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Events for October 03, 2022

  • EYxUSC Tech Takeover Tabling Session (On-Campus, Viterbi)

    Mon, Oct 03, 2022 @ 11:00 AM - 02:00 PM

    Viterbi School of Engineering Career Connections

    Workshops & Infosessions

    Join EY professionals and recruiters for this casual tabling event in order to network and learn more about our technology roles within our Consulting practice.

    We will have professionals from Cybersecurity, Forensics, Technology Consulting, and Quantitative Consulting.

    We are currently hiring for summer 2023 internships and summer/fall full time staff positions. Internships are for those students graduating in summer/fall 2024, and full time roles are for those graduating in summer/fall 2023

    Can you offer Visa sponsorship? Are you able to hire a student on CPT or OPT?

    Yes, but we have very limited spots

    Note: RSVP to let us know you are coming, your RSVP does not reserve a spot. This event is outdoors and students are going to meet EY members on a first-come first-served basis

    Location: Epstein Family Plaza

    Audiences: Everyone Is Invited

    Contact: RTH 218 Viterbi Career Connections

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  • PhD Thesis Proposal - Umang Gupta

    Mon, Oct 03, 2022 @ 03:00 PM - 04:30 PM

    Thomas Lord Department of Computer Science

    University Calendar

    PhD Candidate: Umang Gupta

    Title: Controlling Information for Fairness and Privacy in Machine Learning

    Committee: Greg Ver Steeg, Paul Thompson, Bistra Dilkina, Kristina Lerman, Fred Morstatter.

    With the increasing ubiquity of machine learning models in everyday life, a critical issue occurs when these models capture unintended information. This leads to unintended biases and memorization of training data, resulting in unfair outcomes and risking privacy. These phenomena are especially troublesome in applications where data privacy needs to be upheld, such as medical imaging, or where unfairness can lead to disparate outcomes, such as hiring decisions. To this end, we study this underlying problem of capturing unintended information in various domains. Specifically, we discuss ways to ensure fairness in decision-making by learning fair data representations and controlling unfair language generation by correctly modulating information in neural networks. Finally, we demonstrate that releasing neuroimaging models can reveal private information about the individuals participating in the training set and discuss ongoing work on learning with privacy.

    WebCast Link: https://usc.zoom.us/j/96698045892?pwd=UTRDZUNHRTVFS1dieW1URmtEWXZydz09

    Audiences: Everyone Is Invited

    Contact: Lizsl De Leon

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