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Events for March 27, 2023
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PhD Thesis Proposal - Basel Shbita
Mon, Mar 27, 2023 @ 10:30 AM - 12:00 PM
Thomas Lord Department of Computer Science
University Calendar
Title:
Automatic Semantic Spatio-Temporal Interpretation of Historical Maps
Committee:
Craig A. Knoblock (chair), Cyrus Shahabi, John P. Wilson, Jay Pujara, Yao-Yi Chiang
Date:
Friday, February 17th, 1:30pm-3pm PST
Zoom Meeting Details:
https://usc.zoom.us/j/97387539087?pwd=MWEwaHR0Z0FCOEdwdGdEcWxFSnorZz09
Meeting ID: 973 8753 9087
Passcode: 312501
Abstract:
Historical maps provide rich information for researchers in many areas, including the natural and social sciences. These maps include detailed documentation of a wide variety of natural and human-made features, their spatial extent, their changes over time, their geo-names, and additional metadata. Analyzing map collections that cover the same region at different points in time can be labor-intensive even for a scientist, often requiring further grounding and linking with external sources to contextualize the data. With rapidly increasing amounts of digitized map archives, we require methods to convert these maps into a machine-processable and machine-readable semantic form and do so automatically, efficiently, and at scale. Unfortunately, existing techniques are limited and do not leverage the vast landscape of information extracted from archives of historical maps.
In this thesis proposal, we investigate how to convert the extracted geo-data and metadata to a dynamic knowledge graph representation that captures the data semantics, how the data can be interrelated across entire datasets, and how it can be grounded to real-world phenomena by leveraging external resources on the web. We explore approaches that benefit from the open and connective nature of linked data that can produce a spatio-temporal, semantic, and contextualized output that follows linked data principles, and that can be easily extended with further availability of contemporary maps while supporting backward compatible access. Once materialized in a dynamic knowledge graph, the output can hold the data in a semantic network, making it readily shared, accessible, visualized, standardized across domains, and scalable for effortless use by downstream tasks for analysis and expressive integration over time and space.
WebCast Link: https://usc.zoom.us/j/97387539087?pwd=MWEwaHR0Z0FCOEdwdGdEcWxFSnorZz09
Audiences: Everyone Is Invited
Contact: Asiroh Cham
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor.
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CS Colloquium: Pavel Izmailov (New York University) - Deconstructing models and methods in deep learning
Mon, Mar 27, 2023 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Pavel Izmailov, New York University
Talk Title: Deconstructing models and methods in deep learning
Series: CS Colloquium
Abstract: Machine learning models are ultimately used to make decisions in the real world, where mistakes can be incredibly costly. We still understand surprisingly little about neural networks and the procedures that we use to train them, and, as a result, our models are brittle, often rely on spurious features, and generalize poorly under minor distribution shifts. Moreover, these models are often unable to faithfully represent uncertainty in their predictions, further limiting their applicability. In this talk, I will present works on neural network loss surfaces, probabilistic deep learning, uncertainty estimation and robustness to distribution shifts. In each of these works, we aim to build foundational understanding of models, training procedures, and their limitations, and then use this understanding to develop practically impactful, interpretable, robust and broadly applicable methods and models.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: I am a final year PhD student in Computer Science at New York University, working with Andrew Gordon Wilson. I am primarily interested in understanding and improving deep neural networks. In particular my interests include out of distribution generalization, probabilistic deep learning, representation learning and large models. I am also excited about generative models, uncertainty estimation, semi-supervised learning, language models and other topics. Recently, our work on Bayesian model selection was recognized with an outstanding paper award at ICML 2022.
Host: Robin Jia
Location: Ronald Tutor Hall of Engineering (RTH) - 115
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
CS Teaching Faculty Meeting
Mon, Mar 27, 2023 @ 12:00 PM - 02:00 PM
Thomas Lord Department of Computer Science
Receptions & Special Events
Meeting for invited full-time Computer Science teaching faculty only. Event details emailed directly to attendees.
Location: TBD - Hybrid
Audiences: Invited Faculty Only
Contact: Cherie Carter
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor.
