

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

Abstract: 

With the slowing of Moore's law, computer architects have turned to domain-specific hardware accelerators to improve the performance and efficiency of computing systems. However, programming these systems entails significant modifications to the software stack to properly leverage the specialized…

Abstract:

Recent advances in machine learning such as deep learning have led to powerful tools for modeling complex data with high predictive accuracy.

However, the resulting models are typically black box, limiting their usefulness in scientific discovery. Here we show that an "interpretable-by-design''…

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…

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.

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.

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.