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Event Series Special Seminar Series

Language Models and Human Language Acquisition

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.

Event Series Special Seminar Series

Responsible AI: Privacy and Fairness in Decision and Learning Systems

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.

Event Series Special Seminar Series

Decoding Nature’s Message Through the Channel of Artificial Intelligence

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.

Event Series Ophthalmology Informatics and Data Science Seminar Series

Imaging and Informatics in ROP

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.  

Event Series Special Seminar Series

Leveraging Simulators for ML Inference in Particle Physics

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.

Event Series Special Seminar Series

Security and Privacy in an Everchanging System Landscape

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.

Event Series Seminar Series

Proximal MCMC for Bayesian Inference of Constrained and Regularized Estimation

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.

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.

Event Series Special Seminar Series

Some new results for streaming principal component analysis

Abstract: While streaming PCA (also known as Oja’s algorithm) was proposed about four decades ago and has roots going back to 1949, theoretical resolution in terms of obtaining optimal convergence rates has been obtained only in the last decade. However, we are not aware of any available distributional guarantees, which can help provide confidence intervals on the quality of the solution. In this talk, I will present the problem of quantifying uncertainty for the estimation error of the leading eigenvector using Oja's algorithm for streaming PCA, where the data are generated IID from some unknown distribution. Combining classical tools from the U-statistics literature with recent results on high-dimensional central limit theorems for quadratic forms of random vectors and concentration of matrix products, we establish a distributional approximation result for the error between the population eigenvector and the output of Oja's algorithm. We also propose an online multiplier bootstrap algorithm and establish conditions under which the bootstrap distribution is close to the corresponding sampling distribution with high probability. While there are optimal rates for the streaming PCA problem, they typically apply to the IID setting, whereas in many applications like distributed optimization, the data is generated from a Markov chain and the goal is to infer parameters of the limiting stationary distribution. If time permits, I will also present our near-optimal finite sample guarantees which remove the logarithmic dependence on the sample size in previous work, where Markovian data is downsampled to get a nearly independent data stream.