Select a calendar:
Filter February Events by Event Type:
SUNMONTUEWEDTHUFRISAT
Events for February 07, 2024
-
CS Colloquium - Nathan Sturtevant (University of Alberta / Amii) - Researching the foundations of heuristic search
Wed, Feb 07, 2024 @ 09:00 AM - 10:00 AM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Nathan Sturtevant, University of Alberta / Amii
Talk Title: Researching the foundations of heuristic search
Abstract: Although the field of heuristic search is over 50 years old, the last 6-7 years have seen numerous revisions to the foundational algorithms in the field. These include the theories for bidirectional search, for suboptimal search, and for improving the worst-case performance of fundamental algorithms such as A* and IDA*. This talk will give an overview of these new results, demonstrating the changes and their impact, many of which center around the notion of whether re-expansions are allowed during search.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Nathan is a Fellow and Canada CIFAR AI Chair at Amii and a Professor in the Department of Computing Science at the University of Alberta. His research looks broadly at heuristic and combinatorial search problems, including both theoretical and applied approaches, with many applications in games. His work on pathfinding was used in the game Dragon Age: Origins, and will appear in the upcoming Nightingale. Nathan’s work has won the best paper awards at the AAAI, and SoCS conferences, as well as the AI Journal Prominent Paper Award.
Host: Sven Koenig
More Info: https://usc.zoom.us/j/6192383533
Location: https://usc.zoom.us/j/6192383533
Audiences: Everyone Is Invited
Contact: CS Events
Event Link: https://usc.zoom.us/j/6192383533
-
CS Colloquium - Chien-Ming Huang (Johns Hopkins University) - Becoming Teammates: Designing Assistive, Collaborative Machines
Wed, Feb 07, 2024 @ 10:00 AM - 11:00 AM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Chien-Ming Huang , Johns Hopkins University
Talk Title: Becoming Teammates: Designing Assistive, Collaborative Machines
Abstract: The growing power in computing and AI promises a near-term future of human-machine teamwork. In this talk, I will present my research group’s efforts in understanding the complex dynamics of human-machine interaction and designing intelligent machines aimed to assist and collaborate with people. I will focus on 1) tools for onboarding machine teammates and authoring machine assistance, 2) methods for detecting, and broadly managing, errors in collaboration, and 3) building blocks of knowledge needed to enable ad hoc human-machine teamwork. I will also highlight our recent work on designing assistive, collaborative machines to support older adults aging in place.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Chien-Ming Huang is the John C. Malone Assistant Professor in the Department of Computer Science at the Johns Hopkins University. His research focuses on designing interactive AI aimed to assist and collaborate with people. He publishes in top-tier venues in HRI, HCI, and robotics including Science Robotics, HRI, CHI, and CSCW. His research has received media coverage from MIT Technology Review, Tech Insider, and Science Nation. Huang completed his postdoctoral training at Yale University and received his Ph.D. in Computer Science at the University of Wisconsin–Madison. He is a recipient of the NSF CAREER award. https://www.cs.jhu.edu/~cmhuang/
Host: Stefanos Nikolaidis
Location: Olin Hall of Engineering (OHE) - 132
Audiences: Everyone Is Invited
Contact: CS Events
-
PhD Thesis Defense - Sepanta Zeighami
Wed, Feb 07, 2024 @ 01:00 PM - 03:00 PM
Thomas Lord Department of Computer Science
University Calendar
Committee members: Cyrus Shahabi (chair), Keith Chugg, Vatsal Sharan, Haipeng Luo
Title: A Function Approximation View of Database Operations for Efficient, Accurate, Privacy-Preserving & Robust Query Answering with Theoretical Guarantees
Abstract: Machine learning models have been recently used to replace various database components (e.g., index, cardinality estimator) and provide substantial performance enhancements over their non-learned alternatives. Such approaches take a function approximation view of the database operations. They consider the database operation as a function that can be approximated (e.g., an index is a function that maps items to their location in a sorted array) and learn a model to approximate the operation's output. In this thesis, we first develop the Neural Database (NeuroDB) framework which extends this function approximation view by considering the entire database system as a function that can be approximated. We show, utilizing this framework, that training neural networks that take queries as input and are trained to output query answer estimates provide substantial performance benefits in various important database problems including approximate query processing, privacy-preserving query answering, and query answering on incomplete datasets. Moreover, we present the first theoretical study of this function approximation view of database operations, providing the first-ever theoretical analysis of various learned database operations. Our analysis provides theoretical guarantees on the performance of the learned models, showing why and when they perform well. Furthermore, we theoretically study the model size requirements, showing how model size needs to change as the dataset changes to ensure a desired accuracy level. Our results enhance our understanding of learned database operations and provide the much-needed theoretical guarantees on their performance for robust practical deployment.
Zoom Link: https://usc.zoom.us/j/91683810479?pwd=VXBmblhDdzZCZU1Oc05jRFV2dzI2dz09
Meeting ID: 916 8381 0479
Passcode: 250069Location: Charles Lee Powell Hall (PHE) - 106
Audiences: Everyone Is Invited
Contact: CS Events
Event Link: https://usc.zoom.us/j/91683810479?pwd=VXBmblhDdzZCZU1Oc05jRFV2dzI2dz09
