BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Halıcıoğlu Data Science Institute - UC San Diego - ECPv6.16.3//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-ORIGINAL-URL:https://datascience.ucsd.edu
X-WR-CALDESC:Events for Halıcıoğlu Data Science Institute - UC San Diego
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/Los_Angeles
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:20210314T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20211107T090000
END:STANDARD
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:20220316T100000
DTEND;TZID=America/Los_Angeles:20220316T100000
DTSTAMP:20260606T115836
CREATED:20230313T202204Z
LAST-MODIFIED:20230313T202204Z
UID:10000357-1647424800-1647424800@datascience.ucsd.edu
SUMMARY:TILOS Seminar Series
DESCRIPTION:
URL:https://datascience.ucsd.edu/event/tilos-seminar-series-2/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20220316T100000
DTEND;TZID=America/Los_Angeles:20220316T110000
DTSTAMP:20260606T115836
CREATED:20220315T212733Z
LAST-MODIFIED:20220315T212733Z
UID:10000198-1647424800-1647428400@datascience.ucsd.edu
SUMMARY:TILOS Seminar Series
DESCRIPTION:Please join us for our TILOS Seminar on Wednesday\, March 16\, 2022 at 10:00am PST / 1:00pm EST. \nTitle: The Connections Between Discrete Geometric Mechanics\, Information Geometry\, Accelerated Optimization and Machine Learning \nSpeaker: Melvin Leok\, Department of Mathematics\, University of California\, San Diego \nAbstract: Geometric mechanics describes Lagrangian and Hamiltonian mechanics geometrically\, and information geometry formulates statistical estimation\, inference\, and machine learning in terms of geometry. A divergence function is an asymmetric distance between two probability densities that induces differential geometric structures and yields efficient machine learning algorithms that minimize the duality gap. The connection between information geometry and geometric mechanics will yield a unified treatment of machine learning and structure-preserving discretizations. In particular\, the divergence function of information geometry can be viewed as a discrete Lagrangian\, which is a generating function of a symplectic map\, that arise in discrete variational mechanics. This identification allows the methods of backward error analysis to be applied\, and the symplectic map generated by a divergence function can be associated with the exact time-h flow map of a Hamiltonian system on the space of probability distributions. We will also discuss how time-adaptive Hamiltonian variational integrators can be used to discretize the Bregman Hamiltonian\, whose flow generalizes the differential equation that describes the dynamics of the Nesterov accelerated gradient descent method. \nAbout the Speaker: Melvin Leok is professor of mathematics and co-director of the CSME graduate program at the University of California\, San Diego. His research interests are in computational geometric mechanics\, computational geometric control theory\, discrete geometry\, and structure-preserving numerical schemes\, and particularly how these subjects relate to systems with symmetry. He received his Ph.D. in 2004 from the California Institute of Technology in Control and Dynamical Systems under the direction of Jerrold Marsden. He is a three-time NAS Kavli Frontiers of Science Fellow\, a Simons Fellow in Mathematics\, and has received the DoD Newton Award for Transformative Ideas\, the NSF Faculty Early Career Development (CAREER) award\, the SciCADE New Talent Prize\, the SIAM Student Paper Prize\, and the Leslie Fox Prize (second prize) in Numerical Analysis. He has given plenary talks at Foundations of Computational Mathematics\, NUMDIFF\, and the IFAC Workshop on Lagrangian and Hamiltonian Methods for Nonlinear Control. He serves on the editorial boards of the Journal of Nonlinear Science\, the Journal of Geometric Mechanics\, and the Journal of Computational Dynamics\, and has served on the editorial boards of the SIAM Journal on Control and Optimization\, and the LMS Journal of Computation and Mathematics. \nJoin Zoom Meeting \nhttps://ucsd.zoom.us/j/99334315002 \nMeeting ID: 993 3431 5002
URL:https://datascience.ucsd.edu/event/tilos-seminar-series/
LOCATION:3234 Matthews Ln\, La Jolla\, 92093\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20220328T010000
DTEND;TZID=America/Los_Angeles:20220328T010000
DTSTAMP:20260606T115836
CREATED:20230313T202205Z
LAST-MODIFIED:20230313T202205Z
UID:10000358-1648429200-1648429200@datascience.ucsd.edu
SUMMARY:TILOS Workshop 1
DESCRIPTION:
URL:https://datascience.ucsd.edu/event/tilos-workshop-1-2/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20220328T130000
DTEND;TZID=America/Los_Angeles:20220328T163000
DTSTAMP:20260606T115836
CREATED:20220321T220930Z
LAST-MODIFIED:20220321T220930Z
UID:10000200-1648472400-1648485000@datascience.ucsd.edu
SUMMARY:TILOS Workshop 1
DESCRIPTION:
URL:https://datascience.ucsd.edu/event/tilos-workshop-1/
LOCATION:3234 Matthews Ln\, La Jolla\, 92093\, United States
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20220404T010000
DTEND;TZID=America/Los_Angeles:20220404T010000
DTSTAMP:20260606T115836
CREATED:20230313T202205Z
LAST-MODIFIED:20230313T202205Z
UID:10000359-1649034000-1649034000@datascience.ucsd.edu
SUMMARY:TILOS Workshop 2
DESCRIPTION:
URL:https://datascience.ucsd.edu/event/tilos-workshop-2-2/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20220404T130000
DTEND;TZID=America/Los_Angeles:20220404T163000
DTSTAMP:20260606T115836
CREATED:20220321T221059Z
LAST-MODIFIED:20220321T221059Z
UID:10000202-1649077200-1649089800@datascience.ucsd.edu
SUMMARY:TILOS Workshop 2
DESCRIPTION:
URL:https://datascience.ucsd.edu/event/tilos-workshop-2/
LOCATION:3234 Matthews Ln\, La Jolla\, 92093\, United States
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20220503T160000
DTEND;TZID=America/Los_Angeles:20220503T170000
DTSTAMP:20260606T115837
CREATED:20220503T200559Z
LAST-MODIFIED:20220503T200559Z
UID:10000206-1651593600-1651597200@datascience.ucsd.edu
SUMMARY:Demystifying Graduate Admissions: STEM Programs
DESCRIPTION:To register\, click here.
URL:https://datascience.ucsd.edu/event/demystifying-graduate-admissions-stem-programs/
LOCATION:3234 Matthews Ln\, La Jolla\, 92093\, United States
CATEGORIES:HDSI Event
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20220509T180000
DTEND;TZID=America/Los_Angeles:20220509T193000
DTSTAMP:20260606T115837
CREATED:20220503T201421Z
LAST-MODIFIED:20220503T201421Z
UID:10000342-1652119200-1652124600@datascience.ucsd.edu
SUMMARY:Data Science and Biology - Speaker Series
DESCRIPTION:Data Science and Biology – Speaker Series\nHosted by Diversity in Data Science & CELLebrate\n  \nEvent Description\nData Science and Biology – Speaker Series provides an exciting opportunity to hear from experienced professionals about various data science techniques with applications to the biological sciences. All undergraduate/graduate students\, faculty\, and staff interested in STEM are encouraged to attend! RSVP Here! \nGain insight on cutting edge academic research pertaining to early detection of COVID-19 infection and unsupervised data analysis in neuroscience\, as well as an industry perspective of data analysis in biopharmaceutical manufacturing. See speaker bios on the next page! \nEach speaker will give a short presentation of their background and current research\, followed by a brief Q&A session. Attendees will have the opportunity to ask questions to each speaker. The speakers offer a wealth of experience from both academic and industry perspectives so we encourage everyone to attend and make the most of this opportunity! \n  \nEvent Details\n\nDate & Time: Monday\, May 9th\, 2022 @ 6:00-7:30 pm PST\n\n\n\n\nClick Here to Add Google Calendar Event Link\n\n\n\n\n\n\nHybrid Modality\n\n\n\n\nIn-Person Location: UCSD Main Campus\, Price Center West (Level 2)\, Thurgood Marshall College Room\nZoom ID: 204 876 8506\n\n\n\n  \nEvent Speakers\nBenjamin Smarr\, PhD\nAssistant Professor\, Bioengineering Department and Halicioglu Data Science Institute\, UCSD. \nSmarr Lab website \n  \nThe Smarr lab leverages domain expertise in biological rhythms and neuroendocrinology to uncover patterns in diverse sets of time series data that carry actionable information to impact health and cognitive performance. In 2020 he became the technical lead of the global collaborative TemPredict study\, which developed algorithms for early detection of COVID-19 infection\, and unique cyberinfrastructure to serve rapid\, collaborative explorations of population-scale\, personal time series data. Beyond the pandemic\, Dr. Smarr contributes broadly through science outreach\, popular media\, and industry liaisons. His personal passions lie in advancing women’s health\, and in increasing participant engagement to map physiological diversity in service to precision individual and public health. \n  \nGal Mishne\, PhD\nAssistant Professor\, Halicioglu Data Science Institute\, UCSD. \nUCSD website \n  \nDr. Mishne’s research is at the intersection of signal processing and machine learning for graph-based modeling\, processing and analysis of large-scale high-dimensional real-world data. She develops unsupervised and generalizable methods that allow the data to reveal its own story in an unbiased manner. Her research on unsupervised data analysis in neuroscience\, includes processing of raw neuroimaging data through discovery of neural manifolds to visualization of learning in artificial and biological neural networks. \n  \nDavid Conant\, PhD\nPrincipal Data Scientist at Resilience. \nResilience website \n  \nRSVP here! \n(All up to date information can be found here.)
URL:https://datascience.ucsd.edu/event/data-science-and-biology-speaker-series/
LOCATION:3234 Matthews Ln\, La Jolla\, 92093\, United States
CATEGORIES:Guest Lecture,Seminar,Student Event
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20220512T150000
DTEND;TZID=America/Los_Angeles:20220512T161500
DTSTAMP:20260606T115837
CREATED:20220503T200005Z
LAST-MODIFIED:20220503T200005Z
UID:10000204-1652367600-1652372100@datascience.ucsd.edu
SUMMARY:HDSI Beyond Graduation
DESCRIPTION:
URL:https://datascience.ucsd.edu/event/hdsi-beyond-graduation/
LOCATION:3234 Matthews Ln\, La Jolla\, 92093\, United States
CATEGORIES:HDSI Event
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20220520
DTEND;VALUE=DATE:20220521
DTSTAMP:20260606T115837
CREATED:20220519T001902Z
LAST-MODIFIED:20230301T235216Z
UID:10000344-1653004800-1653091199@datascience.ucsd.edu
SUMMARY:Data Science Day 2022
DESCRIPTION:
URL:https://datascience.ucsd.edu/event/data-science-day-2022/
LOCATION:3234 Matthews Ln\, La Jolla\, 92093\, United States
CATEGORIES:Industry
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20220525T120000
DTEND;TZID=America/Los_Angeles:20220525T130000
DTSTAMP:20260606T115837
CREATED:20220519T000623Z
LAST-MODIFIED:20220519T000623Z
UID:10000343-1653480000-1653483600@datascience.ucsd.edu
SUMMARY:DSC Instructor Meeting
DESCRIPTION:
URL:https://datascience.ucsd.edu/event/dsc-instructor-meeting/
LOCATION:3234 Matthews Ln\, La Jolla\, 92093\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20220808T150000
DTEND;TZID=America/Los_Angeles:20220808T163000
DTSTAMP:20260606T115837
CREATED:20220805T204313Z
LAST-MODIFIED:20220805T204313Z
UID:10000345-1659970800-1659976200@datascience.ucsd.edu
SUMMARY:Accelerating the Deployment of Renewables with Computation | Andrew Grimshaw
DESCRIPTION:Dr. Grimshaw received his BA from UCSD in 1981\, his PhD in Computer Science from the University of Illinois in 1988\, and then joined the Department of Computer Science at the University of Virginia. In his 34-year career at Virginia\, Grimshaw focused on the challenges of designing\, building\, and deploying solutions that meet user requirements on production super computing systems such as those operated by the DoD\, NASA\, DOE\, and the NSF. In addition to his academic career\, Dr. Grimshaw has been a founder\, or very early employee\, of three startups: Software Products International\, Avaki\, and Lancium. Dr. Grimshaw retired this year from the University of Virginia to join Lancium and participate in their transformative mission to change how and where computing is done while decarbonizing the electrical grid. \nEvery MWh of electricity produced in the US today leads\, on average\, to 0.7 metric tons of CO2 emissions. That’s the bad news; The good news is that renewable\, carbon-neutral generation is now the least expensive means to produce electricity in the world. This has led to the development of tens of gigawatts of wind and solar capacity in the US. In West Texas alone\, there is now so much excess power that wind and solar resources are often curtailed\, leaving terawatt-hours of potential power unused. \nIn short\, generating sufficient power with renewables is no longer the problem. Instead\, the problem is leveraging this power. There are two significant challenges. First\, the sun does not always shine\, and the wind does not always blow\, meaning that the current design of the electrical grid will have to change to accommodate fluctuating generation. Second\, the best wind and solar sources are not proximate to sources of load\, namely\, population centers and heavy industry. \nOne way to address this second problem is to identify energy-intensive\, economically viable industries\, that can be feasibly moved to where the power is plentiful and that can accommodate the large amounts of variable generation provided by renewable energy. Computation\, including Bitcoin\, fits these requirements: computation turns electricity into value and requires only power and networking to operate. Computation can be paused\, restarted\, and migrated between sites in response to differences in power availability. In essence\, data centers can act like giant batteries and grid stabilizers\, soaking up power and delivering value when renewable sources are pumping out lots of power\, and reducing consumption and stabilizing the grid when renewable sources are limited. \nIn this talk I begin with some electrical grid basics\, stability\, primary frequency response\, ancillary services\, and the Texas CREZ line. I then show how Bitcoin and computation more generally can be used as a variable and controllable load to stabilize the grid\, consuming energy when it is inexpensive\, and dropping load and releasing energy back to the grid (i.e. humans) when energy prices are high. Further\, buying TWhs of otherwise unused energy causes renewable energy generation to become more profitable by providing a stable base load\, spurring further renewable energy projects. This in turn increasing the availability of renewable energy even on cloudy and windless days.
URL:https://datascience.ucsd.edu/event/accelerating-the-deployment-of-renewables-with-computation-andrew-grimshaw/
LOCATION:3234 Matthews Ln\, La Jolla\, 92093\, United States
CATEGORIES:HDSI Event
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20220925T150000
DTEND;TZID=America/Los_Angeles:20220925T160000
DTSTAMP:20260606T115837
CREATED:20220805T205656Z
LAST-MODIFIED:20260424T030328Z
UID:10000346-1664118000-1664121600@datascience.ucsd.edu
SUMMARY:HDSI Open House 2022
DESCRIPTION:[vc_row][vc_column][vc_column_text css=””] \n\nJoin us for our annual Open House on August 31st at 3PM PDT (held virtually). The event will provide an in-depth look at our undergraduate and graduate data science talent and opportunities to engage with them. This event will be particularly relevant to those involved in talent acquisition as well hiring managers and leaders considering the addition of data science talent to their organizations. The program will cover the following areas: \n\n\nCurriculum Review with HDSI Program Vice Chair\nCapstone Overview with Industry Partner\nHow to engage and recruit our talent\nIndustry Partnership Alliance Program\nQ&A\n\nRSVP HERE\n\n[/vc_column_text][/vc_column][/vc_row]
URL:https://datascience.ucsd.edu/event/hdsi-open-house-2022/
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230306T100000
DTEND;TZID=America/Los_Angeles:20230306T100000
DTSTAMP:20260606T115837
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
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230306T150000
DTEND;TZID=America/Los_Angeles:20230306T150000
DTSTAMP:20260606T115837
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:20230307T140000
DTEND;TZID=America/Los_Angeles:20230307T140000
DTSTAMP:20260606T115837
CREATED:20230302T000627Z
LAST-MODIFIED:20230306T234948Z
UID:10000348-1678197600-1678197600@datascience.ucsd.edu
SUMMARY:Cryptographic Advances in Reasoning about Adversaries
DESCRIPTION:A key challenge in cryptography is to ensure that a protocol resists all computationally feasible attacks\, even when an adversary decides to follow a completely arbitrary and unpredictable strategy. This often turns out to be notoriously difficult — for example\, proofs of security must typically extract an adversary’s implicit input\, but this is at odds with other goals like privacy\, which require that inputs be hidden and difficult to extract. In this talk\, I will describe my work that reimagines how we reason about adversaries\, thereby making progress on foundational questions in classical and quantum protocol design. On the classical front\, these insights have helped understand the mathematical assumptions required to immunize protocols against coordinated attacks on the internet\, and verify computations while preserving privacy. On the quantum front\, these methods help exploit the “destructive” nature of measurements and open up fundamentally new possibilities for cryptography. I will discuss examples that leverage quantum information to (1) weaken the assumptions needed for core tasks like secure computation on distributed data\, and (2) allow outsourcing computations on sensitive data while also verifying that data was deleted after processing.
URL:https://datascience.ucsd.edu/event/dakshita-khurana/
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:20230309T170000
DTEND;TZID=America/Los_Angeles:20230309T180000
DTSTAMP:20260606T115837
CREATED:20230308T235026Z
LAST-MODIFIED:20230309T003020Z
UID:10000356-1678381200-1678384800@datascience.ucsd.edu
SUMMARY:Ophthalmologic Telemedicine: See all the Possibilities | Dr. April Maa\, M.D.
DESCRIPTION:Dr. April Maa is currently an Associate Professor with Emory University School of Medicine\, Department of Ophthalmology\, in the Comprehensive Ophthalmology Section.  She began her career with Emory and the Atlanta VA in 2008\, after finishing medical school and ophthalmology residency in Texas. In 2015\, she led the development of Technology-based Eye Care Services (TECS) with 3 pilot sites in the Atlanta VA catchment area\, which is since grown to a national program serving more than 30\,000 patients last fiscal year\, covering over 60 sites across 20 participating VAMCs.  She currently serves as the Tele-Specialty Care Director of the Southeast Region (Veterans’ Integrated Service Network 7) and is responsible for TECS\, tele-dermatology\, and tele-pain sections of the Clinical Resource Hub (CRH). \n  \nThis talk will cover current VA ocular telehealth programs and future directions\, including our research and collaborations for AI\, predictive analytics\, and very early preliminary results from the Eye911 trial that I am running right now.  \n  \nZoom Link: https://uchealth.zoom.us/j/83927612329?pwd=SFBnTllsWERRclRjVENPWkZxV2VEUT09 \nMeeting ID:  839 2761 2329
URL:https://datascience.ucsd.edu/event/ophthalmologic-telemedicine-see-all-the-possibilities-dr-april-maa-m-d/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230316T140000
DTEND;TZID=America/Los_Angeles:20230316T153000
DTSTAMP:20260606T115837
CREATED:20230315T154024Z
LAST-MODIFIED:20230315T154024Z
UID:10000360-1678975200-1678980600@datascience.ucsd.edu
SUMMARY:Optimal methods for reinforcement learning: Efficient algorithms with instance-dependent guarantees | Wenlong Mou
DESCRIPTION:Abstract: Reinforcement learning (RL) is a pillar for modern artificial intelligence. Compared to classical statistical learning\, several new statistical and computational phenomena arise from RL problems\, leading to different trade-offs in the choice of the estimators\, tuning of their parameters\, and the design of efficient algorithms. In many settings\, asymptotic and/or worst-case theory fails to provide the relevant guidance.\nIn this talk\, I present recent advances that involve a more refined approach to RL\, one that leads to non-asymptotic and instance-optimal guarantees. The bulk of this talk focuses on function approximation methods for policy evaluation. I establish a novel class of optimal and instance-dependent oracle inequalities for projected Bellman equations\, as well as efficient computational algorithms achieving them. Among other results\, I will highlight how the instance-optimal guarantees guide the selection of tuning parameters in temporal different methods\, and tackle the instability issue with general function classes. Drawing on this perspective\, I will also discuss a novel class of stochastic approximation methods that yield optimal statistical guarantees for policy optimization problems. \nBio: Wenlong Mou is a Ph.D. candidate at Department of EECS\, UC Berkeley\, advised by Martin Wainwright and Peter Bartlett. Prior to Berkeley\, he received his B.Sc. degree in Computer Science from Peking University. Wenlong’s research interests include statistics\, machine learning theory\, dynamic programming and optimization\, and applied probability. He is particularly interested in designing optimal statistical methods that enable optimal data-driven decision making\, powered by efficient computational algorithms.
URL:https://datascience.ucsd.edu/event/optimal-methods-for-reinforcement-learning-efficient-algorithms-with-instance-dependent-guarantees-wenlong-mou/
LOCATION:SDSC\, The Auditorium\, 9836 Hopkins Dr\, La Jolla\, San Diego\, CA\, United States
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20230317
DTEND;VALUE=DATE:20230318
DTSTAMP:20260606T115837
CREATED:20230315T155732Z
LAST-MODIFIED:20230315T155732Z
UID:10000361-1679011200-1679097599@datascience.ucsd.edu
SUMMARY:Scientific Machine Learning Symposium
DESCRIPTION:
URL:https://datascience.ucsd.edu/event/scientific-machine-learning-symposium/
LOCATION:The Great Hall\, 9500 Gilman Drive\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Sponsorship,Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230320T140000
DTEND;TZID=America/Los_Angeles:20230320T140000
DTSTAMP:20260606T115837
CREATED:20230302T000631Z
LAST-MODIFIED:20230317T154618Z
UID:10000352-1679320800-1679320800@datascience.ucsd.edu
SUMMARY:Sampling from Graphical Models via Spectral Independence | Zongchen Chen
DESCRIPTION:Abstract: In many scientific settings we use a statistical model to describe a high-dimensional distribution over many variables. Such models are often represented as a weighted graph encoding the dependencies between different variables and are known as graphical models. Graphical models arise in a wide variety of scientific fields throughout science and engineering.\nOne fundamental task for graphical models is to generate random samples from the associated distribution. The Markov chain Monte Carlo (MCMC) method is one of the simplest and most popular approaches to tackle such problems. Despite the popularity of graphical models and MCMC algorithms\, theoretical guarantees of their performance are not known even for some simple models. I will describe a new tool called “spectral independence” to analyze MCMC algorithms and more importantly to reveal the underlying structure behind such models. I will also discuss how these structural properties can be applied to sampling when MCMC fails and to other statistical problems like parameter learning or model fitting. \n\nBio: Zongchen Chen is an instructor (postdoc) in Mathematics at MIT. He received his PhD degree in Algorithms\, Combinatorics and Optimization (ACO) at Georgia Tech in 2021 advised by Eric Vigoda. His thesis received the 2021 Georgia Tech College of Computing Outstanding Doctoral Dissertation Award. He received his BS degree in Mathematics & Applied Mathematics from Zhiyuan College at Shanghai Jiao Tong University in 2016. He is broadly interested in randomized algorithms\, discrete probability\, and machine learning. His current research interests include Markov chain Monte Carlo (MCMC) methods\, approximate counting and sampling\, and learning and testing for high-dimensional distributions.
URL:https://datascience.ucsd.edu/event/zongchen-chen/
LOCATION:SDSC\, The Auditorium\, 9836 Hopkins Dr\, La Jolla\, San Diego\, CA\, United States
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230321T140000
DTEND;TZID=America/Los_Angeles:20230321T153000
DTSTAMP:20260606T115837
CREATED:20230316T180226Z
LAST-MODIFIED:20230316T180226Z
UID:10000362-1679407200-1679412600@datascience.ucsd.edu
SUMMARY:Uncovering the algorithmic foundations of language learning and processing
DESCRIPTION:Abstract: Human language is extraordinarily complex. Nevertheless\, we readily acquire language as children\, when we are most cognitively limited\, and we comprehend language as adults with striking efficiency. My research seeks to understand the mental algorithms that allow us to accomplish this feat\, with particular focus on how memory and prediction mechanisms are recruited to overcome the bottlenecks of real-time language processing. In this talk\, I will review results from three of my lines of inquiry into this question. First\, using diverse naturalistic reading datasets\, I will show evidence that prediction is a central concern of the human language processing system. Second\, using fMRI measures of naturalistic story listening\, I will show evidence that memory and prediction processes are dissociable in the brain’s response to language\, that syntactic structure building plays a major role in ordinary language comprehension\, and that the neural resources that are responsible for structure building are largely specialized for language. Third\, I will show evidence from computational modeling that memory and prediction pressures independently encourage discovery of phonological regularities from natural speech. Together\, these results support an intricate coordination of memory and prediction abilities for language learning and comprehension. I will conclude by outlining planned directions for my future lab\, integrating neuroimaging\, behavioral methods\, natural language processing\, and computational modeling to study language learning and processing.\n\nBio: Cory is a post-doc at MIT and their work uses computational and experimental methods to study language and the mind\, particularly (1) the cognitive processes that allow us to understand the things we hear and read so quickly\, (2) the learning signals that we leverage as children to acquire language from the environment\, and (3) the role played by real-time information processing constraints in shaping language learning and comprehension.\nCory’s work often builds deep learning models to investigate these questions\, and is actively developing machine learning techniques to help scientists understand complex dynamical systems like the human mind and brain. Their work intersects machine learning\, cognitive science\, neuroscience\, artificial intelligence\, natural language processing\, statistics\, and (psycho)linguistics.
URL:https://datascience.ucsd.edu/event/uncovering-the-algorithmic-foundations-of-language-learning-and-processing/
LOCATION:SDSC\, The Synthesis Center\, 9500 Gilman Drive\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230322T140000
DTEND;TZID=America/Los_Angeles:20230322T153000
DTSTAMP:20260606T115837
CREATED:20230323T182317Z
LAST-MODIFIED:20230323T182317Z
UID:10000370-1679493600-1679499000@datascience.ucsd.edu
SUMMARY:Structured Transformer Models for NLP
DESCRIPTION:Abstract: The field of natural language processing has recently unlocked a wide range of new capabilities through the use of large language models\, such as GPT-4. The growing application of these models motivates developing a more thorough understanding of how and why they work\, as well as further improvements in both quality and efficiency. \nIn this talk\, I will present my work on analyzing and improving the Transformer architecture underlying today’s language models through the study of how information is routed between multiple words in an input. I will show that such models can predict the syntactic structure of text in a variety of languages\, and discuss how syntax can inform our understanding of how the networks operate. I will also present my work on structuring information flow to build radically more efficient models\, including models that can process text of up to one million words\, which enables new possibilities for NLP with book-length text. \nBio: Nikita Kitaev is a final-year Ph.D. student in Computer Science at UC Berkeley\, advised by Dan Klein. His research spans natural language processing and machine learning\, with a focus on understanding the structure and operation of large language models\, including leveraging insights from the field of syntax. He is the recipient of an NSF Graduate Research Fellowship and his work has been recognized through a best paper award at ACL. Previously\, Nikita has received a B.S. in Electrical Engineering and Computer Science from UC Berkeley.
URL:https://datascience.ucsd.edu/event/structured-transformer-models-for-nlp/
LOCATION:SDSC\, The Synthesis Center\, 9500 Gilman Drive\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Colloquium
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20230323
DTEND;VALUE=DATE:20230325
DTSTAMP:20260606T115837
CREATED:20230323T180908Z
LAST-MODIFIED:20230712T092822Z
UID:10000363-1679529600-1679702399@datascience.ucsd.edu
SUMMARY:PhD Open House
DESCRIPTION:HDSI will host the first ever in person DSC Inaugural Open House for Prospective PhD Students (Mar 23 + 24).  The event will include Graduate Student Poster Session and will showcase HDSI diverse research activities. Prospective PhD students are invited. \n  \nPlease contact Laura Horton (lkhorton@ucsd.edu) for event details.
URL:https://datascience.ucsd.edu/event/phd-open-house/
LOCATION:SDSC\, The Auditorium\, 9836 Hopkins Dr\, La Jolla\, San Diego\, CA\, United States
CATEGORIES:Student Event
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230329T140000
DTEND;TZID=America/Los_Angeles:20230329T153000
DTSTAMP:20260606T115837
CREATED:20230302T000631Z
LAST-MODIFIED:20230323T175902Z
UID:10000353-1680098400-1680103800@datascience.ucsd.edu
SUMMARY:Distance-Estimation in Modern Graphs: Algorithms and Impossibility | Nicole Wein
DESCRIPTION:The size and complexity of today’s graphs present challenges that necessitate the discovery of new algorithms. One central area of research in this endeavor is computing and estimating distances in graphs. In this talk I will discuss two fundamental families of distance problems in the context of modern graphs: Diameter/Radius/Eccentricities and Hopsets/Shortcut Sets. The best-known algorithm for computing the diameter (largest distance) of a graph is the naive algorithm of computing all-pairs shortest paths and returning the largest distance. Unfortunately\, this can be prohibitively slow for massive graphs. Thus\, it is important to understand how fast and how accurately the diameter of a graph can be approximated. I will present tight bounds for this problem via conditional lower bounds from fine-grained complexity. Secondly\, for a number of settings relevant to modern graphs (e.g. parallel algorithms\, streaming algorithms\, dynamic algorithms)\, distance computation is more efficient when the input graph has low hop-diameter. Thus\, a useful preprocessing step is to add a set of edges (a hopset) to the graph that reduces the hop-diameter of the graph\, while preserving important distance information. I will present progress on upper and lower bounds for hopsets. \n  \nBio: Nicole Wein is a Simons Postdoctoral Leader at DIMACS at Rutgers University. Previously\, she obtained her Ph.D. from MIT advised by Virginia Vassilevska Williams. She is a theoretical computer scientist and her research interests include graph algorithms and lower bounds including in the areas of distance-estimation algorithms\, dynamic algorithms\, and fine-grained complexity.
URL:https://datascience.ucsd.edu/event/nicole-wein/
LOCATION:SDSC\, The Auditorium\, 9836 Hopkins Dr\, La Jolla\, San Diego\, CA\, United States
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230403T140000
DTEND;TZID=America/Los_Angeles:20230403T153000
DTSTAMP:20260606T115837
CREATED:20230323T181509Z
LAST-MODIFIED:20240402T224726Z
UID:10000366-1680530400-1680535800@datascience.ucsd.edu
SUMMARY:Frameworks for High Dimensional Optimization
DESCRIPTION:I present frameworks for solving extremely large\, prohibitively massive optimization problems. Today\, practical applications require optimization solvers to work at extreme scales\, but existing solvers do not often scale as desired. I present black-box acceleration algorithms for speeding up optimization solvers\, in both distributed and parallel settings. Given a huge problem\, I develop dimension reduction techniques that allow the problem to be solved in a fraction of the original time\, and simultaneously make the computation amenable to distributed computation. Efficient\, dependable and secure distributed computing is increasingly fundamental to a wide range of core applications including distributed data centers\, decentralized power grid\, coordination of autonomous devices\, and scheduling and routing problems. \nIn particular\, I consider two optimization settings of interest. First\, I consider packing linear programming (LP). LP solvers are fundamental to many problems in supply chain management\, routing\, learning and inference problems. I present a framework that speeds up linear programming solvers such as Cplex and Gurobi by an order of magnitude\, while maintaining provably nearly optimal solutions. Secondly\, I present a distributed algorithm that achieves an exponential reduction in message complexity compared to existing distributed methods. I present both empirical demonstrations and theoretical guarantees on the quality of the solution and the speedup provided by my methods
URL:https://datascience.ucsd.edu/event/palma-london/
LOCATION:SDSC\, The Auditorium\, 9836 Hopkins Dr\, La Jolla\, San Diego\, CA\, United States
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230404T140000
DTEND;TZID=America/Los_Angeles:20230404T153000
DTSTAMP:20260606T115837
CREATED:20230323T181718Z
LAST-MODIFIED:20230403T190229Z
UID:10000367-1680616800-1680622200@datascience.ucsd.edu
SUMMARY:Lijun Ding
DESCRIPTION:
URL:https://datascience.ucsd.edu/event/lijun-ding/
LOCATION:SDSC\, The Auditorium\, 9836 Hopkins Dr\, La Jolla\, San Diego\, CA\, United States
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230405T140000
DTEND;TZID=America/Los_Angeles:20230405T153000
DTSTAMP:20260606T115837
CREATED:20230323T181933Z
LAST-MODIFIED:20230403T190338Z
UID:10000368-1680703200-1680708600@datascience.ucsd.edu
SUMMARY:Constrained\, Casual\, and Logical Reasoning for Neural Language Generation
DESCRIPTION:Today’s language models (LMs) can produce human-like fluent text. However\, they generate words with no grounding in the world and cannot flexibly reason about everyday situations and events\, such as counterfactual (“what if?”) and abductive (“what might explain these observations?”) reasoning that are important forms of human cognition activities. In this talk\, I will present my research on connecting reasoning with language generation. Reasoning for language generation poses several key challenges\, including incorporating diverse contextual constraints on the fly\, understanding cause and effect when events unfold\, and grounding on logic structures for consistent reasoning. I will first discuss COLD decoding\, a unified energy-based framework for any off-the-shelf LMs to reason with arbitrary constraints. It also introduces differentiable reasoning over discrete symbolic text for improved efficiency. Secondly\, I will focus on a particularly important form of reasoning\, counterfactual reasoning\, including its first formulation in language generation and our algorithm\, DeLorean\, that enables off-the-shelf LMs to capture causal invariance. Thirdly\, I will present Maieutic prompting\, which improves the logical consistency of neural reasoning by integrating with logic structures. I will conclude with future research toward more general\, grounded\, and trustworthy reasoning with language. \nBio: Lianhui Qin is a final year PhD student in Paul G. Allen School of Computer Science & Engineering at University of Washington\, advised by Prof. Yejin Choi. Her research interests lie in natural language processing\, artificial intelligence\, and machine learning\, with a particular focus on natural language reasoning and generation. Her research has been recognized with Best Paper Award at NAACL 2022\, Best Paper Award at WeCNLP 2020\, Best Demo Paper Nomination at ACL 2019\, as well as Microsoft Research PhD Fellowship.
URL:https://datascience.ucsd.edu/event/lianhui-qin/
LOCATION:SDSC\, The Auditorium\, 9836 Hopkins Dr\, La Jolla\, San Diego\, CA\, United States
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230406T110000
DTEND;TZID=America/Los_Angeles:20230406T123000
DTSTAMP:20260606T115837
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:20260606T115837
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:20260606T115837
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
END:VCALENDAR