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Events for October 21, 2024
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PhD Dissertation Defense - Han Zhang
Mon, Oct 21, 2024 @ 09:30 AM - 11:30 AM
Thomas Lord Department of Computer Science
University Calendar
Title: Speeding up Multi-Objective Search Algorithms
Date: Oct 21, 2024
Location: SAL- Henry Salvatori Computer Science Center 213
Time: 9:30 AM - 11:30 PM
Committee members: Satish Kumar, Sven Koenig, Bistra Dilkina, Satyandra Kumar Gupta, Ariel Felner
Abstract: The multi-objective search problem is the problem of finding paths from a start state to a goal state in a graph where each edge is annotated with multiple costs. A typical task of multi-objective search is to find the Pareto frontier, that is, the set of all undominated paths from the start state to the goal state. This problem is important for many applications, such as transporting hazardous materials, where travel distance and risk are two costs that need to be considered. While researchers have developed various techniques over the past years for speeding up single-objective searches on large graphs, many of them have not been investigated in the context of multi-objective search. In this thesis, I hypothesize that one can speed up multi-objective search algorithms by applying insights gained from single-objective search algorithms after proper generalization. Specifically, I consider the following four classes of techniques that have been used to speed up single-objective search algorithms, namely, (1) by trading off solution quality with efficiency, (2) by anytime search, (3) by preprocessing techniques, and (4) by efficient data structures for time-consuming operations. We validate this hypothesis by introducing various new multi-objective search algorithms and speed-up techniques.Location: Henry Salvatori Computer Science Center (SAL) - 213
Audiences: Everyone Is Invited
Contact: Ellecia Williams
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PhD Thesis Proposal - Weizhao Jin
Mon, Oct 21, 2024 @ 02:00 PM - 03:00 PM
Thomas Lord Department of Computer Science
University Calendar
Title: Efficiency in Privacy-Preserving Computation Via Domain Knowledge
Date and Time: 10/21/24 - 2:00p - 3:00p
Location: DMC 103
Committee Members: Srivatsan Ravi, Bhaskar Krishnamachari, Harsha V. Madhyastha, Fred Morstatter
Abstract: In recent years, the importance of privacy has grown significantly due to the increasing reliance on user data for building server-side applications and services. To comply with expanding privacy regulations such as GDPR, service providers have adopted privacy-preserving primitives that maintain computational functionality while ensuring user privacy. However, a key challenge lies in integrating these privacy-preserving techniques, such as homomorphic encryption and multi-party computation, into application protocols in a way that balances the efficiency and feasibility of deployment. My thesis proposal investigates two distinct domains emphasizing privacy-preserving computation variants and proposes practical domain-knowledge-based solutions to address challenges related to overhead and protocol complexity for efficient privacy in machine learning and in networks/IoT.Location: DMC 103
Audiences: Everyone Is Invited
Contact: Felante' Charlemagne
