• Deep Latent Variable Modeling of Physiological Signals

    Zoom

    DATE/TIME: Friday, May 24th, 9:30 am
    SPEAKER: PhD Candidate Khuong An Vo
    A deep latent variable model is a powerful tool for modeling complex distributions. These models assume that underlying structures, but unobserved, are present within the data. In this dissertation, we explore high-dimensional problems related to physiological monitoring using latent variable models.

  • Differential Privacy for Non-standard Settings

    EH 5204

    Name: Joann Chen Chair: Prof. Zhou Li Date: May 28, 2024 Time:  11:00 AM Location: EH 5204 Committee: Prof. Yanning Shen, Prof. Gene Tsudik Title: Differential Privacy for Non-standard Settings Abstract: […]

  • Multi-Sensor Data Fusion and Machine Learning for Adaptive Autonomous Systems

    ISEB 1200 Interdisciplinary Science and Engineering Building University of California, Irve, Irvine, CA, United States

    Name: Trier Mortlock Chair: Prof. Mohammad Al Faruque Date: May 31, 2024 Time:  2 PM Location: ISEB 1200 Committee: Prof. Faryar Jabbari, Prof. Pramod Khargonekar Title: Multi-Sensor Data Fusion and […]

  • Modeling and Evaluation of a Cache-less Grid of Processing Cells

    EH 3206 Engineering Hall University of California, Irve, Irvine, CA, United States

    Name: Vivek Govindasamy Chair: Prof. Rainer Doemer Date: March 11, 2025 Time: 10:00 AM Location: EH 3206 (CECS Conference Room) Committee: Prof. Mohammad Al Faruque, Prof. Ian Harris Title: Modeling […]

  • In-Memory Computing to Accelerate Machine Learning

    EH 2430 Engineering Hall, University of California, Irvine, Irvine, CA, United States

    DATE/TIME: Wednesday, May 14, 11:00am
    SPEAKER: Mariam Rakka
    In-memory computing (IMC) offers a promising alternative by enabling computation directly within memory arrays. This dissertation presents novel architectural and algorithmic strategies to accelerate machine learning inference using IMC.