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  • Security and Privacy in an Everchanging System Landscape

    Special Seminar Series
    SDSC, The Auditorium 9836 Hopkins Dr, La Jolla, San Diego, CA, United States

    Abstract: From AI and IoT to AR/VR and Web 3.0, computer systems are evolving at an unprecedented rate. While this evolution has given rise to exciting applications and opportunities, it has also brought about novel security and privacy challenges within these systems and across their interactions with existing platforms. In this talk, I will discuss how system security researchers can keep up with this everchanging landscape and showcase some of my lab's recent work on understanding and detecting malicious web bots. I will explore how we can build and roll out research infrastructure to measure web bot activities and later use our newfound understanding to develop practical solutions to counter them. I will highlight how we can apply similar research principles to areas such as AI and IoT. Finally, I will conclude my talk by previewing some of my ongoing work and outlining my research roadmap toward achieving "security at inception" for emerging systems.

  • Proximal MCMC for Bayesian Inference of Constrained and Regularized Estimation

    Seminar Series
    SDSC, The Auditorium 9836 Hopkins Dr, La Jolla, San Diego, CA, United States

    Abstract: In this talk I will introduce some extensions to the proximal Markov Chain Monte Carlo (Proximal MCMC) - a flexible and general Bayesian inference framework for constrained or regularized parametric estimation. The basic idea of Proximal MCMC is to approximate nonsmooth regularization terms via the Moreau-Yosida envelope. Initial proximal MCMC strategies, however, fixed nuisance and regularization parameters as constants, and relied on the Langevin algorithm for the posterior sampling. We extend Proximal MCMC to the full Bayesian framework with modeling and data-adaptive estimation of all parameters including regularization parameters. More efficient sampling algorithms such as the Hamiltonian Monte Carlo are employed to scale Proximal MCMC to high-dimensional problems. Our proposed Proximal MCMC offers a versatile and modularized procedure for the inference of constrained and non-smooth problems that is mostly tuning parameter free. We illustrate its utility on various statistical estimation and machine learning tasks.

  • Just Opt Out? Lessons Learned From a Decade of Evasion

    IPE Data Lunchtime Series
    Public Engagement Building (PEB) 721 9625 Scholars Drive North MC 0305, La Jolla, CA, United States

    Abstract: With the rise of techlash, an increasing number of users wish they could just say no to data tracking, surveillance capitalism, and the socially divisive effects of creepy technologies […]

  • Earth & Ocean Image Processing Made Easy with MATLAB – Lunch and Learn

    Vaughan Hall, Room 100 (UCSD-SIO) 8629 Kennel Way, La Jolla, CA, United States

    Join us for a lunch-n-learn technical seminar from our MathWorks team on image processing with MATLAB! MathWorks is looking to create a connection point for conversations about the landscape of computational languages - and the broader impact that software has in academia and industry today. This session explores the basics of pixel-level image processing and high-level machine learning models in MATLAB for images.

  • UC San Diego & MathWorks | Research & Curriculum Micro Symposium

    Qualcomm Conference Room at JSOE Jacobs Hall, 9736 Engineers Ln, La Jolla, San Diego, CA, United States

    A series of lightning talks will be presented highlighting collaborative efforts between UC San Diego and MathWorks via series of supported research and curriculum projects. Join us to learn how each project team are crossing boundaries and using MATLAB & Simulink in their work in areas such as Data Science, Oceanography, and various Engineering disciplines!

  • Scaling and Generalizing Approximate Bayesian Inference | David Blei

    Jeffrey Elman Distinguished Lecturer Series
    SDSC, The Auditorium 9836 Hopkins Dr, La Jolla, San Diego, CA, United States

    A core problem in statistics and machine learning is to approximate difficult-to-compute probability distributions. This problem is especially important in Bayesian statistics, which frames all inference about unknown quantities as a calculation about a conditional distribution. In this talk I review and discuss innovations in variational inference (VI), a method that approximates probability distributions through optimization. VI has been used in myriad applications in machine learning and Bayesian statistics.

  • Algorithms for multi-group learning

    Colloquia Lecture Series
    SDSC, The Synthesis Center 9500 Gilman Drive, La Jolla, CA, United States

    Abstract: Multi-group agnostic learning is a formal learning criterion that is concerned with the conditional risks of predictors within subgroups of a population. The criterion addresses recent practical concerns such as subgroup fairness and hidden stratification. I'll talk about the structure of solutions to the multi-group learning problem, as well as some simple and near-optimal algorithms for the learning problem. This is based on joint work with Christopher Tosh.