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DTSTART;TZID=America/Los_Angeles:20230406T110000
DTEND;TZID=America/Los_Angeles:20230406T123000
DTSTAMP:20260606T133334
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:20260606T133334
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:20260606T133334
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:20260606T133334
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:20260606T133334
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:20260606T133334
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/
LOCATION:CA
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230414T120000
DTEND;TZID=America/Los_Angeles:20230414T140000
DTSTAMP:20260606T133335
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:20260606T133335
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:20260606T133335
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:20260606T133335
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:20260606T133335
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:20260606T133335
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:20260606T133335
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:20260606T133335
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:20260606T133335
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:20260606T133335
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:20260606T133335
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:20260606T133335
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:20260606T133335
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:20260606T133335
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
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230530T140000
DTEND;TZID=America/Los_Angeles:20230530T153000
DTSTAMP:20260606T133335
CREATED:20230530T153018Z
LAST-MODIFIED:20230530T153018Z
UID:10000389-1685455200-1685460600@datascience.ucsd.edu
SUMMARY:Representation Learning: A Causal Perspective
DESCRIPTION:Abstract: Representation learning constructs low-dimensional representations to summarize essential features of high-dimensional data like images and texts. Ideally\, such a representation should efficiently capture non-spurious features of the data. It shall also be disentangled so that we can interpret what feature each of its dimensions captures. However\, these desiderata are often intuitively defined and challenging to quantify or enforce. \nIn this talk\, we take on a causal perspective of representation learning. We show how desiderata of representation learning can be formalized using counterfactual notions\, enabling metrics and algorithms that target efficient\, non-spurious\, and disentangled representations of data. We discuss the theoretical underpinnings of the algorithm and illustrate its empirical performance in both supervised and unsupervised representation learning.
URL:https://datascience.ucsd.edu/event/representation-learning-a-causal-perspective/
LOCATION:SDSC\, The Auditorium\, 9836 Hopkins Dr\, La Jolla\, San Diego\, CA\, United States
CATEGORIES:Colloquium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230531T140000
DTEND;TZID=America/Los_Angeles:20230531T153000
DTSTAMP:20260606T133335
CREATED:20230530T153213Z
LAST-MODIFIED:20230530T153213Z
UID:10000390-1685541600-1685547000@datascience.ucsd.edu
SUMMARY:On the complexity of Frank-Wolfe methods
DESCRIPTION:Abstract: Frank-Wolfe methods are popular for optimization over a polytope. One of the reasons is because they do not need projection onto the polytope but only linear optimization over it. This talk has two parts. \nThe first part will be about the complexity of Wolfe’s method\, an algorithm closely related to Frank-Wolfe methods. In 1974 Phillip Wolfe proposed a method to find the minimum Euclidean-norm point in a convex polyhedron. The method is essentially the same as the Lawson-Hanson algorithm for non-negative least squares. The complexity of Wolfe’s method has remained unknown since he proposed it. The method is important because it is used as a subroutine for one of the most practical algorithms for submodular function minimization. We present the first example that Wolfe’s method takes exponential time. Additionally\, we improve previous results to show that linear programming reduces in strongly-polynomial time to the minimum norm point problem over a simplex. \nThe second part will be about the smoothed complexity of Frank-Wolfe methods. To understand their complexity\, a fruitful approach in many\nworks has been the use of condition measures of polytopes. Lacoste-Julien and Jaggi introduced a condition number for polytopes and showed linear convergence for several variations of the method. The actual running time can still be exponential in the worst case (when the condition number is exponential). We study the smoothed complexity of the condition number\, namely the condition number of small random perturbations of the input polytope and show that it is polynomial for any simplex and exponential for general polytopes. Our argument for polytopes is a refinement of an argument that we develop to study the conditioning of random matrices. The basic argument shows that for c > 1\, a d-by-n random Gaussian matrix with n >= cd has a d-by-d submatrix with minimum singular value that is exponentially small with high probability. This also has consequences on known results about the robust uniqueness of tensor decompositions\, the complexity of the simplex method and the diameter of polytopes.
URL:https://datascience.ucsd.edu/event/on-the-complexity-of-frank-wolfe-methods/
LOCATION:SDSC\, The Auditorium\, 9836 Hopkins Dr\, La Jolla\, San Diego\, CA\, United States
CATEGORIES:Colloquium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230602T100000
DTEND;TZID=America/Los_Angeles:20230602T120000
DTSTAMP:20260606T133335
CREATED:20230505T060339Z
LAST-MODIFIED:20230505T060339Z
UID:10000384-1685700000-1685707200@datascience.ucsd.edu
SUMMARY:HDSI 2023 Undergraduate Scholarship Showcase
DESCRIPTION:The Halıcıoğlu Data Science Institute is preparing to host our annual Undergraduate Scholarship Showcase. We invite you to join our scholarship cohort in an interactive presentation of the projects they have worked on for the past academic year. \n> View Project List Here < \nWe will be sending the RSVP form with the Zoom meeting link within the next week. Please be sure to monitor your email for additional updates\, and reach out to us at dscstudent@ucsd.edu if you have any questions or concerns.
URL:https://datascience.ucsd.edu/event/hdsi-2023-undergraduate-scholarship-showcase/
LOCATION:CA
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230605T080000
DTEND;TZID=America/Los_Angeles:20230605T170000
DTSTAMP:20260606T133335
CREATED:20230613T210741Z
LAST-MODIFIED:20230613T213829Z
UID:10000393-1685952000-1685984400@datascience.ucsd.edu
SUMMARY:5-Year Anniversary Celebration & Ribbon Cutting
DESCRIPTION:On June 5th\, 2023 the Halıcıoğlu Data Science Institute celebrated the 5-year anniversary since becoming its own academic department. In parallel to these celebrations\, HDSI unveiled it’s first dedication space\, the Halıcıoğlu Data Science Institute building. \n  \n[ngg src=”galleries” ids=”1″ display=”basic_thumbnail” thumbnail_crop=”0″ images_per_page=”15″] 
URL:https://datascience.ucsd.edu/event/5-year-anniversary-celebration-ribbon-cutting/
LOCATION:3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:HDSI Event
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230606T130000
DTEND;TZID=America/Los_Angeles:20230606T140000
DTSTAMP:20260606T133335
CREATED:20230601T065512Z
LAST-MODIFIED:20230601T070105Z
UID:10000391-1686056400-1686060000@datascience.ucsd.edu
SUMMARY:Samuel Lau | Instructor-Centered Design of Tools to Support Teaching Programming and Data Science At Scale
DESCRIPTION:Abstract \nInstructors of technical subjects like programming and data science use a wide array of software tools that enable them to create sophisticated and engaging lessons at scale. Although there are many such tools available\, instructors often find themselves repurposing software originally designed for other people\, like professional software engineers. This mismatch of intent adds extra logistical complexity to the already-challenging task of designing and delivering effective learning content. \nTo address these issues\, this dissertation takes an instructor-centered approach. It surfaces previously unmet needs through studies of instructors\, their goals\, and their software tools. The key findings are that instructors constantly seek to update their learning materials\, yet encounter heavy logistical challenges in doing so because the tools they use to help design their lessons were not intended for instructional use. \nThis dissertation also contributes novel interactive systems that directly support teaching by designing for instructor needs. In particular\, this dissertation contributes program visualization tools that enable instructors to show how code transforms data: TweakIt helps learners work with unfamiliar code snippets\, and the Pandas/Tidy Data/SQL Tutors automatically visualize code that manipulates data tables step-by-step. Together\, this dissertation provides the first evidence that the insights gathered from an instructor-centered approach can lead to tools that better support the work of instruction. \n  \nCommittee members: \nPhilip J. Guo\, Chair\, Cognitive Science \nJames D. Hollan\, Cognitive Science \nRanjit Jhala\, Computer Science and Engineering \nBradley Voytek\, Cognitive Science \nHaijun Xia\, Cognitive Science \n. \nDate & Time : Tuesday\, June 6th\, 1:00pm – 2:00pm \nLocation : Design and Innovation Building\, Room 406 or Join with Zoom
URL:https://datascience.ucsd.edu/event/instructor-centered-design-of-tools-to-support-teaching-programming-and-data-science-at-scale-samuel-lau/
LOCATION:Design & Innovation Building\, Room 406
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230609T150000
DTEND;TZID=America/Los_Angeles:20230609T160000
DTSTAMP:20260606T133335
CREATED:20230605T233443Z
LAST-MODIFIED:20230914T164123Z
UID:10000392-1686322800-1686326400@datascience.ucsd.edu
SUMMARY:Deep Latent Variable Models for Compression and Natural Science | Stephan Mandt
DESCRIPTION:Abstract: Latent variable models have been an integral part of probabilistic machine learning\, ranging from simple mixture models to variational autoencoders to powerful diffusion probabilistic models at the center of recent media attention. Perhaps less well-appreciated is the intimate connection between latent variable models and data compression\, and the potential of these models for advancing natural science. This talk will explore these topics. I will begin by showcasing connections between variational methods and the theory and practice of neural data compression. On the applied side\, variational methods lead to machine-learned compressors of data such as images and videos and offer principled techniques for enhancing their compression performance\, as well as reducing their decoding complexity. On the theory side\, variational methods also provide scalable bounds on the fundamental compressibility of real-world data\, such as images and particle physics data. Lastly\, I will also delve into climate science projects\, where a combination of deep latent variable modeling and vector quantization enables assessing distribution shifts induced by varying climate models and the effects of global warming. \nBio: Stephan Mandt is an Associate Professor of Computer Science and Statistics at the University of California\, Irvine. From 2016 until 2018\, he was a Senior Researcher and Head of the statistical machine learning group at Disney Research in Pittsburgh and Los Angeles. He held previous postdoctoral positions at Columbia University and Princeton University. Stephan holds a Ph.D. in Theoretical Physics from the University of Cologne in Germany\, where he received the National Merit Scholarship. He received the NSF CAREER Award\, a Kavli Fellowship of the U.S. National Academy of Sciences\, the German Research Foundation’s Mercator Fellowship\, and the UCI ICS Mid-Career Excellence in Research Award. He is a member of the ELLIS Society and a former visiting researcher at Google Brain. Stephan will serve as Program Chair of the AISTATS 2024 conference\, currently serves as an Action Editor for JMLR and TMLR\, and frequently serves as Area Chair for NeurIPS\, ICML\, AAAI\, and ICLR.
URL:https://datascience.ucsd.edu/event/deep-latent-variable-models-for-compression-and-natural-science-stephan-mandt/
LOCATION:Computer Science & Engineering Building (CSE)\, Room 1202
CATEGORIES:Colloquium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230626T120000
DTEND;TZID=America/Los_Angeles:20230626T130000
DTSTAMP:20260606T133335
CREATED:20230629T160446Z
LAST-MODIFIED:20230629T160646Z
UID:10000396-1687780800-1687784400@datascience.ucsd.edu
SUMMARY:The Emergence of General AI for Medicine | Dr. Peter Lee
DESCRIPTION:Dr. Peter Lee is Corporate Vice President of Research and Incubations at Microsoft where he leads Microsoft Research and incubates new research-powered products and lines of business in areas such as artificial intelligence\, computing foundations\, health\, and life sciences. He speaks and writes widely on science and technology trends\, including the attached NEJM article “Benefits\, Limits\, and Risks of GPT-4 as an AI Chatbot for Medicine” and recently published a book with Dr. Isaac Kohane\, “The AI Revolution in Medicine: GPT-4 and Beyond.” \nBefore joining Microsoft in 2010\, he was at DARPA\, where he established a new technology office that created operational capabilities in machine learning\, data science\, and computational social science. Prior to that\, he was a professor and the head of the computer science department at Carnegie Mellon University. Dr. Lee is a member of the National Academy of Medicine and serves on the Boards of Directors of several institutes for the Allen Institute for Artificial Intelligence\, the Brotman Baty Institute for Precision Medicine\, and the Kaiser Permanente Bernard J. Tyson School of Medicine. He served on President Obama’s Commission on Enhancing National Cybersecurity and led studies for PCAST and the National Academies. He has testified before both the US House Science and Technology Committee and the US Senate Commerce Committee.
URL:https://datascience.ucsd.edu/event/the-emergence-of-general-ai-for-medicine-dr-peter-lee/
LOCATION:SDSC\, The Auditorium\, 9836 Hopkins Dr\, La Jolla\, San Diego\, CA\, United States
CATEGORIES:Guest Lecture
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230805T180000
DTEND;TZID=America/Los_Angeles:20230805T210000
DTSTAMP:20260606T133335
CREATED:20230616T085435Z
LAST-MODIFIED:20230616T170852Z
UID:10000395-1691258400-1691269200@datascience.ucsd.edu
SUMMARY:HDSI Alumni Celebration
DESCRIPTION:If you are a HDSI alumni\, faculty or staff\, you’re invited to join us in celebrating and socializing with HDSI alumni this summer when we gather for our annual celebration!  \nREGISTER HERE \nRegistration is 18$ which includes a food\, a free drink\, and more! \nThe registration deadline is July 28th. \nPlease note that registration is required and that this is a private event for HDSI alumni\, and HDSI faculty & staff only. \n  \nThank you for all that you do in educating so many young professionals. We hope to see you on August 5th! 
URL:https://datascience.ucsd.edu/event/hdsi-alumni-celebration/
LOCATION:Ridgewalk Social\, University of California San Diego\, Rimac Annex\, San Diego\, 92093
CATEGORIES:HDSI Event,Social Event
ATTACH;FMTTYPE=image/png:https://datascience.ucsd.edu/wp-content/uploads/2023/06/hdsi-alumni-celeb-e1686905645343.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230914T100000
DTEND;TZID=America/Los_Angeles:20230914T130000
DTSTAMP:20260606T133336
CREATED:20230818T062040Z
LAST-MODIFIED:20230818T062843Z
UID:10000398-1694685600-1694696400@datascience.ucsd.edu
SUMMARY:Causal Inference symposium
DESCRIPTION:Curious about Causality? \nCausality is increasingly a part of AI\, data science\, robotics\, and more\, but it is not always clear how we can learn causality from data. Halıcıoğlu Data Science Institute (HDSI) will be hosting a Causal Inference symposium featuring leading HDSI Faculty who will be providing an introductory overview on these methods\, followed by domain-specific talks and open discussion. \nWe welcome all UCSD faculty\, graduate students\, HDSI industry partners\, and guests to join us. \nRegister here: https://www.eventbrite.com/e/causality-symposium-tickets-677459017157?aff=oddtdtcreator  \n\n\n\n\nPlease complete your registration by Friday\, Sept. 8\, 2023. \n\n\n\n\n \nEvent details\n\nDate: September 14\, 2023\nTime: 10:00 am\nLocation: Halıcıoğlu Data Science Institute\, Room 123 (Multipurpose Room)\n\n  \nAgenda \n\n9:30 am – 10 am: Check-in & Coffee\n10 am – 10:30 am: Welcome Remarks & Causality Overview – David Danks\n10:30 am – 11:50 am: Faculty Presentations \n\nCausal Inference for Responsible and Reliable Data Science – Babak Salimi\nRobust Causal Inference with Complex Datasets – Jelena Bradic\nDemystifying Neural Networks Through Interpretable Neurons – Lily Weng\nAdvancing Machine Intelligence Through Learning and Using Causal Knowledge – Biwei Huang\n\n\n11:50 am – 12 pm: Break\n12 pm – 1 pm: Discussion Session\n\nLunch will be provided afterwards. 
URL:https://datascience.ucsd.edu/event/causal-inference-symposium/
LOCATION:3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Symposium
ATTACH;FMTTYPE=image/png:https://datascience.ucsd.edu/wp-content/uploads/2023/08/Event-Flyer-1-e1692339619192.png
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DTSTART;TZID=America/Los_Angeles:20231002T140000
DTEND;TZID=America/Los_Angeles:20231002T150000
DTSTAMP:20260606T133336
CREATED:20230919T181539Z
LAST-MODIFIED:20230919T181539Z
UID:10000399-1696255200-1696258800@datascience.ucsd.edu
SUMMARY:Some new results for streaming principal component analysis
DESCRIPTION: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. \nBio: Purnamrita Sarkar is an associate professor of Statistics at the University of Texas at Austin. Their interests are in the intersection of asymptotic statistics\, scalable algorithms and networks and recently on uncertainty estimation for streaming algorithms and resampling methods for networks. Dr. Sarkar is affiliated with the AI institute and EnCORE: Institute for Emerging CORE Methods of Data Science. They were a postdoctoral scholar at the University of California\, Berkeley working on asymptotic theory for network models and the nonparametric bootstrap for big data. Dr\, Sarkar earned their PhD from the Machine Learning Department at Carnegie Mellon University\n 
URL:https://datascience.ucsd.edu/event/some-new-results-for-streaming-principal-component-analysis/
LOCATION:3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Seminar
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