BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Halıcıoğlu Data Science Institute - UC San Diego - ECPv6.16.2//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Halıcıoğlu Data Science Institute - UC San Diego
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:20230411T140000
DTEND;TZID=America/Los_Angeles:20230411T153000
DTSTAMP:20260527T163436
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:20230410T120000
DTEND;TZID=America/Los_Angeles:20230410T130000
DTSTAMP:20260527T163436
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:20230406T140000
DTEND;TZID=America/Los_Angeles:20230406T150000
DTSTAMP:20260527T163436
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:20230406T110000
DTEND;TZID=America/Los_Angeles:20230406T123000
DTSTAMP:20260527T163436
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:20230405T140000
DTEND;TZID=America/Los_Angeles:20230405T153000
DTSTAMP:20260527T163436
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:20230404T140000
DTEND;TZID=America/Los_Angeles:20230404T153000
DTSTAMP:20260527T163436
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:20230403T140000
DTEND;TZID=America/Los_Angeles:20230403T153000
DTSTAMP:20260527T163436
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:20230329T140000
DTEND;TZID=America/Los_Angeles:20230329T153000
DTSTAMP:20260527T163436
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;VALUE=DATE:20230323
DTEND;VALUE=DATE:20230325
DTSTAMP:20260527T163436
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:20230320T140000
DTEND;TZID=America/Los_Angeles:20230320T140000
DTSTAMP:20260527T163436
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:20230316T140000
DTEND;TZID=America/Los_Angeles:20230316T153000
DTSTAMP:20260527T163436
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;TZID=America/Los_Angeles:20230306T150000
DTEND;TZID=America/Los_Angeles:20230306T150000
DTSTAMP:20260527T163436
CREATED:20230302T000631Z
LAST-MODIFIED:20230302T183320Z
UID:10000355-1678114800-1678114800@datascience.ucsd.edu
SUMMARY:Enrique Zuazua | Control and Machine Learning
DESCRIPTION:In this lecture we shall present some recent results on the interplay between control and Machine Learning\, and more precisely\, Supervised Learning and Universal Approximation. We adopt the perspective of the simultaneous or ensemble control of systems of Residual Neural Networks (ResNets). Roughly\, each item to be classified corresponds to a different initial datum for the Cauchy problem of the ResNets\, leading to an ensemble of solutions to be driven to the corresponding targets\, associated to the labels\, by means of the same control. We present a genuinely nonlinear and constructive method\, allowing to show that such an ambitious goal can be achieved\, estimating the complexity of the control strategies. This property is rarely fulfilled by the classical dynamical systems in Mechanics and the very nonlinear nature of the activation function governing the ResNet dynamics plays a determinant role. It allows deforming half of the phase space while the other half remains invariant\, a property that classical models in mechanics do not fulfill. The turnpike property is also analyzed in this context\, showing that a suitable choice of the cost functional used to train the ResNet leads to more stable and robust dynamics. This lecture is inspired in joint work\, among others\, with Borjan Geshkovski (MIT)\, Carlos Esteve (Cambridge)\, Domènec Ruiz-Balet (IC\, London) and Dario Pighin (Sherpa.ai). \nBio \nEnrique Zuazua Iriondo (Eibar\, Basque Country – Spain\, 1961) holds a Chair of Dynamics\, Control and Numerics – Alexander von Humboldt Professorship at FAU- Friedrich–Alexander University\, Erlangen–Nürnberg (Germany). He also holds secondary appointments as Professor of Applied Mathematics (UAM) and Director of CCM – Chair of Computational Mathematics (Deusto). \nHis research in the area of Applied Mathematics covers topics in Partial Differential Equations\, Systems Control\, Numerical Analysis and Machine Learning\, and led to fruitful collaborations in different industrial sectors such as the optimal shape design in aeronautics\, the management of electrical and water distribution networks and the design of recommendation systems. His research had a high impact (h-index 46) and he has mentored a significant number of postdoctoral researchers and coached a wide network of Science managers. \nHe holds a degree in Mathematics from the University of the Basque Country\, and a dual PhD degree from the same university (1987) and the Université Pierre et Marie Curie\, Paris (1988). In 1990 he became Professor of Applied Mathematics at the Complutense University of Madrid\, to later move to UAM in 2001. He has been awarded the Euskadi (Basque Country) Prize for Science and Technology 2006 and the Spanish National Julio Rey Pastor Prize 2007 in Mathematics and Information and Communication Technology\, the Advanced Grants NUMERIWAVES in 2010 and DyCon in 2016 of the European Research Council (ERC) and the SIAM W.T. and Idalia Reid Prize 2022. \nHe is an Honorary member of the of Academia Europaea and Jakiunde\, the Basque Academy of Sciences\, Letters and Humanities\, Doctor Honoris Causa from the Université de Lorraine in France and Ambassador of the Friedrisch-Alexandre University in Erlangen-Nurenberg\, Germany. He was an invited speaker at ICM2006 in the section on Control and Optimization. \nFrom 1999-2002 he was the first Scientific Manager of the Panel for Mathematics within the Spanish National Research Plan and the Founding Scientific Director of the BCAM – Basque Center for Applied Mathematics from 2008-2012. He is also a member of the Scientific Council of a number of international research institutions such as the INSMI-CNRS and CERFACS in France and member of the Editorial Board in some of the leading journals in Applied Mathematics and Control Theory.
URL:https://datascience.ucsd.edu/event/enrique-zuazua/
LOCATION:SDSC\, The Auditorium\, 9836 Hopkins Dr\, La Jolla\, San Diego\, CA\, United States
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://datascience.ucsd.edu/wp-content/uploads/2023/03/enriquezuazuairiondo_headshot.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230306T100000
DTEND;TZID=America/Los_Angeles:20230306T100000
DTSTAMP:20260527T163436
CREATED:20230302T000627Z
LAST-MODIFIED:20230303T163910Z
UID:10000347-1678096800-1678096800@datascience.ucsd.edu
SUMMARY:Towards the Statistically Principled Design of ML Algorithms | Frederic Koehler
DESCRIPTION:What are the optimal algorithms for learning from data? Have we found them already\, or are better ones out there to be discovered? Making these questions precise\, and answering them\, requires taking on the mathematically deep interplay between statistical and computational constraints. It also requires reconciling our theoretical toolbox with surprising new phenomena arising from practice\, which seem to violate conventional rules of thumb regarding algorithm and model design. I will discuss progress along these lines: in terms of designing new algorithms for basic learning problems\, controlling generalization in large statistical models\, and understanding key statistical questions for generative modeling. \nBio: Frederic is currently a Motwani Postdoctoral Fellow in the Department of Computer Science at Stanford University. He was previously a research fellow at the Simons Institute\, and before that received his PHD in Mathematics and Statistics.
URL:https://datascience.ucsd.edu/event/frederic-koehler/
LOCATION:SDSC\, The Auditorium\, 9836 Hopkins Dr\, La Jolla\, San Diego\, CA\, United States
CATEGORIES:Guest Lecture
END:VEVENT
END:VCALENDAR