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  • CS Colloquium: Yang Liu (UC Santa Cruz) - Reliable Machine Learning: From Data to Deployment

    Tue, Feb 28, 2023 @ 11:00 AM - 12:00 PM

    Thomas Lord Department of Computer Science

    Conferences, Lectures, & Seminars

    Speaker: Yang Liu, UC Santa Cruz

    Talk Title: Reliable Machine Learning: From Data to Deployment

    Series: CS Colloquium

    Abstract: Developing reliable machine learning systems presents challenges in handling biased input data and the consequences of deployment. For instance, a machine learning model for question answering (e.g., ChatGPT) can encode mistakes and biases that persist in the database; an unaware machine learning-powered decision-maker (e.g., for loan approval) can automatically deny people the chance of recourse, resulting in a decline of trust between human and machines; deploying a sequence of myopically optimized models may create an unfair "echo chamber" for users. The list goes on. This talk presents three challenges to building a reliable machine learning system: (1) developing fair and robust algorithms with biased training data, (2) auditing the dynamic interactions between users and machine learning models, and (3) maximizing the long-term welfare of machine learning ecosystems with efficient interventions. We will discuss our group's efforts in addressing these challenges.

    This lecture satisfies requirements for CSCI 591: Research Colloquium

    Biography: Yang Liu is an Assistant Professor of Computer Science and Engineering at UC Santa Cruz (2019 - present). He also leads the machine learning fairness team at ByteDance AI Lab. He was previously a postdoctoral fellow at Harvard University (2016 - 2018). In 2015, he received his Ph.D. degree from the Department of EECS at the University of Michigan, Ann Arbor. His research focuses on developing fair and robust machine learning algorithms to tackle the challenges of biased and shifting data. He is a recipient of the NSF CAREER Award. He has been selected to participate in several high-profile projects, including NSF-Amazon Fairness in AI, DARPA SCORE, and IARPA HFC. His research has observed deployments with FICO and Amazon. His recent work has been recognized with four best paper awards at relevant workshops.

    Host: Vatsal Sharan

    Location: Olin Hall of Engineering (OHE) - 132

    Audiences: Everyone Is Invited

    Contact: Assistant to CS chair


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