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Events for November 21, 2022
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PhD Defense- Ninareh Mehrabi
Mon, Nov 21, 2022 @ 10:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Candidate: Ninareh Mehrabi
Date: Monday, November 21st, 2022
Time: 10:00 am - noon PT
Zoom Meeting ID: 986 7933 6430
Passcode: 813783
Or via URL: https://usc.zoom.us/j/98679336430?pwd=akpBV05CQ3o5VVlwWnpxT2piVlB3QT09
Title: Responsible Artificial Intelligence for a Complex World
Abstract: With the advancement of Artificial Intelligence (AI) and its omnipresent role in different applications, it is crucial to ensure that AI systems comply with responsible practices. Moreover, the environment in which AI systems learn and interact with contains various external factors that might adversely affect their behavior. Thus, those systems should be able to mitigate potentially negative impacts of such factors. This dissertation explores several important dimensions that are essential for designing responsible AI systems. First, we focus on fairness as a central concept for responsible AI systems and analyze existing biases in various data sources and models. Moreover, we describe a framework based on interpretability for generating fair and equitable outcomes. Second, we discuss robustness to external perturbations as another important property for such systems. Next, we discuss human-centered AI systems which take natural language prompts as input, demonstrate possible issues due to ambiguous interpretation of those prompts, and describe a framework for resolving such ambiguities and generating faithful outcomes to human intention. Finally, we discuss ideas for designing AI systems that can internalize ethics and form a realization about the consequences of tasks and design choices associated with them. We hope that the contributions presented in this dissertation will move us closer to having more responsible AI systems.
WebCast Link: https://usc.zoom.us/j/98679336430?pwd=akpBV05CQ3o5VVlwWnpxT2piVlB3QT09
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
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PhD Defense - Tu Do
Mon, Nov 21, 2022 @ 01:00 PM - 03:00 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Candidate: Tu Do
Title: Optimizing Execution of In situ Workflows
Committee: Ewa Deelman (Chair), Aiichiro Nakano, Viktor Prasanna, Michela Taufer
Abstract:
Advances in high-performance computing (HPC) allow scientific simulations to run at an ever-increasing scale, generating a large amount of data that needs to be analyzed over time. Conventionally, the simulation outputs the entire simulated data set to the file system for later post-processing. Unfortunately, the slow growth of I/O technologies compared to the computing capability of present-day processors causes an I/O bottleneck of post-processing as saving data to storage is not as fast as data is generated. According to data-centric models, a new processing paradigm has recently emerged, called in situ, where simulation data is analyzed on-the-fly to reduce the expensive I/O cost of saving massive data for post-processing. Since an in situ workflow usually consists of co-located tasks running concurrently on the same resources in an iterative manner, the execution yields complicated behaviors that create challenges in evaluating the efficiency of an in situ run. To enable efficient execution of in situ workflows, this dissertation proposes a framework to enable in situ execution between simulations and analyses and introduces a computational efficiency model to characterize efficiency of an in situ execution. By extending the proposed performance model to resource-aware performance indicators, we introduce a method to assess resource usage, resource allocation, and resource provisioning for in situ workflow ensembles. Finally, we discuss the ideas of designing effective scheduling of a workflow ensemble through determining appropriate co-scheduling strategies and resource assignment for each simulation and analysis in the ensemble.
WebCast Link: https://usc.zoom.us/j/94496448526
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
