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X-ORIGINAL-URL:https://datascience.ucsd.edu
X-WR-CALDESC:Events for Halıcıoğlu Data Science Institute - UC San Diego
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DTSTART;TZID=America/Los_Angeles:20220328T130000
DTEND;TZID=America/Los_Angeles:20220328T163000
DTSTAMP:20260607T060610
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:20220328T010000
DTEND;TZID=America/Los_Angeles:20220328T010000
DTSTAMP:20260607T060610
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:20220316T100000
DTEND;TZID=America/Los_Angeles:20220316T110000
DTSTAMP:20260607T060610
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:20220316T100000
DTEND;TZID=America/Los_Angeles:20220316T100000
DTSTAMP:20260607T060610
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:20220311T100000
DTEND;TZID=America/Los_Angeles:20220311T120000
DTSTAMP:20260607T060610
CREATED:20220228T220656Z
LAST-MODIFIED:20220228T220656Z
UID:10000192-1646992800-1647000000@datascience.ucsd.edu
SUMMARY:HDSI 2022 Senior Class Capstone Showcase
DESCRIPTION:More info here!
URL:https://datascience.ucsd.edu/event/hdsi-2022-senior-class-capstone-showcase/
LOCATION:3234 Matthews Ln\, La Jolla\, 92093\, United States
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20220310T140000
DTEND;TZID=America/Los_Angeles:20220310T150000
DTSTAMP:20260607T060610
CREATED:20220310T203704Z
LAST-MODIFIED:20220310T203704Z
UID:10000196-1646920800-1646924400@datascience.ucsd.edu
SUMMARY:HDSI Seminar: Divya Mahajan
DESCRIPTION:
URL:https://datascience.ucsd.edu/event/hdsi-seminar-divya-mahajan/
LOCATION:3234 Matthews Ln\, La Jolla\, 92093\, United States
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20220309T150000
DTEND;TZID=America/Los_Angeles:20220309T190000
DTSTAMP:20260607T060610
CREATED:20220222T210142Z
LAST-MODIFIED:20220222T210142Z
UID:10000341-1646838000-1646852400@datascience.ucsd.edu
SUMMARY:Data Science Study Jam WI22
DESCRIPTION:
URL:https://datascience.ucsd.edu/event/data-science-study-jam-wi22/
LOCATION:3234 Matthews Ln\, La Jolla\, 92093\, United States
CATEGORIES:HDSI Event
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20220303T160000
DTEND;TZID=America/Los_Angeles:20220303T180000
DTSTAMP:20260607T060610
CREATED:20220228T215822Z
LAST-MODIFIED:20260411T014725Z
UID:10000190-1646323200-1646330400@datascience.ucsd.edu
SUMMARY:Physics Colloquium
DESCRIPTION:[vc_row][vc_column][vc_column_text css=””] \nPlease join us Thursday\, March 3rd\, at 4PMFor the Physics ColloquiumZoom link: https://ucsd.zoom.us/j/93730100055?pwd=eFl2ZGw5WExKSU1JSy9lbXp1ZGxXUT09 Passcode: PHYSCOLL Dr. Frederica BiancoUniversity of Delaware “Beyond the stars: data-driven approaches to astrophysics\, survey design\, and interdisciplinary research” Astrophysics is the perfect nursery for Data Science: we cannot touch the stars\, we cannot explode them in a lab. We know them only through our data collection efforts\, messy\, complex data. The Rubin Observatory Legacy Survey of Space and Time (LSST) is about to usher yet a new era in data-intensive astrophysics. The “next-generation” ground-based astronomical survey\, LSST will generate 20TB of information-rich optical-imaging data every night for 10 years starting in 2024. The survey is designed to study nearly all subdomains of astrophysics\, from the closest Solar System objects to the farthest cosmological explosions\, and with a unique data policy that gives unrestricted access to all US and Chilean scientists\, it is staged to be truly transformational. I will talk about how we are optimizing the survey and preparing to unknock the most pressing secrets about the universe and discover unusual and even completely new phenomena with LSST through a community- and data-driven approach. Beyond Rubin\, I will discuss examples of interdisciplinary projects where Data Science techniques common in astrophysicist are brought across domains: in Urban Science\, where we study the urban ecology and sociology through images\, in COVID-19 predictions to support local hospital resource planning\, and even in feminist approaches to substance abuse.\nHere is the link for the sign-up sheet to meet with meet with Dr. Frederica Bianco who will be speaking at the Physics Colloquium at the NSB Auditorium Thursday\, March 3rd\, at 4PM on “Beyond the stars: data-driven approaches to astrophysics\, survey design\, and interdisciplinary research”. \nSignup to meet with Dr. Bianco \n\n\n\n\nMeet with Dr. BiancoSheet1 Available Time Slot\,Sign up – Full Name Thursday 3/3/22 10:00-10:30 AM Thursday 3/3/22 10:30-11:30 AM Thursday 3/3/22 11:15-12:15 PM Lunch Thursday 3/3/22 2:00-2:30 PM\,David Danks Prof. of Data Science and Philosophy Thursday 3/3/22 2:30-3:00 PM Thursday 3/3/22 6:15 PM Dinner Friday 3/4/2…docs.google.com\n\n\n\n[/vc_column_text][/vc_column][/vc_row]
URL:https://datascience.ucsd.edu/event/physics-colloquium/
LOCATION:3234 Matthews Ln\, La Jolla\, 92093\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20220303T140000
DTEND;TZID=America/Los_Angeles:20220303T153000
DTSTAMP:20260607T060610
CREATED:20220303T221558Z
LAST-MODIFIED:20220303T221558Z
UID:10000194-1646316000-1646321400@datascience.ucsd.edu
SUMMARY:HDSI Seminar: Cody Carroll
DESCRIPTION:
URL:https://datascience.ucsd.edu/event/hdsi-seminar-cody-carroll/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20220301T130000
DTEND;TZID=America/Los_Angeles:20220301T163000
DTSTAMP:20260607T060610
CREATED:20220228T215041Z
LAST-MODIFIED:20220228T215041Z
UID:10000188-1646139600-1646152200@datascience.ucsd.edu
SUMMARY:HDSI 4th Anniversary
DESCRIPTION:
URL:https://datascience.ucsd.edu/event/hdsi-4th-anniversary/
LOCATION:3234 Matthews Ln\, La Jolla\, 92093\, United States
CATEGORIES:HDSI Event
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20220214
DTEND;VALUE=DATE:20220219
DTSTAMP:20260607T060610
CREATED:20220202T211233Z
LAST-MODIFIED:20220202T211233Z
UID:10000340-1644796800-1645228799@datascience.ucsd.edu
SUMMARY:UC-wide Love Data Week celebration
DESCRIPTION:
URL:https://datascience.ucsd.edu/event/12071/
LOCATION:3234 Matthews Ln\, La Jolla\, 92093\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20220202T160000
DTEND;TZID=America/Los_Angeles:20220202T173000
DTSTAMP:20260607T060610
CREATED:20220202T154645Z
LAST-MODIFIED:20220202T154645Z
UID:10000339-1643817600-1643823000@datascience.ucsd.edu
SUMMARY:Statistical Learning and Market Design
DESCRIPTION:
URL:https://datascience.ucsd.edu/event/statistical-learning-and-market-design/
LOCATION:3234 Matthews Ln\, La Jolla\, 92093\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20211117T140000
DTEND;TZID=America/Los_Angeles:20211117T150000
DTSTAMP:20260607T060610
CREATED:20211112T223936Z
LAST-MODIFIED:20211112T223936Z
UID:10000338-1637157600-1637161200@datascience.ucsd.edu
SUMMARY:HDSI Seminar Series | Theories of Inference for Visual Analysis
DESCRIPTION:
URL:https://datascience.ucsd.edu/event/hdsi-seminar-series-theories-of-inference-for-visual-analysis/
CATEGORIES:Colloquium,Guest Lecture,HDSI Event,Industry,Seminar,Webinar,Workshops
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20211028T173000
DTEND;TZID=America/Los_Angeles:20211028T190000
DTSTAMP:20260607T060610
CREATED:20210929T222223Z
LAST-MODIFIED:20210929T222223Z
UID:10000337-1635442200-1635447600@datascience.ucsd.edu
SUMMARY:Data Science Insights Speaker Series: Lily Weng
DESCRIPTION:
URL:https://www.meetup.com/San-Diego-Machine-Learning/events/280949913/#new_tab
CATEGORIES:HDSI Event,Industry
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20211014T153000
DTEND;TZID=America/Los_Angeles:20211014T183000
DTSTAMP:20260607T060610
CREATED:20210929T053818Z
LAST-MODIFIED:20210929T053818Z
UID:10000335-1634225400-1634236200@datascience.ucsd.edu
SUMMARY:Data Science Talent Day 2021
DESCRIPTION:Click here to register for this event!
URL:https://datascience.ucsd.edu/event/data-science-talent-day-2021/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20210922T173000
DTEND;TZID=America/Los_Angeles:20210922T190000
DTSTAMP:20260607T060610
CREATED:20210910T201020Z
LAST-MODIFIED:20210910T201020Z
UID:10000336-1632331800-1632337200@datascience.ucsd.edu
SUMMARY:Data Science Insights Speaker Series: Arun Kumar
DESCRIPTION:
URL:https://www.meetup.com/San-Diego-Machine-Learning/events/280353929/#new_tab
CATEGORIES:Seminar,Workshops
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20210827T093000
DTEND;TZID=America/Los_Angeles:20210827T103000
DTSTAMP:20260607T060610
CREATED:20210825T190805Z
LAST-MODIFIED:20210825T190805Z
UID:10000334-1630056600-1630060200@datascience.ucsd.edu
SUMMARY:E4E Summer 2021 Research Forum
DESCRIPTION:Summer Research Presentations \nFriday August 27\, 2021\, 9:30 – 10:30am Pacific Time \nRegistration: https://ucsd.zoom.us/j/96150602602  \n  \nJoin us to hear about our Engineers for Exploration (E4E) research projects. This summer\, E4E researchers made impressive contributions to develop technologies that help understand critical ecosystems and monitor endangered species.  \n\nAcoustic Species Identification: Leverage machine learning and digital signal processing to automatically analyze over fifteen-thousand hours of audio data collected from low-cost passive-acoustic-monitoring systems from the Peruvian Amazon.\nAye-Aye Sleep Monitoring: Determine the variations in Aye-Aye sleep patterns by developing a sensor suite for the Aye-Aye’s at the SD Zoo. Develop data analysis to determine behaviors. \nBaboons on the Move: Tracking the behavior of a large troop of baboons in Kenya using drones and computer vision.  \nBurrowing Owl: Applying machine learning to automate the labeling of camera trap footage of burrowing owls in Southern California in order to preserve their population. \nFishSense: Create underwater depth mapping hardware and software to monitor fish size and population via noninvasive sampling.\nMangrove Monitoring: Utilize drones and machine learning to aid scientific collaborators and policymakers in mangrove conservation.\nMaya Archaeology: Build realistic 3D models of Maya archaeological sites in Guatemala using LiDAR\, depth\, and other 3D vision sensors.\nRadio Telemetry Tracker: Create an autonomous aerial platform for locating endangered species equipped with radio transmitting devices. \nSmartfin: Turn recreational surfers into research quality ocean buoys.\n\nEach project presentation consists of a short video that overviews these projects and highlights the work from the summer. The researchers will be available after each video to answer questions.  \nAbout the program: Engineers for Exploration (http://e4e.ucsd.edu) is a one of a kind program promoting multidisciplinary and collaborative research projects with the broad goals of protecting the environment\, uncovering mysteries related to cultural heritage\, and providing experiential learning experiences for participants. We team student engineers with scientists from a wide range of disciplines to create innovative technologies that are deployed around the world.
URL:https://datascience.ucsd.edu/event/e4e-summer-2021-research-forum/
CATEGORIES:Showcase,Webinar
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20210825T173000
DTEND;TZID=America/Los_Angeles:20210825T190000
DTSTAMP:20260607T060610
CREATED:20210804T210907Z
LAST-MODIFIED:20210804T210907Z
UID:10000333-1629912600-1629918000@datascience.ucsd.edu
SUMMARY:Data Science Insights Speaker Series: Arya Mazumdar
DESCRIPTION:In partnership with the San Diego Machine Learning Meetup Group\, we are excited to be launching this monthly speaker series. The intent for this series is to highlight faculty and data science related research from the Institute and UC San Diego to the broader community. \nOur next monthly event will be taking place on Wednesday\, August 25th from 5:30PM-7PM with Associate Professor Arya Mazumdar as our guest speaker. \nFor More Info & RSVP
URL:https://datascience.ucsd.edu/event/data-science-insights-speaker-series-arya-mazumdar/
CATEGORIES:Guest Lecture,Industry,Seminar
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20210825T150000
DTEND;TZID=America/Los_Angeles:20210825T160000
DTSTAMP:20260607T060610
CREATED:20210804T210706Z
LAST-MODIFIED:20210804T210706Z
UID:10000332-1629903600-1629907200@datascience.ucsd.edu
SUMMARY:HDSI Open House Fall 2021
DESCRIPTION:HDSI Open House is an event that will provide attendees with an in-depth look at our undergraduate data science talent and the various 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• Curriculum Review with Program Vice Chair\n• Capstone Overview with Industry Partner\n• How to Engage and Recruit our Talent\n• Industry Partnership Alliance Program\n• Q&A \n  \nRSVP Here
URL:https://datascience.ucsd.edu/event/hdsi-open-house-fall-2021/
CATEGORIES:Industry,Social Event,Webinar
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20210728T173000
DTEND;TZID=America/Los_Angeles:20210728T190000
DTSTAMP:20260607T060610
CREATED:20210712T220353Z
LAST-MODIFIED:20210712T220353Z
UID:10000331-1627493400-1627498800@datascience.ucsd.edu
SUMMARY:Data Science Insights Speaker Series: Yusu Wang
DESCRIPTION:
URL:https://www.meetup.com/San-Diego-Machine-Learning/events/278970327/#new_tab
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20210625T100000
DTEND;TZID=America/Los_Angeles:20210625T110000
DTSTAMP:20260607T060610
CREATED:20210622T230437Z
LAST-MODIFIED:20210622T230437Z
UID:10000330-1624615200-1624618800@datascience.ucsd.edu
SUMMARY:Seminar: Machine Learning for Force Field Parameterization - Application to 2D Materials
DESCRIPTION:Title:  Machine learning for force field parameterization — Application to 2D materials \nSpeaker:  Horacio Espinosa (Northwestern University) \nAbstract: \nThe parameterization of interatomic potentials for molecular dynamics (MD) simulations has long been a highly-specialized endeavor requiring strong domain expertise and in most cases deep chemical intuition. We propose a robust approach incorporating multi-objective genetic algorithms and machine-learning-inspired protocols. Using monolayer MoSe2 as a testbed\, we demonstrate the effectiveness of the proposed approach in parametrizing interatomic potentials with different levels of complexities for structural and mechanical properties in both the equilibrium and non-equilibrium regimes. Applications to flexible electronics\, heat transfer\, surface stability\, as well as force field transferability will be discussed. \nBrief bio:  \nHoracio Espinosa is the James and Nancy Farley Professor of Manufacturing and Entrepreneurship\, Mechanical Engineering\, and the Director of the Theoretical and Applied Mechanics Program at Northwestern University. He made key contributions in the areas of deformation and failure of materials\, design of micro- and nano-systems\, and in-situ microscopy characterization of nanomaterials. Espinosa received numerous awards and is a member of the National Academy of Engineering (NAE)\, foreign member of Academia Europaea\, the Russian Academy of Engineering\, and Fellow of AAAS\, ASME\, SEM\, and AAM.
URL:https://datascience.ucsd.edu/event/seminar-machine-learning-for-force-field-parameterization-application-to-2d-materials/
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20210625T100000
DTEND;TZID=America/Los_Angeles:20210625T110000
DTSTAMP:20260607T060610
CREATED:20210305T184138Z
LAST-MODIFIED:20210305T184138Z
UID:10000167-1624615200-1624618800@datascience.ucsd.edu
SUMMARY:Seminar: Machine Learning for Biomimetic Nanoparticles and Fibrous Nanocomposites
DESCRIPTION:ZoomID: https://cuboulder.zoom.us/j/96007553384 \nPassword:i-aim \nNicholas Kotov\, University of Michigan\n“Machine Learning for Biomimetic Nanoparticles and Fibrous Nanocomposites” \nAbstract:\nTBA \nAbout the Speaker:\nTBA
URL:https://datascience.ucsd.edu/event/seminar-machine-learning-for-biomimetic-nanoparticles-and-fibrous-nanocomposites/
CATEGORIES:Seminar
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20210623T173000
DTEND;TZID=America/Los_Angeles:20210623T190000
DTSTAMP:20260607T060610
CREATED:20210610T215526Z
LAST-MODIFIED:20210610T215526Z
UID:10000329-1624469400-1624474800@datascience.ucsd.edu
SUMMARY:Data Science Insights Speaker Series: Yian Ma
DESCRIPTION:Details\n\nExploration Problem in Sequential Decision Making: A Computational Perspective\nby Yian Ma \nAbstract:\nEfficient Exploration is often the bottleneck for solving sequential decision making problems. Many different approaches have been proposed and analyzed\, such as explore-then-commit\, upper confidence bound\, etc. Much of the focus has been on using frequentist perspectives to understand and develop entirely model-free or model-based methods. In practice\, we often have some information about the system and can benefit from a generative model that has the flexibility of incorporating new information at different stages of the learning process. \nIn this talk\, Yian will discuss how to design scalable computational methods that learn from the generative model and ensure that the optimal regret is achieved with a constant computational budget. That requires us to have increasingly accurate estimation with a growing data set\, under a constant number of iterations and computation per iteration. He will present a stochastic gradient Markov chain Monte Carlo algorithm to achieve this goal. \nBio:\nYian Ma is an assistant professor at the Halıcıoğlu Data Science Institute and an affiliated faculty member at the Computer Science and Engineering Department of the University of California San Diego. Prior to UCSD\, he spent a year as a visiting faculty at Google Research. Before that\, he was a post-doctoral fellow at EECS\, UC Berkeley. He completed his Ph.D. at the University of Washington. His current research primarily involves scalable inference methods and their theoretical guarantees. He has been designing new Bayesian inference algorithms (with a focus on applying them to time series data and sequential decision making) that are provably efficient in terms of computational and statistical guarantees. \n=================\nAgenda (Pacific Daylight Time\, UTC -07)\n=================\n– 5:30 – 5:40 pm — Gathering and introductions\n– 5:40 – 6:30 pm — Talk\n– 6:30 – 7:00 pm — Q & A\, discussion \nLinks to slides and videos of meetup presentations are available on the SDML GitHub repo https://github.com/SanDiegoMachineLearning/talks \n=================\nQuestions?\n=================\nJoin our slack channel or leave a comment below if you have any questions about the group or need clarification on anything.\nhttps://join.slack.com/t/sdmachinelearning/shared_invite/zt-6b0ojqdz-9bG7tyJMddVHZ3Zm9IajJA
URL:https://datascience.ucsd.edu/event/data-science-insights-speaker-series-yian-ma/
CATEGORIES:Guest Lecture,Seminar
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20210614T150000
DTEND;TZID=America/Los_Angeles:20210614T170000
DTSTAMP:20260607T060610
CREATED:20210511T155921Z
LAST-MODIFIED:20210511T155921Z
UID:10000324-1623682800-1623690000@datascience.ucsd.edu
SUMMARY:HDSI Virtual Graduation Celebration: Class of 2021
DESCRIPTION:
URL:https://datascience.ucsd.edu/event/hdsi-virtual-graduation-celebration-class-of-2021/
CATEGORIES:HDSI Event
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20210611T100000
DTEND;TZID=America/Los_Angeles:20210611T110000
DTSTAMP:20260607T060610
CREATED:20210219T230141Z
LAST-MODIFIED:20210219T230141Z
UID:10000317-1623405600-1623409200@datascience.ucsd.edu
SUMMARY:Seminar: Leonidas Guibas\, Stanford University
DESCRIPTION:Zoom ID: https://cuboulder.zoom.us/j/2251625831\nPassword: i-aim \nLeonidas Guibas\, Stanford University\nTBA \nAbstract:\nTBA \nAbout the Speaker:\nTBA
URL:https://datascience.ucsd.edu/event/seminar-manifold-learning-for-free-energy-surface-exploration-2021-06-11/
CATEGORIES:Seminar
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DTSTART;TZID=America/Los_Angeles:20210601T140000
DTEND;TZID=America/Los_Angeles:20210601T153000
DTSTAMP:20260607T060610
CREATED:20210601T160449Z
LAST-MODIFIED:20210601T160449Z
UID:10000328-1622556000-1622561400@datascience.ucsd.edu
SUMMARY:Seminar: Recourse in Machine Learning
DESCRIPTION:Please join us on Tuesday\, June 1 @ 2:00 pm for a ZOOM talk by Berk Ustun. The talk will be about 45 minutes and will be followed by a 30 minute Q&A. \nTitle: Recourse in Machine Learning \nAbstract: Machine learning models are often used to automate decisions that affect humans: whether to approve a loan\, extend a job interview\, or provide insurance. In such tasks\, a person should have the ability to change the decision of the model. When a person is denied a loan by a model\, for example\, they should be able to alter its inputs in a way that guarantees approval. Otherwise\, they will be denied the loan so long as the model is deployed\, and – more importantly – lack control over a decision that affects their livelihood. \nIn this talk\, I will discuss these issues in terms of a formal notion called recourse – i.e.\, the ability of a person to change the decision of a model by altering actionable input variables (e.g.\, income as opposed to age). I will describe how models may deny recourse to their decision subjects due to widespread practices in model development\, and present a suite of methods to prevent this harm. I will end with a brief discussion on how recourse can facilitate meaningful protection in consumer-facing applications of machine learning. \nZoom Meeting: https://ucsd.zoom.us/j/96929564293 \nMeeting ID: 969 2956 4293 \nOne tap mobile+16699006833\,\,96929564293# US (San Jose)+12133388477\,\,96929564293# US (Los Angeles) \nDial by your location+1 669 900 6833 US (San Jose)+1 213 338 8477 US (Los Angeles)+1 669 219 2599 US (San Jose)Meeting ID: 969 2956 4293Find your local number: https://ucsd.zoom.us/u/ab705B7wqi
URL:https://datascience.ucsd.edu/event/seminar-recourse-in-machine-learning/
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DTSTART;TZID=America/Los_Angeles:20210528T100000
DTEND;TZID=America/Los_Angeles:20210528T110000
DTSTAMP:20260607T060610
CREATED:20210219T230141Z
LAST-MODIFIED:20210219T230141Z
UID:10000316-1622196000-1622199600@datascience.ucsd.edu
SUMMARY:Seminar: Graph Theoretical Descriptors for Biomimetic Nanoparticles and Fibrous Nanocomposites
DESCRIPTION:Zoom ID: https://cuboulder.zoom.us/j/96007553384 \nPassword: i-aim \nNicholas Kotov (U Michigan)\n“Graph Theoretical Descriptors for Biomimetic Nanoparticles and Fibrous Nanocomposites” \nAbstract: Descriptors based on graph theory (GT) are needed to achieve accurate representations of two classes of nanostructures for the successful application of machine learning (ML). First\, a method to depict protein structure at molecular\, nanoscale\, and sub-microscale levels is described to predict complex formation and organization of protein-nanoparticle interfaces using several ML-algorithms. Second\, a methodology to utilize GT descriptors in nanofibrous composites is developed. The computational package Structural GT is introduced to automatically produce a GT description and structural descriptors of percolating nanoscale networks from micrographs. \nAbout the Speaker: Nicholas Kotov is the Irving Langmuir Distinguished Professor of Chemical Sciences and Engineering at the University of Michigan. He demonstrated that the ability to self-organize into complex structures is the unifying property of all inorganic nanostructures. He developed a family of bioinspired composite materials with a wide spectrum of properties that were previously unattainable in classical materials\, such as nacre-like ultrastrong\, transparent composites\, enamel-like\, stiff yet vibration-isolating composites\, and cartilage-like membranes with high strength and ion conductance. \n  \n 
URL:https://datascience.ucsd.edu/event/seminar-manifold-learning-for-free-energy-surface-exploration-2021-05-28/
CATEGORIES:Seminar
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DTSTART;TZID=America/Los_Angeles:20210527T130000
DTEND;TZID=America/Los_Angeles:20210527T140000
DTSTAMP:20260607T060610
CREATED:20210524T173111Z
LAST-MODIFIED:20230929T171611Z
UID:10000325-1622120400-1622124000@datascience.ucsd.edu
SUMMARY:Jeffrey L. Elman Distinguished Lecture Series: The Decision-Making Side of Machine Learning: Computational\, Inferential and Economic Perspectives
DESCRIPTION:Title: \nThe Decision-Making Side of Machine Learning: Computational\, Inferential and Economic Perspectives \nAbstract: \nMuch of the recent focus in machine learning has been on the pattern-recognition side of the field. I will focus instead on the decision-making side\, where many fundamental challenges remain. Some are statistical in nature\, including the challenges associated with multiple decision-making\, and some are algorithmic\, including the challenge of coordinated decision-making on distributed platforms. Finally\, others are economic\, involving learning systems that must cope with scarcity and competition. I will present recent progress on each of these fronts. \nBio: \nMichael I. Jordan is the Pehong Chen Distinguished Professor in the Department of Electrical Engineering and Computer Science and the Department of Statistics at the University of California\, Berkeley. He received his Masters in Mathematics from Arizona State University\, and earned his PhD in Cognitive Science in 1985 from the University of California\, San Diego. He was a professor at MIT from 1988 to 1998. His research interests bridge the computational\, statistical\, cognitive\, biological and social sciences. Prof. Jordan is a member of the National Academy of Sciences\, a member of the National Academy of Engineering\, and a member of the American Academy of Arts and Sciences. He is a Foreign Member of the Royal Society. He is a Fellow of the American Association for the Advancement of Science. He received the Ulf Grenander Prize from the American Mathematical Society in 2021\, the IEEE John von Neumann Medal in 2020\, the IJCAI Research Excellence Award in 2016\, the David E. Rumelhart Prize in 2015\, and the ACM/AAAI Allen Newell Award in 2009. He has been named a Neyman Lecturer and a Medallion Lecturer by the Institute of Mathematical Statistics. He was a Plenary Lecturer at the International Congress of Mathematicians in 2018. He is a Fellow of the AAAI\, ACM\, ASA\, CSS\, IEEE\, IMS\, ISBA and SIAM. \nIn 2016\, Professor Jordan was named the “most influential computer scientist” worldwide in an article in Science\, based on rankings from the Semantic Scholar search engine.
URL:https://datascience.ucsd.edu/event/jeffrey-l-elman-distinguished-lecture-series-the-decision-making-side-of-machine-learning-computational-inferential-and-economic-perspectives/
CATEGORIES:Guest Lecture,Seminar
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DTSTART;TZID=America/Los_Angeles:20210527T100000
DTEND;TZID=America/Los_Angeles:20210527T170000
DTSTAMP:20260607T060610
CREATED:20210525T234052Z
LAST-MODIFIED:20210525T234052Z
UID:10000327-1622109600-1622134800@datascience.ucsd.edu
SUMMARY:Symposium: Harnessing Data Science for Autonomous Computing Materials
DESCRIPTION:
URL:https://docs.google.com/forms/d/e/1FAIpQLSfLdY_if3STySU8_BCn9A8RZ0JtLeIWthrzCVAnOLeh9N6tOQ/viewform#new_tab
CATEGORIES:Guest Lecture,Webinar
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DTSTART;TZID=America/Los_Angeles:20210526T173000
DTEND;TZID=America/Los_Angeles:20210526T190000
DTSTAMP:20260607T060610
CREATED:20210505T211010Z
LAST-MODIFIED:20210505T211010Z
UID:10000322-1622050200-1622055600@datascience.ucsd.edu
SUMMARY:Data Science Insights: Causal Algorithmic Fairness and Transparency
DESCRIPTION:
URL:https://www.meetup.com/San-Diego-Machine-Learning/events/277718675/#new_tab
CATEGORIES:Guest Lecture,Seminar
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