Seminars

Upcoming Seminars

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…

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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…

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…

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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…

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Previous Seminars

Robustness for Models and Algorithms in Machine Learning
Mon, Nov 28, 2022, 12:30 pm

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]

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Robust and Risk-Averse Accelerated Gradient Methods
Mon, Nov 21, 2022, 12:30 pm

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]

Sparse Estimation: Closing the Gap Between L0 and L1 Models
Thu, Nov 17, 2022, 4:30 pm

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.

Large Language Models: Will they keep getting bigger? And, how will we use them if they do?
Thu, Nov 17, 2022, 12:30 pm

The trend of building ever larger language models has dominated much research in NLP over the last few years. In this talk, I will discuss our recent efforts to (at least partially) answer two key questions in this area: Will we be able to keep scaling? And, how will we actually use the models, if we do?

Explainable AI for Climate Science: Detection, Prediction and Discovery
Thu, Nov 10, 2022, 12:30 pm

Earth’s climate is chaotic and noisy. Finding usable signals amidst all of the noise can be challenging. Here, I will demonstrate how explainable artificial intelligence (XAI) techniques can sift through vast amounts of climate data and push the bounds of scientific discovery. Examples include extracting robust indicator patterns of climate…

Machine Learning advancements for design of water and energy policies in a changing climate and society
Tue, Nov 1, 2022, 4:30 pm

In this talk, we provide an overview of recent advances in data-driven modeling and control of human-water-energy systems and showcase how Machine Learning techniques can help (i) infer natural and anthropogenic drivers of observed hydroclimatic patterns and improve their predictability in space and time, (ii) understand and conceptualize the mutual influences between human behaviors and water-energy systems; and (iii) design strategic planning and management policies optimizing multiple and conflicting objectives with different dynamics and informed by heterogeneous information. 

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Learning Space-Group Invariant Functions
Mon, Oct 31, 2022, 4:30 pm

The plane and space groups are groups that specify how to tile two- or three-dimensional Euclidean space with a shape: They enumerate all possible ways in which a shape can be isometrically replicated across the space. I will describe how to explicitly compute approximate eigenfunctions of the Laplace-Beltrami operator on the orbifold defined by any such group.

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Using Transportability Estimators to Understand Reasons For Differences In Intervention Effects Across Sites
Tue, Oct 25, 2022, 12:00 pm

Multi-site studies are common, and intervention effect estimates frequently differ across sites. We discuss reasons why effect estimates may differ across sites and relate these to transportability. In scenarios where transport is possible, we develop transport estimators to better understand why intervention effects differ across sites/population.

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A Language-Based Model of Organizational Identification Demonstrates How Within-Person Changes in Identification Relate to Network Position
Mon, Oct 10, 2022, 12:00 pm

Shifting attachments to social groups are a constant in the modern era.They are especially pronounced in the contemporary workplace. What accounts for variation in the strength of organizational identification?

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Completing large low rank matrices with only few observed entries: A one-line algorithm with provable guarantees
Mon, Sep 19, 2022, 4:30 pm

Suppose you observe very few entries from a large matrix. Can we predict the missing entries, say assuming the matrix is (approximately) low rank ? We describe a very simple method to solve this matrix completion problem. We show our method is able to recover matrices from very few entries and/or with ill conditioned matrices, where many other popular methods fail. 

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