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  • PhD Thesis Defense - Aaron Ferber

    Fri, Aug 11, 2023 @ 09:30 AM - 11:30 AM

    Thomas Lord Department of Computer Science

    University Calendar

    PhD Thesis Defense - Aaron Ferber

    Committee Members: Bistra Dilkina (Chair), Yan Liu, and Phebe Vayanos

    Title: Artificial Decision Intelligence: Integrating Deep Learning and Combinatorial Optimization

    Abstract: Artificial Intelligence (AI) has the potential to impact many facets of our society largely due to its ability to quickly make high quality data driven decisions at scale. We consider Artificial Decision Intelligence (ADI) to be a paradigm for building artificial intelligence methods geared explicitly toward automatic decision making. In the rapidly evolving paradigms of machine learning (ML) and combinatorial optimization (CO), remarkable progress has been made in different directions, revolutionizing how we synthesize insights from data as well as how to best act on those insights. Machine learning, specifically deep learning, with its ability to learn intricate patterns from seemingly unstructured data, has seen profound success across diverse applications. Simultaneously, combinatorial optimization has made significant strides, efficiently performing industrial scale decision-making by searching for optimal solutions from combinatorially large and highly structured search spaces. This thesis explores different perspectives on the tight integration of these two paradigms: machine learning and combinatorial optimization, developing new tools that demonstrate the strengths of both approaches for solving complex tasks. Taking different perspectives on machine learning, combinatorial optimization, and how they can be combined in a cohesive and complementary manner, we propose new methodologies that enable end to end data driven decision making, deep predictive models that respect combinatorial constraints, methods that solve complex problems by learning to formulate simpler surrogate optimization problems, and optimization algorithms that learn from historical data to improve solver performance. The proposed methodologies contribute to the advancement of our capability in handling new and complex real world problems. Specifically, we demonstrate the impact of our methodologies in several domains, such as identifying wildlife trafficking routes, designing photonic devices, large scale recommendation systems, financial portfolio optimization, generating game levels, and smart energy grid scheduling. Thus, this thesis serves as a step forward in artificial decision intelligence by solving complex tasks and providing decision support tools that leverage machine learning and combinatorial optimization.

    Location: Henry Salvatori Computer Science Center (SAL) - 213

    Audiences: Everyone Is Invited

    Contact: Melissa Ochoa

    Event Link: https://usc.zoom.us/my/aaron.ferber

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