BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Halıcıoğlu Data Science Institute - UC San Diego - ECPv6.16.3//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-ORIGINAL-URL:https://datascience.ucsd.edu
X-WR-CALDESC:Events for Halıcıoğlu Data Science Institute - UC San Diego
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/Los_Angeles
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:20220313T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20221106T090000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:20230312T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20231105T090000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:20240310T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20241103T090000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230316T140000
DTEND;TZID=America/Los_Angeles:20230316T153000
DTSTAMP:20260607T093543
CREATED:20230315T154024Z
LAST-MODIFIED:20230315T154024Z
UID:10000360-1678975200-1678980600@datascience.ucsd.edu
SUMMARY:Optimal methods for reinforcement learning: Efficient algorithms with instance-dependent guarantees | Wenlong Mou
DESCRIPTION:Abstract: Reinforcement learning (RL) is a pillar for modern artificial intelligence. Compared to classical statistical learning\, several new statistical and computational phenomena arise from RL problems\, leading to different trade-offs in the choice of the estimators\, tuning of their parameters\, and the design of efficient algorithms. In many settings\, asymptotic and/or worst-case theory fails to provide the relevant guidance.\nIn this talk\, I present recent advances that involve a more refined approach to RL\, one that leads to non-asymptotic and instance-optimal guarantees. The bulk of this talk focuses on function approximation methods for policy evaluation. I establish a novel class of optimal and instance-dependent oracle inequalities for projected Bellman equations\, as well as efficient computational algorithms achieving them. Among other results\, I will highlight how the instance-optimal guarantees guide the selection of tuning parameters in temporal different methods\, and tackle the instability issue with general function classes. Drawing on this perspective\, I will also discuss a novel class of stochastic approximation methods that yield optimal statistical guarantees for policy optimization problems. \nBio: Wenlong Mou is a Ph.D. candidate at Department of EECS\, UC Berkeley\, advised by Martin Wainwright and Peter Bartlett. Prior to Berkeley\, he received his B.Sc. degree in Computer Science from Peking University. Wenlong’s research interests include statistics\, machine learning theory\, dynamic programming and optimization\, and applied probability. He is particularly interested in designing optimal statistical methods that enable optimal data-driven decision making\, powered by efficient computational algorithms.
URL:https://datascience.ucsd.edu/event/optimal-methods-for-reinforcement-learning-efficient-algorithms-with-instance-dependent-guarantees-wenlong-mou/
LOCATION:SDSC\, The Auditorium\, 9836 Hopkins Dr\, La Jolla\, San Diego\, CA\, United States
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20230317
DTEND;VALUE=DATE:20230318
DTSTAMP:20260607T093543
CREATED:20230315T155732Z
LAST-MODIFIED:20230315T155732Z
UID:10000361-1679011200-1679097599@datascience.ucsd.edu
SUMMARY:Scientific Machine Learning Symposium
DESCRIPTION:
URL:https://datascience.ucsd.edu/event/scientific-machine-learning-symposium/
LOCATION:The Great Hall\, 9500 Gilman Drive\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Sponsorship,Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230320T140000
DTEND;TZID=America/Los_Angeles:20230320T140000
DTSTAMP:20260607T093543
CREATED:20230302T000631Z
LAST-MODIFIED:20230317T154618Z
UID:10000352-1679320800-1679320800@datascience.ucsd.edu
SUMMARY:Sampling from Graphical Models via Spectral Independence | Zongchen Chen
DESCRIPTION:Abstract: In many scientific settings we use a statistical model to describe a high-dimensional distribution over many variables. Such models are often represented as a weighted graph encoding the dependencies between different variables and are known as graphical models. Graphical models arise in a wide variety of scientific fields throughout science and engineering.\nOne fundamental task for graphical models is to generate random samples from the associated distribution. The Markov chain Monte Carlo (MCMC) method is one of the simplest and most popular approaches to tackle such problems. Despite the popularity of graphical models and MCMC algorithms\, theoretical guarantees of their performance are not known even for some simple models. I will describe a new tool called “spectral independence” to analyze MCMC algorithms and more importantly to reveal the underlying structure behind such models. I will also discuss how these structural properties can be applied to sampling when MCMC fails and to other statistical problems like parameter learning or model fitting. \n\nBio: Zongchen Chen is an instructor (postdoc) in Mathematics at MIT. He received his PhD degree in Algorithms\, Combinatorics and Optimization (ACO) at Georgia Tech in 2021 advised by Eric Vigoda. His thesis received the 2021 Georgia Tech College of Computing Outstanding Doctoral Dissertation Award. He received his BS degree in Mathematics & Applied Mathematics from Zhiyuan College at Shanghai Jiao Tong University in 2016. He is broadly interested in randomized algorithms\, discrete probability\, and machine learning. His current research interests include Markov chain Monte Carlo (MCMC) methods\, approximate counting and sampling\, and learning and testing for high-dimensional distributions.
URL:https://datascience.ucsd.edu/event/zongchen-chen/
LOCATION:SDSC\, The Auditorium\, 9836 Hopkins Dr\, La Jolla\, San Diego\, CA\, United States
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230321T140000
DTEND;TZID=America/Los_Angeles:20230321T153000
DTSTAMP:20260607T093543
CREATED:20230316T180226Z
LAST-MODIFIED:20230316T180226Z
UID:10000362-1679407200-1679412600@datascience.ucsd.edu
SUMMARY:Uncovering the algorithmic foundations of language learning and processing
DESCRIPTION:Abstract: Human language is extraordinarily complex. Nevertheless\, we readily acquire language as children\, when we are most cognitively limited\, and we comprehend language as adults with striking efficiency. My research seeks to understand the mental algorithms that allow us to accomplish this feat\, with particular focus on how memory and prediction mechanisms are recruited to overcome the bottlenecks of real-time language processing. In this talk\, I will review results from three of my lines of inquiry into this question. First\, using diverse naturalistic reading datasets\, I will show evidence that prediction is a central concern of the human language processing system. Second\, using fMRI measures of naturalistic story listening\, I will show evidence that memory and prediction processes are dissociable in the brain’s response to language\, that syntactic structure building plays a major role in ordinary language comprehension\, and that the neural resources that are responsible for structure building are largely specialized for language. Third\, I will show evidence from computational modeling that memory and prediction pressures independently encourage discovery of phonological regularities from natural speech. Together\, these results support an intricate coordination of memory and prediction abilities for language learning and comprehension. I will conclude by outlining planned directions for my future lab\, integrating neuroimaging\, behavioral methods\, natural language processing\, and computational modeling to study language learning and processing.\n\nBio: Cory is a post-doc at MIT and their work uses computational and experimental methods to study language and the mind\, particularly (1) the cognitive processes that allow us to understand the things we hear and read so quickly\, (2) the learning signals that we leverage as children to acquire language from the environment\, and (3) the role played by real-time information processing constraints in shaping language learning and comprehension.\nCory’s work often builds deep learning models to investigate these questions\, and is actively developing machine learning techniques to help scientists understand complex dynamical systems like the human mind and brain. Their work intersects machine learning\, cognitive science\, neuroscience\, artificial intelligence\, natural language processing\, statistics\, and (psycho)linguistics.
URL:https://datascience.ucsd.edu/event/uncovering-the-algorithmic-foundations-of-language-learning-and-processing/
LOCATION:SDSC\, The Synthesis Center\, 9500 Gilman Drive\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230322T140000
DTEND;TZID=America/Los_Angeles:20230322T153000
DTSTAMP:20260607T093543
CREATED:20230323T182317Z
LAST-MODIFIED:20230323T182317Z
UID:10000370-1679493600-1679499000@datascience.ucsd.edu
SUMMARY:Structured Transformer Models for NLP
DESCRIPTION:Abstract: The field of natural language processing has recently unlocked a wide range of new capabilities through the use of large language models\, such as GPT-4. The growing application of these models motivates developing a more thorough understanding of how and why they work\, as well as further improvements in both quality and efficiency. \nIn this talk\, I will present my work on analyzing and improving the Transformer architecture underlying today’s language models through the study of how information is routed between multiple words in an input. I will show that such models can predict the syntactic structure of text in a variety of languages\, and discuss how syntax can inform our understanding of how the networks operate. I will also present my work on structuring information flow to build radically more efficient models\, including models that can process text of up to one million words\, which enables new possibilities for NLP with book-length text. \nBio: Nikita Kitaev is a final-year Ph.D. student in Computer Science at UC Berkeley\, advised by Dan Klein. His research spans natural language processing and machine learning\, with a focus on understanding the structure and operation of large language models\, including leveraging insights from the field of syntax. He is the recipient of an NSF Graduate Research Fellowship and his work has been recognized through a best paper award at ACL. Previously\, Nikita has received a B.S. in Electrical Engineering and Computer Science from UC Berkeley.
URL:https://datascience.ucsd.edu/event/structured-transformer-models-for-nlp/
LOCATION:SDSC\, The Synthesis Center\, 9500 Gilman Drive\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Colloquium
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20230323
DTEND;VALUE=DATE:20230325
DTSTAMP:20260607T093544
CREATED:20230323T180908Z
LAST-MODIFIED:20230712T092822Z
UID:10000363-1679529600-1679702399@datascience.ucsd.edu
SUMMARY:PhD Open House
DESCRIPTION:HDSI will host the first ever in person DSC Inaugural Open House for Prospective PhD Students (Mar 23 + 24).  The event will include Graduate Student Poster Session and will showcase HDSI diverse research activities. Prospective PhD students are invited. \n  \nPlease contact Laura Horton (lkhorton@ucsd.edu) for event details.
URL:https://datascience.ucsd.edu/event/phd-open-house/
LOCATION:SDSC\, The Auditorium\, 9836 Hopkins Dr\, La Jolla\, San Diego\, CA\, United States
CATEGORIES:Student Event
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230329T140000
DTEND;TZID=America/Los_Angeles:20230329T153000
DTSTAMP:20260607T093544
CREATED:20230302T000631Z
LAST-MODIFIED:20230323T175902Z
UID:10000353-1680098400-1680103800@datascience.ucsd.edu
SUMMARY:Distance-Estimation in Modern Graphs: Algorithms and Impossibility | Nicole Wein
DESCRIPTION:The size and complexity of today’s graphs present challenges that necessitate the discovery of new algorithms. One central area of research in this endeavor is computing and estimating distances in graphs. In this talk I will discuss two fundamental families of distance problems in the context of modern graphs: Diameter/Radius/Eccentricities and Hopsets/Shortcut Sets. The best-known algorithm for computing the diameter (largest distance) of a graph is the naive algorithm of computing all-pairs shortest paths and returning the largest distance. Unfortunately\, this can be prohibitively slow for massive graphs. Thus\, it is important to understand how fast and how accurately the diameter of a graph can be approximated. I will present tight bounds for this problem via conditional lower bounds from fine-grained complexity. Secondly\, for a number of settings relevant to modern graphs (e.g. parallel algorithms\, streaming algorithms\, dynamic algorithms)\, distance computation is more efficient when the input graph has low hop-diameter. Thus\, a useful preprocessing step is to add a set of edges (a hopset) to the graph that reduces the hop-diameter of the graph\, while preserving important distance information. I will present progress on upper and lower bounds for hopsets. \n  \nBio: Nicole Wein is a Simons Postdoctoral Leader at DIMACS at Rutgers University. Previously\, she obtained her Ph.D. from MIT advised by Virginia Vassilevska Williams. She is a theoretical computer scientist and her research interests include graph algorithms and lower bounds including in the areas of distance-estimation algorithms\, dynamic algorithms\, and fine-grained complexity.
URL:https://datascience.ucsd.edu/event/nicole-wein/
LOCATION:SDSC\, The Auditorium\, 9836 Hopkins Dr\, La Jolla\, San Diego\, CA\, United States
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230403T140000
DTEND;TZID=America/Los_Angeles:20230403T153000
DTSTAMP:20260607T093544
CREATED:20230323T181509Z
LAST-MODIFIED:20240402T224726Z
UID:10000366-1680530400-1680535800@datascience.ucsd.edu
SUMMARY:Frameworks for High Dimensional Optimization
DESCRIPTION:I present frameworks for solving extremely large\, prohibitively massive optimization problems. Today\, practical applications require optimization solvers to work at extreme scales\, but existing solvers do not often scale as desired. I present black-box acceleration algorithms for speeding up optimization solvers\, in both distributed and parallel settings. Given a huge problem\, I develop dimension reduction techniques that allow the problem to be solved in a fraction of the original time\, and simultaneously make the computation amenable to distributed computation. Efficient\, dependable and secure distributed computing is increasingly fundamental to a wide range of core applications including distributed data centers\, decentralized power grid\, coordination of autonomous devices\, and scheduling and routing problems. \nIn particular\, I consider two optimization settings of interest. First\, I consider packing linear programming (LP). LP solvers are fundamental to many problems in supply chain management\, routing\, learning and inference problems. I present a framework that speeds up linear programming solvers such as Cplex and Gurobi by an order of magnitude\, while maintaining provably nearly optimal solutions. Secondly\, I present a distributed algorithm that achieves an exponential reduction in message complexity compared to existing distributed methods. I present both empirical demonstrations and theoretical guarantees on the quality of the solution and the speedup provided by my methods
URL:https://datascience.ucsd.edu/event/palma-london/
LOCATION:SDSC\, The Auditorium\, 9836 Hopkins Dr\, La Jolla\, San Diego\, CA\, United States
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230404T140000
DTEND;TZID=America/Los_Angeles:20230404T153000
DTSTAMP:20260607T093544
CREATED:20230323T181718Z
LAST-MODIFIED:20230403T190229Z
UID:10000367-1680616800-1680622200@datascience.ucsd.edu
SUMMARY:Lijun Ding
DESCRIPTION:
URL:https://datascience.ucsd.edu/event/lijun-ding/
LOCATION:SDSC\, The Auditorium\, 9836 Hopkins Dr\, La Jolla\, San Diego\, CA\, United States
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230405T140000
DTEND;TZID=America/Los_Angeles:20230405T153000
DTSTAMP:20260607T093544
CREATED:20230323T181933Z
LAST-MODIFIED:20230403T190338Z
UID:10000368-1680703200-1680708600@datascience.ucsd.edu
SUMMARY:Constrained\, Casual\, and Logical Reasoning for Neural Language Generation
DESCRIPTION:Today’s language models (LMs) can produce human-like fluent text. However\, they generate words with no grounding in the world and cannot flexibly reason about everyday situations and events\, such as counterfactual (“what if?”) and abductive (“what might explain these observations?”) reasoning that are important forms of human cognition activities. In this talk\, I will present my research on connecting reasoning with language generation. Reasoning for language generation poses several key challenges\, including incorporating diverse contextual constraints on the fly\, understanding cause and effect when events unfold\, and grounding on logic structures for consistent reasoning. I will first discuss COLD decoding\, a unified energy-based framework for any off-the-shelf LMs to reason with arbitrary constraints. It also introduces differentiable reasoning over discrete symbolic text for improved efficiency. Secondly\, I will focus on a particularly important form of reasoning\, counterfactual reasoning\, including its first formulation in language generation and our algorithm\, DeLorean\, that enables off-the-shelf LMs to capture causal invariance. Thirdly\, I will present Maieutic prompting\, which improves the logical consistency of neural reasoning by integrating with logic structures. I will conclude with future research toward more general\, grounded\, and trustworthy reasoning with language. \nBio: Lianhui Qin is a final year PhD student in Paul G. Allen School of Computer Science & Engineering at University of Washington\, advised by Prof. Yejin Choi. Her research interests lie in natural language processing\, artificial intelligence\, and machine learning\, with a particular focus on natural language reasoning and generation. Her research has been recognized with Best Paper Award at NAACL 2022\, Best Paper Award at WeCNLP 2020\, Best Demo Paper Nomination at ACL 2019\, as well as Microsoft Research PhD Fellowship.
URL:https://datascience.ucsd.edu/event/lianhui-qin/
LOCATION:SDSC\, The Auditorium\, 9836 Hopkins Dr\, La Jolla\, San Diego\, CA\, United States
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230406T110000
DTEND;TZID=America/Los_Angeles:20230406T123000
DTSTAMP:20260607T093544
CREATED:20230323T182059Z
LAST-MODIFIED:20240402T224726Z
UID:10000369-1680778800-1680784200@datascience.ucsd.edu
SUMMARY:Acceleration in Optimization\, Sampling\, and Machine Learning
DESCRIPTION: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. \nIn 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. \nIn 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. \nBio: Jun-Kun Wang is a postdoctoral researcher in the Department of Computer Science at Yale University\, working with Dr. Andre Wibisono. He received his Ph.D. in Computer Science from the Georgia Institute of Technology in 2021\, advised by Dr. Jacob Abernethy. He earned an MS in Communication Engineering and a BS in Electrical Engineering from National Taiwan University. His research interests are in the theoretical and algorithmic foundations of optimization\, sampling\, and machine learning.
URL:https://datascience.ucsd.edu/event/jun-kun-wang/
LOCATION:SDSC\, The Auditorium\, 9836 Hopkins Dr\, La Jolla\, San Diego\, CA\, United States
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230406T140000
DTEND;TZID=America/Los_Angeles:20230406T150000
DTSTAMP:20260607T093544
CREATED:20230302T000631Z
LAST-MODIFIED:20240402T224726Z
UID:10000354-1680789600-1680793200@datascience.ucsd.edu
SUMMARY:Intelligent mobile systems for equitable healthcare
DESCRIPTION: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. \nBio: Justin Chan is a Ph.D. candidate at the Paul G. Allen School of Computer Science and Engineering at the University of Washington. His work on smartphone-based ear infections is now FDA-listed and is available to select early access healthcare systems. His work on new-born hearing screening has led to an international effort called TUNE with the goal of bringing universal newborn hearing screening across Kenya as well as collaborations with NGOs such as the Global Foundation for Children with Hearing Loss to deploy this technology in Nepal and Mongolia. His work on contactless cardiac arrest detection has been licensed to a startup which has recently been acquired by Google. He was also a lead contributor for CovidSafe (now WA Notify)\, a COVID-19 contact tracing and symptom tracking app\, which became part of official efforts by the WA Department of Health to manage the pandemic. He has authored publications in interdisciplinary journals like Nature Biomedical Engineering\, Science Translational Medicine\, Nature Communications as well as Computer Science and Engineering venues like MobiSys\, MobiCom\, SIGCOMM\, SIGGRAPH Asia and UIST.
URL:https://datascience.ucsd.edu/event/justin-chan/
LOCATION:SDSC\, The Auditorium\, 9836 Hopkins Dr\, La Jolla\, San Diego\, CA\, United States
CATEGORIES:Guest Lecture,Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230410T120000
DTEND;TZID=America/Los_Angeles:20230410T130000
DTSTAMP:20260607T093544
CREATED:20230406T192237Z
LAST-MODIFIED:20230407T165127Z
UID:10000375-1681128000-1681131600@datascience.ucsd.edu
SUMMARY:Language Models and Human Language Acquisition
DESCRIPTION: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.\n \nBio: Alex Warstadt is a postdoc in the computer science department at ETH Zürich working with Ryan Cotterell. In 2022\, he completed a PhD in linguistics at New York University supervised by Sam Bowman. Alex works on a variety of topics at the intersection of natural language processing and linguistics\, including language model pretraining\, evaluation and interpretability\, language acquisition\, and pragmatics.
URL:https://datascience.ucsd.edu/event/language-models-and-human-language-acquisition/
LOCATION:SDSC\, The Auditorium\, 9836 Hopkins Dr\, La Jolla\, San Diego\, CA\, United States
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://datascience.ucsd.edu/wp-content/uploads/2023/04/Alex-Warstadt-e1680886271585.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230411T140000
DTEND;TZID=America/Los_Angeles:20230411T153000
DTSTAMP:20260607T093544
CREATED:20230302T000628Z
LAST-MODIFIED:20240402T224727Z
UID:10000351-1681221600-1681227000@datascience.ucsd.edu
SUMMARY:Responsible AI: Privacy and Fairness in Decision and Learning Systems
DESCRIPTION: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. \nIn 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. \nBio: Ferdinando Fioretto is an assistant professor at Syracuse University. He works at the juncture of Machine Learning\, optimization\, privacy\, and ethics focusing on two themes: (1) Responsible AI: it analyzes the equity of AI systems in support of decision-making and learning tasks and designs algorithms that better align with societal values and (2) ML for Science and Engineering: it develops the foundation to blend deep learning with mathematical optimization to enable the integration of knowledge\, constraints\, and physical principles into learning models. \nHe is a recipient of the 2022 NSF CAREER award\, the 2022 Amazon Research Award\, the 2022 Google Research Scholar Award\, the 2022 Caspar Bowden PET award\, the 2021 ISSNAF Mario Gerla Young Investigator Award\, the 2021 ACP Early Career Researcher Award\, the 2017 AI*AI Best AI dissertation award\, and several best paper awards. He is also actively involved in the organization of several events\, including the Privacy-Preserving Artificial Intelligence workshop at AAAI\, the Algorithmic Fairness through the lens of Causality and Privacy at NeurIPS\, and the Optimization and Learning in multiagent systems workshop at AAMAS.
URL:https://datascience.ucsd.edu/event/ferdinando-nando-fioretto/
LOCATION:SDSC\, The Auditorium\, 9836 Hopkins Dr\, La Jolla\, San Diego\, CA\, United States
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://datascience.ucsd.edu/wp-content/uploads/2023/03/Ferdinando-Fioretto-e1680886080944.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230412T140000
DTEND;TZID=America/Los_Angeles:20230412T153000
DTSTAMP:20260607T093544
CREATED:20230407T173252Z
LAST-MODIFIED:20230407T173252Z
UID:10000379-1681308000-1681313400@datascience.ucsd.edu
SUMMARY:Decoding Nature's Message Through the Channel of Artificial Intelligence
DESCRIPTION: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.\n\nBio: Aobo Li received his B.S. in physics at the University of Washington in 2015\, then did his graduate work at Boston University as part of the KamLAND-Zen collaboration. After getting his Ph.D. in 2020\, Aobo joined UNC Chapel Hill as a Postdoctoral Research Associate and COSMS Fellow. He initiates and leads the Ge Machine Learning (GeM) group\, bringing AI solutions to the LEGEND and the Majorana Demonstrator experiment. Aobo has received many awards\, including the American Physical Society 2023 Dissertation Award in Nuclear Physics\, the UNC Postdoctoral Award of Research Excellence\, and the NeurIPS 2022 ML4PS Workshop Outstanding Paper Award.
URL:https://datascience.ucsd.edu/event/decoding-natures-message-through-the-channel-of-artificial-intelligence/
LOCATION:SDSC\, The Synthesis Center\, 9500 Gilman Drive\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230413T170000
DTEND;TZID=America/Los_Angeles:20230413T180000
DTSTAMP:20260607T093544
CREATED:20230407T171647Z
LAST-MODIFIED:20230407T171857Z
UID:10000376-1681405200-1681408800@datascience.ucsd.edu
SUMMARY:Imaging and Informatics in ROP
DESCRIPTION: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. \nBio: \nDr. Campbell is the Edwin and Josephine Knowles Professor of Ophthalmology at the Casey Eye Institute\, Oregon Health & Science University. He has a clinical focus on adult and pediatric vitreoretinal surgery\, and is a translational clinician scientist broadly focused on imaging in pediatric vitreoretinal disease. Specifically\, he has been actively involved in two main research areas: the development of artificial intelligence (AI) algorithms in retinopathy of prematurity (ROP)\, and optical coherence tomography (OCT) for pediatric retina.  Dr. Campbell is the PI for the Imaging and Informatics in ROP (i-ROP) research consortium\, previously led by Michael Chiang (now director of the National Eye Institute). He has also been a close collaborator with the Center for Ophthalmic Optics & Lasers [COOL Lab] headed by David Huang\, MD at OHSU. He has received funding from the US Agency for International Development and the Seva Foundation for collaborative work with the Aravind Eye Institute implementing and evaluating AI technology for ROP screening in low and middle income countries. Dr. Campbell has published more than 150 peer-reviewed articles\, and is the recipient of a Career Development Award from Research to Prevent Blindness. He was recently a member of the 3rd International Classification of Retinopathy of Prematurity Committee\, and is the Chair of the American Academy of Ophthalmology Committee on Artificial Intelligence. \nIf you would like to be added to the mailing list for the UCSD Ophthalmology Informatics and Data Science Seminar Series please email vpatronilo@health.ucsd.edu \nZoom Link: https://uchealth.zoom.us/j/83927612329?pwd=SFBnTllsWERRclRjVENPWkZxV2VEUT09 \nMeeting ID:  839 2761 2329
URL:https://datascience.ucsd.edu/event/imaging-and-informatics-in-rop/
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230414T120000
DTEND;TZID=America/Los_Angeles:20230414T140000
DTSTAMP:20260607T093544
CREATED:20230403T223545Z
LAST-MODIFIED:20230407T172438Z
UID:10000372-1681473600-1681480800@datascience.ucsd.edu
SUMMARY:The Interplay of Technology\, Ethics\, and Policy
DESCRIPTION: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 different conceptions of fairness—abound in many technologies that are now achieving great scale in commonly used tech platforms. The decisions made by the people inside the companies deploying those technologies impose their value choices upon millions of users\, often with negative externalities that are now on full display.\n\n\nIn Reich’s work with policy experts and technologists (particularly “System Error: Where Big Tech Went Wrong and How We Can Reboot”)\, Reich tries to provide a multidisciplinary view—the perspectives of a philosopher\, a political scientist\, and a computer scientist\, respectively—to disentangle the systematic drivers that we believe have led to the ethical reckoning that Big Tech is now facing. Reich examines the value trade-offs arising in systems for algorithmic decision-making\, questions related to data gathering and privacy\, the impacts of AI and automation\, and the power of private platforms to control our information eco-system. Reich then discusses the ways we can all play a role in helping to shape technology and the policies that govern it with an eye toward achieving better outcomes for society. Case studies will be used to engage the audience in the conversation.\n\n\nBio: Rob Reich is the Professor of Political Science\, director of the Center for Ethics in Society\, co-director of the Center on Philanthropy and Civil Society\, and associate director of the Institute for Human-Centered AI. He is the author of “System Error: Where Big Tech Went Wrong and How We Can Reboot” (with Mehran Sahami and Jeremy M. Weinstein) and “Just Giving: Why Philanthropy is Failing Democracy and How It Can Do Better” (2018); “Digital Technology and Democratic Theory” (edited with Lucy Bernholz and Hélène Landemore\, 2021). His teaching and writing these days focuses on ethics\, policy\, and technology.\n\nThe meeting will be held in person at PEB 721\, on the 7th floor of the UC San Diego Social Sciences Public Engagement Building. Lunch will be served. Vegan\, vegetarian\, and gluten-free options will be available. Kindly RSVP by Apr. 12 at 2 p.m. if you are planning to attend (limited number of seats available!).\n\nRSVP
URL:https://datascience.ucsd.edu/event/the-interplay-of-technology-ethics-and-policy/
LOCATION:Public Engagement Building (PEB) 721\, 9625 Scholars Drive North MC 0305\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Guest Lecture
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230417T140000
DTEND;TZID=America/Los_Angeles:20230417T153000
DTSTAMP:20260607T093544
CREATED:20230413T234714Z
LAST-MODIFIED:20230413T234959Z
UID:10000380-1681740000-1681745400@datascience.ucsd.edu
SUMMARY:Beyond classification: using Machine Learning to probe new physics with the ATLAS experiment in “impossible” final states
DESCRIPTION:Abstract: Although the discovery of the Higgs Boson is often referred to as the completion of the Standard Model of Particle Physics\, the many outstanding mysteries of our universe indicate that some unknown new physics is awaiting discovery. Machine learning has played an increasingly critical role in searching for this new physics\, typically by better separating a physical process of interest (signal) from other Standard Model processes producing similar detector signatures (background). However\, we can also cleverly utilize machine learning to better understand these background processes\, opening up “impossible” regions of data for analysis. In this talk\, I will present two examples of analyses from the ATLAS experiment utilizing machine learning to tackle especially challenging backgrounds. I will also discuss how future advances in machine learning in both data analysis and particle detector hardware will continue to open new avenues for probing for new physics.\n\n\nBio: Dr. Rachel Hyneman is currently a postdoctoral researcher working with Dr. Michael Kagan at SLAC National Accelerator Laboratory\, where she studies physics at the smallest scales as part of the ATLAS Experiment at CERN. Her research has focused on taking advantage of machine learning techniques to search for evidence of new physics hiding in the behavior of the Higgs Boson\, as well as the developing the construction procedure and readout of the upgraded ATLAS Inner Tracker detector for the High-Luminosity LHC program. She earned her PhD in physics from the University of Michigan\, Ann Arbor\, under the supervision of Dr. Tom Schwarz. Prior to her graduate studies\, she earned her bachelors degree in physics with a minor in music from the College of William and Mary in Virginia. Outside of physics\, Rachel enjoys playing double bass and venturing to mountains for hiking and skiing.”\n\nZoom Info: http://bit.ly/HDSI-Seminars
URL:https://datascience.ucsd.edu/event/beyond-classification-using-machine-learning-to-probe-new-physics-with-the-atlas-experiment-in-impossible-final-states/
LOCATION:SDSC\, The Auditorium\, 9836 Hopkins Dr\, La Jolla\, San Diego\, CA\, United States
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230419T100000
DTEND;TZID=America/Los_Angeles:20230419T130000
DTSTAMP:20260607T093544
CREATED:20230403T185835Z
LAST-MODIFIED:20230407T172914Z
UID:10000371-1681898400-1681909200@datascience.ucsd.edu
SUMMARY:Chatting GPT
DESCRIPTION: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 a surprising level of capability\, with the most recent iterations able to write essays\, poems\, and computer code\, and score near the 90th percentile on standardized tests such as the LSAT and the Math SAT. The most popular interface to this technology\, ChatGPT\, made the power of LLMs readily-available to the general public for the first time\, and in doing so became the fastest-growing consumer application in history. It is clear that ChatGPT and other LLMs will have major impacts on how we work\, learn\, and live — and there is a sense that we have only seen the tip of the iceberg in terms of what these technologies can do. \nIn this series of talks and panels\, targeted to the campus community and open to the general public\, UCSD experts will discuss ChatGPT and other generative artificial intelligence: What is it? How does it work? What are its ethical implications? And what impacts will it have on fields such as medicine\, business\, and education?
URL:https://www.sdsc.edu/event_items/202304-ChatGPT.html
LOCATION:SDSC\, The Auditorium\, 9836 Hopkins Dr\, La Jolla\, San Diego\, CA\, United States
CATEGORIES:Symposium,Webinar
ATTACH;FMTTYPE=image/png:https://datascience.ucsd.edu/wp-content/uploads/2023/04/UCSD-Lecture-Template_Chatting-GPT-e1680886379636.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230425T140000
DTEND;TZID=America/Los_Angeles:20230425T140000
DTSTAMP:20260607T093544
CREATED:20230424T213937Z
LAST-MODIFIED:20230424T214101Z
UID:10000381-1682431200-1682431200@datascience.ucsd.edu
SUMMARY:Leveraging Simulators for ML Inference in Particle Physics
DESCRIPTION: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.\nBio: Dr. Aishik Ghosh is a postdoctoral scholar at UC Irvine and Berkeley National Lab where he develops innovative machine learning solutions for particle physics\, and is part of the ATLAS collaboration. He earned his Ph.D. from University of Paris-Saclay where he developed the first deep generative models for fast calorimeter simulation in the ATLAS experiment. Since then he has worked on several topics at the intersection of ML and uncertainty quantification and uncertainty mitigation\, including applications in astrophysics\, as well as generative models for physics simulation. Recently\, he has been working on reinforcement learning methods for particle physics. Dr. Ghosh has fostered interdisciplinary collaborations within academia and with industry. He has contributed to a book on Artificial Intelligence for High Energy Physics and organises ML training schools for graduate students. Dr. Ghosh consults on AI policy with international organisations like the OECD\, with whom he has published writings on Trustworthy AI and AI for Science\, and has given interviews to organisations like The Royal Society\,
URL:https://datascience.ucsd.edu/event/leveraging-simulators-for-ml-inference-in-particle-physics/
LOCATION:SDSC\, The Auditorium\, 9836 Hopkins Dr\, La Jolla\, San Diego\, CA\, United States
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230503T140000
DTEND;TZID=America/Los_Angeles:20230503T153000
DTSTAMP:20260607T093544
CREATED:20230501T161933Z
LAST-MODIFIED:20230501T161933Z
UID:10000382-1683122400-1683127800@datascience.ucsd.edu
SUMMARY:Security and Privacy in an Everchanging System Landscape
DESCRIPTION: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. \nBio: Amir Rahmati is an Assistant Professor in the Department of Computer Science at Stony Brook University\, where he leads the Ethos Security & Privacy lab. He received his Ph.D. in Computer Science & Engineering from the University of Michigan in 2017. His research focuses on understanding emerging threats in computer systems and building practical solutions that can tackle their security and privacy challenges. His work has resulted in tens of publications and patents\, as well as thousands of citations. Rahmati’s research is supported by the Air Force Office of Scientific Research (AFOSR)\, Office of Naval Research (ONR)\, Meta\, and IBM. His research has received frequent attention from media outlets\, including MIT Technology Review\, Washington Post\, and Bloomberg. His work on the security of autonomous driving systems is part of the permanent display at the London Science Museum.
URL:https://datascience.ucsd.edu/event/security-and-privacy-in-an-everchanging-system-landscape/
LOCATION:SDSC\, The Auditorium\, 9836 Hopkins Dr\, La Jolla\, San Diego\, CA\, United States
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230510T140000
DTEND;TZID=America/Los_Angeles:20230510T153000
DTSTAMP:20260607T093544
CREATED:20230509T195847Z
LAST-MODIFIED:20230510T170811Z
UID:10000385-1683727200-1683732600@datascience.ucsd.edu
SUMMARY:Proximal MCMC for Bayesian Inference of Constrained and Regularized Estimation
DESCRIPTION: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.
URL:https://datascience.ucsd.edu/event/proximal-mcmc-for-bayesian-inference-of-constrained-and-regularized-estimation/
LOCATION:SDSC\, The Auditorium\, 9836 Hopkins Dr\, La Jolla\, San Diego\, CA\, United States
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230511T120000
DTEND;TZID=America/Los_Angeles:20230511T140000
DTSTAMP:20260607T093544
CREATED:20230403T224301Z
LAST-MODIFIED:20230407T172325Z
UID:10000373-1683806400-1683813600@datascience.ucsd.edu
SUMMARY:Just Opt Out? Lessons Learned From a Decade of Evasion
DESCRIPTION: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 in our daily lives. But can we truly walk away from these systems? And what do we learn when we do? In this talk\, Vertesi tells the zany stories and practical tips that emerged from my extreme experiments in living digitally off Big Tech’s grid. Vertesi uncovers the sociological mechanisms that fuel these companies’ effective monetization of our lives and shares the hard-won tools and fresh insights Vertesi developed to help us all disable toxic tech and restore our right to choose.\n\nBio: Dubbed “Margaret Mead among the Starfleet” in the Times Literary Supplement\, Janet Vertesi is an associate professor of Sociology at Princeton University. She has spent fifteen years embedded with NASA’s robotic spacecraft teams as a sociologist of science and technology. Her publications range from the books Shaping Science and Seeing Like a Rover (both University of Chicago Press)\, edited collections digitalSTS (Princeton Press) and Representation in Scientific Practice Revisited (MIT Press)\, and top ranked journals and conference proceedings in the fields of the sociology of science and technology\, and human-computer and human-robot interaction. Currently co-editor of MIT Press’ Infrastructures series\, Vertesi is well known for her “Opt Out Experiments” evading capture in the personal data economy\, including a famous obfuscated pregnancy and trip to Disneyland. More at http://janet.vertesi.comand https://optoutproject.net\n\nThe meeting will be held in person at PEB 721\, on the 7th floor of the UC San Diego Social Sciences Public Engagement Building. Lunch will be served. Vegan\, vegetarian\, and gluten-free options will be available. Kindly RSVP by May 9 at 2 p.m. if you are planning to attend (limited number of seats available!).\n\nRSVP here
URL:https://datascience.ucsd.edu/event/just-opt-out-lessons-learned-from-a-decade-of-evasion/
LOCATION:Public Engagement Building (PEB) 721\, 9625 Scholars Drive North MC 0305\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Guest Lecture
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230517T120000
DTEND;TZID=America/Los_Angeles:20230517T133000
DTSTAMP:20260607T093544
CREATED:20230512T225134Z
LAST-MODIFIED:20230512T234954Z
UID:10000386-1684324800-1684330200@datascience.ucsd.edu
SUMMARY:Earth & Ocean Image Processing Made Easy with MATLAB - Lunch and Learn
DESCRIPTION:In our continued collaboration with UCSD\, we are pleased to announce our spring seminar topic! 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. \n\n\n\n\nBring your questions about the landscape of computational tools and topics such as managing data\, climate change\, image processing\, and more! This session will also include a section on career paths for engineers and scientists – our engineers will share a bit about their personal education and career trajectory – and what technical skills can support your own career paths! \nFollow this link to register as seats are limited! Lunch will be provided. \n  \n\n\n\n\nEarth & Ocean Image Processing Made Easy with MATLAB \nThere is a very rich and sophisticated ecosystem today for advanced image processing\, ranging from complex computer vision techniques to AI/machine learning applications. MATLAB is a popular tool used by research and development engineers in many imaging applications. It’s used for a variety of tasks from analyzing\, enhancing\, and visualizing images to developing advanced imaging algorithms deployed on PCs\, embedded systems\, and the cloud. \nThis session explores the basics of pixel-level image processing and high-level machine learning models in MATLAB for images. Some of the tasks we will work through include: \n\nImporting various image and Landsat files\nNew Apps in MATLAB for Imaging: Introduction to apps and features to simplify image dataexploration\, processing\, visualization\, and algorithm development\nBasics of machine learning models for image classification\nProcess\, analyze data\, and map data\, creating .shp (GIS) files and publication-ready figures.\n\n  \nPresented by: Laura Sammon \n\n\n\nLaura is a Customer Success Engineer at MathWorks. She supports teaching and research across science and engineering disciplines\, specializing in coding applications for Earth and ocean science. Laura earned her Ph.D. in Geology from the University of Maryland where she studied the composition of Earth’s crust and interior through geochemical and geophysical data.
URL:https://datascience.ucsd.edu/event/earth-ocean-image-processing-made-easy-with-matlab-lunch-and-learn/
LOCATION:Vaughan Hall\, Room 100\, (UCSD-SIO) 8629 Kennel Way\, La Jolla\, CA\, 92037\, United States
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230518T120000
DTEND;TZID=America/Los_Angeles:20230518T140000
DTSTAMP:20260607T093544
CREATED:20230512T234520Z
LAST-MODIFIED:20230512T235340Z
UID:10000387-1684411200-1684418400@datascience.ucsd.edu
SUMMARY:UC San Diego & MathWorks | Research & Curriculum Micro Symposium
DESCRIPTION: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! \nJoin us for lunch and lightning talks: \n\nWe will have speakers from JSOE\, HDSI and Scripps who will share their MATLAB uses in teaching\, projects and research.\nThe opportunity to talk to the MathWorks team about the tools they provide – learn about what’s new and ask your questions here!\nAn opportunity to network with your fellow UC San Diego colleagues from other departments!\n\n\n\n\n\nRegister Here – seats are limited and lunch will be provided! \nLocation: Qualcomm Conference Center at JSOE\nTime: May 18th\, 2023\, 12pm – 2pm \n\n\n\n\n  \nADDITIONAL RESOURCES: \nLearn about job openings/career opportunities here  \nVisit MathWorks/UC San Diego collaboration website
URL:https://datascience.ucsd.edu/event/uc-san-diego-mathworks-research-curriculum-micro-symposium/
LOCATION:Qualcomm Conference Room at JSOE\, Jacobs Hall\, 9736 Engineers Ln\, La Jolla\, San Diego\, CA\, 92093\, United States
CATEGORIES:Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230518T140000
DTEND;TZID=America/Los_Angeles:20230518T150000
DTSTAMP:20260607T093544
CREATED:20230323T181111Z
LAST-MODIFIED:20230513T000759Z
UID:10000364-1684418400-1684422000@datascience.ucsd.edu
SUMMARY:Scaling and Generalizing Approximate Bayesian Inference | David Blei
DESCRIPTION:Abstract: 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. It tends to be faster than more traditional methods\, such as Markov chain Monte Carlo sampling. \nAfter quickly reviewing the basics\, I will discuss two lines of research in VI. I first describe stochastic variational inference\, an approximate inference algorithm for handling massive datasets\, and demonstrate its application to probabilistic topic models of millions of articles. Then I discuss black box variational inference\, a generic algorithm for approximating the posterior. Black box inference easily applies to many models but requires minimal mathematical work to implement. I will demonstrate black box inference on deep exponential families—a method for Bayesian deep learning—and describe how it enables powerful tools for probabilistic programming. \n  \nBio: David Blei is a Professor of Statistics and Computer Science at Columbia University\, and a member of the Columbia Data Science\nInstitute. He studies probabilistic machine learning\, including its theory\, algorithms\, and application. David has received several awards for his research. He received a Sloan Fellowship (2010)\, Office of Naval Research Young Investigator Award (2011)\, Presidential Early Career Award for Scientists and Engineers (2011)\, Blavatnik Faculty Award (2013)\, ACM-Infosys Foundation Award (2013)\, a Guggenheim fellowship (2017)\, and a Simons Investigator Award (2019). He is the co-editor-in-chief of the Journal of Machine Learning Research. He is a fellow of the ACM and the IMS. \nWebsite : http://www.cs.columbia.edu/~blei/ \n  \nZoom Link : http://bit.ly/HDSI-Seminars
URL:https://datascience.ucsd.edu/event/david-blei/
LOCATION:SDSC\, The Auditorium\, 9836 Hopkins Dr\, La Jolla\, San Diego\, CA\, United States
CATEGORIES:Colloquium,Guest Lecture
ATTACH;FMTTYPE=image/jpeg:https://datascience.ucsd.edu/wp-content/uploads/2023/03/professordavisblei_headshot-scaled.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230519T120000
DTEND;TZID=America/Los_Angeles:20230519T140000
DTSTAMP:20260607T093544
CREATED:20230403T224454Z
LAST-MODIFIED:20230407T172236Z
UID:10000374-1684497600-1684504800@datascience.ucsd.edu
SUMMARY:Beyond 'The Algorithm': Fields\, Drama\, and Extreme Content Among Vegan Influencers
DESCRIPTION:Abstract: Existing research on polarization on social media platforms emphasizes the role of algorithmic “filter bubbles” and platform failure in amplifying extreme attitudes among online audiences. This article provides a different approach by focusing on online creators rather than audiences. Christin adapts field theory to examine the dynamics structuring exchanges between social media influencers\, which she analyzes as contentious position-takings within fields created and mediated by social media platforms. To demonstrate the relevance of this framework\, Christin draws on a qualitative study of vegan influencers on YouTube and Instagram. Two pathways shape the structuration of fields of social media production: drama\, or highly publicized scandals and interpersonal conflicts between influencers; and extreme content\, in which influencers and users reinforce their shared worldviews through niche and inflammatory content. Christin concludes by discussing the relevance of field theory for the study of social media and online disinformation more broadly.\n\n\nBiography: Angèle Christin is an assistant professor in the Department of Communication and affiliated faculty in the Sociology Department\, the Program in Science\, Technology\, and Society\, and the Center for Work\, Technology\, and Organization at Stanford University. She studies how algorithms and analytics transform professional values\, expertise\, and work practices.\n\n\nThe meeting will be held in person at PEB 721\, on the 7th floor of the UC San Diego Social Sciences Public Engagement Building. Lunch will be served. Vegan\, vegetarian\, and gluten-free options will be available. Kindly RSVP by May 17 at 2 p.m. if you are planning to attend (limited number of seats available!).\n\nRSVP here
URL:https://datascience.ucsd.edu/event/beyond-the-algorithm-fields-drama-and-extreme-content-among-vegan-influencers/
LOCATION:Public Engagement Building (PEB) 721\, 9625 Scholars Drive North MC 0305\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Guest Lecture
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230522T130000
DTEND;TZID=America/Los_Angeles:20230522T143000
DTSTAMP:20260607T093544
CREATED:20230523T143630Z
LAST-MODIFIED:20230523T143643Z
UID:10000388-1684760400-1684765800@datascience.ucsd.edu
SUMMARY:Algorithms for multi-group learning
DESCRIPTION: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.
URL:https://datascience.ucsd.edu/event/algorithms-for-multi-group-learning/
LOCATION:SDSC\, The Synthesis Center\, 9500 Gilman Drive\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Colloquium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230524T120000
DTEND;TZID=America/Los_Angeles:20230524T150000
DTSTAMP:20260607T093544
CREATED:20230503T030808Z
LAST-MODIFIED:20230503T030808Z
UID:10000383-1684929600-1684940400@datascience.ucsd.edu
SUMMARY:Housing Fair : A Community Effort
DESCRIPTION:
URL:https://datascience.ucsd.edu/event/housing-fair-a-community-effort/
LOCATION:Library Walk
CATEGORIES:Mixer,Social Event
ATTACH;FMTTYPE=image/jpeg:https://datascience.ucsd.edu/wp-content/uploads/2023/05/OPTION-1-IG-e1683083080841.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230524T140000
DTEND;TZID=America/Los_Angeles:20230524T150000
DTSTAMP:20260607T093544
CREATED:20230323T181207Z
LAST-MODIFIED:20240402T224726Z
UID:10000365-1684936800-1684940400@datascience.ucsd.edu
SUMMARY:Spectral clustering in high-dimensional Gaussian mixture block models
DESCRIPTION:The Gaussian mixture block model is a simple generative model for networks: to generate a sample\, we associate each node with a latent feature vector sampled from a mixture of Gaussians\, and we add an edge between nodes if and only if their feature vectors are sufficiently similar. The different components of the Gaussian mixture represent the fact that there may be several types of nodes with different distributions over features — for example\, in a social network each component represents the different attributes of a distinct community. In this talk I will discuss recent results on the performance of spectral clustering algorithms on networks sampled from high-dimensional Gaussian mixture block models\, where the dimension of the latent feature vectors grows as the size of the network goes to infinity. Our results merely begin to sketch out the information-computation landscape for clustering in these models\, and I will make an effort to emphasize open questions.\nBased on joint work with Shuangping Li.
URL:https://datascience.ucsd.edu/event/tselil-schramm/
LOCATION:SDSC\, The Synthesis Center\, 9500 Gilman Drive\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Colloquium
END:VEVENT
END:VCALENDAR