Upcoming Seminars

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…


Previous Seminars

Thematic Day on the Mean Field Training of Deep Neural Networks
Sat, Jul 25, 2020, 12:00 pm

12pm: Roberto I. Oliveira – TBA 

1pm: Konstantinos Spiliopoulos  - Mean field limits of neural networks: typical behavior and fluctuations

2pm: Huy Tuan Pham - A general framework for the mean field limit of multilayer neural networks

Managing Research Data
Thu, Jul 23, 2020, 12:00 pm

This webinar will go over tips on how to keep track of your data files more efficiently, better organize your data files, and how to manage your data, code and other research materials, to save yourself headaches down the road.

This event is part of the 

On the foundations of computational mathematics, Smale’s 18th problem and the potential limits of AI
Wed, Jul 15, 2020, 12:00 pm

There is a profound optimism on the impact of deep learning (DL) and AI in the sciences with Geoffrey Hinton concluding that 'They should stop educating radiologists now'. However, DL has an Achilles heel: it is universally unstable so that small changes in the initial data can lead to large errors in the final result. This has been documented…


Trainability and accuracy of artificial neural networks
Wed, Jul 8, 2020, 12:00 pm

The methods and models of machine learning (ML) are rapidly becoming de facto tools for the analysis and interpretation of large data sets. Complex classification tasks such as speech and image recognition, automatic translation, decision making, etc. that were out of reach a decade ago are now routinely performed by computers with a high…


Towards a mathematical understanding of supervised learning: What we know and what we don't know
Wed, Jul 1, 2020, 12:00 pm

Two of the biggest puzzles in machine learning are: Why is it so successful and why is it quite fragile? This talk will present a framework for unraveling these puzzles from the perspective of approximating functions in high dimensions. We will discuss what's known and what's not known about the approximation generalization properties of neural…


Reimagining Digitized Newspapers with Machine Learning
Fri, May 15, 2020, 11:30 am

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…


Running and Analyzing Large-scale Psychology Experiments
Fri, Mar 6, 2020, 12:00 pm

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. In this talk, I will explore some of the consequences of being able to conduct psychological research…


Preference Modeling with Context-Dependent Salient Features
Mon, Feb 24, 2020, 4:00 pm

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. Formalizing this idea, I will introduce our…


Massively Parallel Evolutionary Computation for Empowering Electoral Reform
Fri, Feb 21, 2020, 12:15 pm

Important insights into redistricting can be gained by formulating and analyzing the problem with a Markov Chain Monte Carlo framework that utilizes optimization heuristics to inform transition proposals.


Wearable Brain-Machine Interface Architectures for Neurocognitive Stress
Mon, Feb 10, 2020, 4:30 pm

The human body responds to neurocognitive stress in multiple ways through its autonomic nervous system. Changes in skin conductance measurements indicate sudomotor nerve activity, and could be used in inferring the underlying autonomic nervous system stimulation. We model skin conductance measurements using a state-space model with sparse impulsive events as inputs to the model.