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University Calendar
Events for March
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PHD Thesis Defense - Dimitris Stripelis
Mon, Mar 20, 2023 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
University Calendar
PHD Thesis Defense - Dimitris Stripelis
Title:
Heterogeneous Federated Learning
Committee Members:
Jose-Luis Ambite (Chair), Cyrus Shahabi, Paul Thompson, Greg Ver Steeg
Abstract:
Data relevant to machine learning problems are distributed across multiple data silos that cannot share their data due to regulatory, competitiveness, or privacy reasons. Federated Learning has emerged as a standard computational paradigm for distributed training of machine learning and deep learning models across silos. However, the participating silos may have heterogeneous system capabilities and data specifications. In this thesis, we address the challenges in federated learning arising from both computational and semantic heterogeneities. We present federated training policies that accelerate the convergence of the federated model and lead to reduced communication, processing, and energy costs during model aggregation, training, and inference. We show the efficacy of these policies across a wide range of challenging federated environments with highly diverse data distributions in benchmark domains and in neuroimaging. We conclude by describing the federated data harmonization problem and presenting a comprehensive federated learning and integration system architecture that addresses the critical challenges of secure and private federated data harmonization, including schema mapping, data normalization, and data imputation.
Location: https://usc.zoom.us/j/93599773555?pwd=TmI3M1JvTkxEV05DSmQ3dzYyVElmQT09
Audiences: Everyone Is Invited
Contact: Asiroh Cham
Event Link: https://usc.zoom.us/j/93599773555?pwd=TmI3M1JvTkxEV05DSmQ3dzYyVElmQT09
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PhD Thesis Proposal - Gautam Salhotra
Thu, Mar 23, 2023 @ 09:00 AM - 11:00 AM
Thomas Lord Department of Computer Science
University Calendar
Title: Accelerating Robot Reinforcement Learning Using Demonstrations
Committee: Gaurav Sukhatme (Chair), SK Gupta, Laurent Itti, Stefanos Nikolaidis, Somil Bansal
Date: Thursday March 23, 9am PST
Abstract:
Reinforcement learning is a promising and, recently, popular tool to solve robotic tasks such as object manipulation and locomotion. However, it is also well known for being a very hard problem setting to explore in. In contrast, Learning from demonstrations (LfD) methods train agents to the desired solution using demonstrations from a teacher.
I will explore the role of LfD methods to guide the exploration of RL methods, with the aim of applying it to regular object manipulation tasks. I will talk about work that uses planners and trajectory optimizers to guide RL, and then discuss the role human experts can play in LfD for RL. Finally, I will talk about proposed projects that can extend the current work to get the benefits of demonstrations while avoiding the downsides of obtaining them.
Location: Ronald Tutor Hall of Engineering (RTH) - 406
Audiences: Everyone Is Invited
Contact: Melissa Ochoa
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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
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2023 Mork Family Department Student Research Symposium
Fri, Mar 31, 2023 @ 09:00 AM - 03:30 PM
Mork Family Department of Chemical Engineering and Materials Science
University Calendar
Location: Michelson Center for Convergent Bioscience (MCB) - First Floor
Audiences: Everyone Is Invited
Contact: Candy Escobedo
