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  • Acceleration in Optimization, Sampling, and Machine Learning

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

    Optimization, sampling, and machine learning are essential components of data science. In this talk, I will cover my work on accelerated methods in these fields and highlight some connections between them.

    In optimization, I will present optimization as a two-player zero-sum game, which is a modular approach for designing and analyzing convex optimization algorithms by pitting a pair of no-regret learning strategies against each other. This approach not only recovers several existing algorithms but also gives rise to new ones. I will also discuss the use of Heavy Ball in non-convex optimization, which is a popular momentum method in deep learning. Despite its success in practice, Heavy Ball currently lacks theoretical evidence for its acceleration in non-convex optimization. To bridge this gap, I will present some non-convex problems where Heavy Ball exhibits provable acceleration guarantees.

    In sampling, I will describe how to accelerate a classical sampling method called Hamiltonian Monte Carlo by setting its integration time appropriately, which builds on a connection between sampling and optimization. In machine learning, I will talk about Gradient Descent with pseudo-labels for fast test-time adaptation under the context of tackling distribution shifts.

  • Intelligent mobile systems for equitable healthcare

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

    Access to even basic medical resources is greatly influenced by factors like an individual's birth country and zip code. In this talk, I will present my work on designing AI-based mobile systems for equitable healthcare. I will showcase three systems that are not only interesting from an AI standpoint but are also having real-world medical impact. The first system can detect ear infections using only a smartphone and a paper cone. The second system enables low-cost newborn hearing screening using inexpensive earphones. Lastly, I will present an ambient sensing system that employs smart devices to detect emergent and life-threatening medical events such as cardiac arrest. Through these examples, I will demonstrate how new applied machine learning and sensing approaches that generalize across hardware and work in real-world environments can help to address pressing societal problems.

  • Language Models and Human Language Acquisition

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

    Abstract: Children have a remarkable ability to acquire language. This propensity has been an object of fascination in science for millennia, but in just the last few years, neural language models (LMs) have also proven to be incredibly adept at learning human language. In this talk, I discuss scientific progress that uses recent developments in natural language processing to advance linguistics—and vice-versa. My research explores this intersection from three angles: evaluation, experimentation, and engineering. Using linguistically motivated benchmarks, I provide evidence that LMs share many aspects of human grammatical knowledge and probe how this knowledge varies across training regimes. I further argue that—under the right circumstances—we can use LMs to test hypotheses that have been difficult or impossible to evaluate with human subjects. Such experiments have the potential to transform debates about the roles of nature and nurture in human language learning. As a proof of concept, I describe a controlled experiment examining how the distribution of linguistic phenomena in the input affects syntactic generalization. While the results suggest that the linguistic stimulus may be richer than often thought, there is no avoiding the fact that current LMs and humans learn language in vastly different ways. I describe ongoing work to engineer learning environments and objectives for LM pretraining inspired by human development, with the goal of making LMs more data efficient and more plausible models of human learning.

  • Responsible AI: Privacy and Fairness in Decision and Learning Systems

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

    Differential Privacy has become the go-to approach for protecting sensitive information in data releases and learning tasks that are used for critical decision processes. For example, census data is used to allocate funds and distribute benefits, while several corporations use machine learning systems for criminal assessments, hiring decisions, and more. While this privacy notion provides strong guarantees, we will show that it may also induce biases and fairness issues in downstream decision processes. These issues may adversely affect many individuals' health, well-being, and sense of belonging, and are currently poorly understood.

    In this talk, we delve into the intersection of privacy, fairness, and decision processes, with a focus on understanding and addressing these fairness issues. We first provide an overview of Differential Privacy and its applications in data release and learning tasks. Next, we examine the societal impacts of privacy through a fairness lens and present a framework to illustrate what aspects of the private algorithms and/or data may be responsible for exacerbating unfairness. We hence show how to extend this framework to assess the disparate impacts arising in Machine Learning tasks. Finally, we propose a path to partially mitigate these fairness issues and discuss grand challenges that require further exploration.

  • Decoding Nature’s Message Through the Channel of Artificial Intelligence

    Special Seminar Series
    SDSC, The Synthesis Center 9500 Gilman Drive, La Jolla, CA, United States

    Abstract: Nature contains many interesting physics we want to search for, but it cannot speak them out loud. Therefore physicists need to build large particle physics experiments that encode nature's message into experimental data. My research leverages artificial intelligence and machine learning to maximally decode nature's message from those data. The questions I want to ask nature is: Are neutrinos Majorana particles? The answer to this question would fundamentally revise our understanding of physics and the cosmos. Currently, the most effective experimental probe for Majorana neutrino is neutrinoless double-beta decay(0vββ). Cutting-edge AI algorithms could break down significant technological barriers and, in turn, deliver the world's most sensitive search for 0vββ. This talk will discuss one such algorithm, KamNet, which plays a pivotal role in the new result of the KamLAND-Zen experiment. With the help of KamNet, KamLAND-Zen provides a limit that reaches below 50 meV for the first time and is the first search for 0νββ in the inverted mass ordering region. Looking further, the next-generation 0vββ experiment LEGEND has created the Germanium Machine Learning group to aid all aspects of LEGEND analysis and eventually build an independent AI analysis. As the odyssey continues, AI will enlighten the bright future of experimental particle physics.

  • Imaging and Informatics in ROP

    Ophthalmology Informatics and Data Science Seminar Series

    I will briefly summarize the history of the "Imaging and Informatics in ROP" (i-ROP) consortium, originally started by Michael Chiang, and how a focus on building a multidisciplinary team with expertise in informatics and data science laid the groundwork for a number of advances in the field. Downstream work led to innovations in basic machine learning methodology, understanding of clinical diagnostic patterns and inter-observer variability which influenced the most recent international classification of ROP, a novel potential genetic association, disease epidemiology in low and middle income countries, the potential for racial/ethnic bias in AI, federated learning, and more with translational applications beyond ROP.  I will touch on a number of topics very superficially and then can discuss in more detail whatever is most interesting in the Q&A.  

  • The Interplay of Technology, Ethics, and Policy

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

    Abstract: Technology is often designed and deployed without critical reflection of the values that it embodies. Value trade-offs—between security and privacy, free speech and dignity, autonomy and human agency, and […]

  • chat gpt ucsd lecture

    Chatting GPT

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

    Artificial Intelligence (AI) systems have made astonishing progress in the last year. In particular, Large Language Models (LLMs) — AI systems trained on massive amounts of text — have reached […]

    Free

  • Leveraging Simulators for ML Inference in Particle Physics

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

    Abstract: The field of research investigating machine-learning (ML) methods that can exploit a physical model of the world through simulators is rapidly growing, particularly for applications in particle physics. While these methods have shown considerable promise in phenomenological studies, they are also known to be susceptible to inaccuracies in the simulators used to train them. In this work, we design a novel analysis strategy that uses the concept of simulation-based inference for a crucial Higgs Boson measurement, where traditional methods are rendered sub-optimal due to quantum interference between Higgs and non-Higgs processes. Our work develops uncertainty quantification methods that account for the impact of inaccuracies in the simulators, uncertainties in the ML predictions themselves, and novel strategies to test the coverage of these quoted uncertainties. These new ML methods leverage the vast computational resources that have recently become available to perform scientific measurements in a way that was not feasible before. In addition, this talk briefly discusses certain ML-bias-mitigation methods developed in particle physics and their potential wider applications.