Final Public Oral Examinations

  • Understanding and Measuring Privacy Risks in Machine Learning

    Thu, Sep 16, 2021, 2:00 pm

    Machine learning models have achieved great success and have been deployed prominently in many real-world applications. However, the sensitive nature of individual users’ data has also raised privacy concerns against machine learning. A recent thread of research has shown that a malicious adversary can infer private information of users’ data by querying target machine learning models.

  • Security Meets Deep Learning

    Thu, Sep 9, 2021, 9:30 am

    Recent years have witnessed the rapid development of deep learning in many domains. These successes inspire using deep learning in the area of security. However, there are at least two main challenges when security meets deep learning. First, the deep learning systems themselves are vulnerable to various attacks, bringing new concerns when using deep learning to improve security in computer systems. Second, the availability of attack data is a problem.

  • Machine Learning Methods for Computational Social Science

    Fri, Aug 20, 2021, 10:00 am

    Contributing to the rising popularity of computational social science, this dissertation presents new methods grounded in machine learning for solving several important problems in political science.

  • Probing Many-body liquids and solids in two-dimensional electron systems

    Mon, Aug 16, 2021, 9:00 am

    This talk will cover many-body liquids and solids occurring in two-dimensional electron systems  (2DESs)  confined  to  GaAs  and  AlAs 

    quantum  wells  subjected  to  a  large perpendicular magnetic field and cooled to very low temperatures. In AlAs 2DESs,  we investigated the Wigner solid and fractional quantum Hall liquid competition.

    Also, the very low-disorder GaAs 2DESs allow us to probe the thermal melting of bubble phases, a type of Wigner solid.

  • Building quantum network nodes based on neutral silicon vacancy centers in diamond

    Wed, Jul 28, 2021, 1:30 pm

    Color centers in diamond are attractive candidates for implementing single-atom quantum memories in a quantum network. This thesis describes an approach to build quantum networks nodes based on color centers in diamond. We propose to use a novel single-atom quantum memory, the neutral charge state of silicon vacancy (SiV0), as the building block for future quantum network. The unique combination of long spin coherence times and efficient optical transitions makes SiV0 a promising candidate for such application.

  • Multipoles, symmetry representations and thermal fluctuations in elastic systems

    Thu, Jul 15, 2021, 3:00 pm to 5:00 pm

    In recent years, we have seen exciting new developments in research on mechanical metamaterials, topological phononics, and mechanics of atomically thin 2D materials. In this talk, I present how methods from physics can help us in understanding the mechanical properties of these systems as well as gaining further intuition.

  • Neural Network Learning: A Multiscale-Entropy and Self-Similarity Approach

    Fri, Jul 16, 2021, 8:30 am to 10:30 am

    Neural networks are machine learning models whose original design has been vaguely inspired by the structure networks of neurons in human brains. Due to recent technological advances that have enabled fast computations on larger models and more training data, neural networks have found many applications in a growing number of areas of science such as computer vision, natural language processing, and medical imaging.

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