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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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BEGIN:VEVENT
DTSTART;VALUE=DATE:20220214
DTEND;VALUE=DATE:20220219
DTSTAMP:20260607T081759
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:20260607T081759
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:20260607T081759
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:20260607T081759
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:20260607T081759
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:20260607T081759
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:20260607T081759
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:20260607T081759
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:20260607T081759
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:20260607T081759
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:20260607T081759
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:20260607T081759
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:20260607T081759
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:20260607T081759
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:20260607T081759
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
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20210601T140000
DTEND;TZID=America/Los_Angeles:20210601T153000
DTSTAMP:20260607T081759
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/
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20210528T100000
DTEND;TZID=America/Los_Angeles:20210528T110000
DTSTAMP:20260607T081759
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
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20210527T130000
DTEND;TZID=America/Los_Angeles:20210527T140000
DTSTAMP:20260607T081759
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
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20210527T100000
DTEND;TZID=America/Los_Angeles:20210527T170000
DTSTAMP:20260607T081759
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
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20210526T173000
DTEND;TZID=America/Los_Angeles:20210526T190000
DTSTAMP:20260607T081759
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
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20210514T100000
DTEND;TZID=America/Los_Angeles:20210514T110000
DTSTAMP:20260607T081759
CREATED:20210219T230141Z
LAST-MODIFIED:20210219T230141Z
UID:10000315-1620986400-1620990000@datascience.ucsd.edu
SUMMARY:Seminar: Determining the 3D Atomic Structures of Non-Crystalline Materials
DESCRIPTION:Zoom ID: https://cuboulder.zoom.us/j/2251625831\nPassword: i-aim \nJianwei (John) Miao\, UCLA\n“Determining the 3D Atomic Structures of Non-Crystalline Materials” \nAbstract:\nTBA \nAbout the Speaker:\n​TBA 
URL:https://datascience.ucsd.edu/event/seminar-manifold-learning-for-free-energy-surface-exploration-2021-05-14/
CATEGORIES:Seminar
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20210513T130000
DTEND;TZID=America/Los_Angeles:20210513T140000
DTSTAMP:20260607T081759
CREATED:20210511T213341Z
LAST-MODIFIED:20230324T155808Z
UID:10000326-1620910800-1620914400@datascience.ucsd.edu
SUMMARY:Some Results on Label Shift and Label Noise | Zachary Chase Lipton
DESCRIPTION:Title: Some Results on Label Shift and Label Noise \nAbstract: In this talk I will discuss distribution shift\, both as an obstacle to be overcome to achieve generalization\, and as a device for obtaining generalization guarantees. In the first part\, I will discuss the problem of label shift\, where the proportion among the labels can shift but the class conditional distributions do not change\, including connections to some practical problems and some theoretical results. Then I will discuss a new work in which we deliberately alter the distribution of training data in order to obtain a generalization guarantee. \nBio\nZachary Chase Lipton is the BP Junior Chair Assistant Professor of Operations Research and Machine Learning at Carnegie Mellon University and a Visiting Scientist at Amazon AI. He directs the Approximately Correct Machine Intelligence (ACMI) lab\, whose research spans core machine learning methods\, applications to clinical medicine and natural language processing\, and the impact of automation on social systems. Current focuses include robustness under distribution shift\, decision-making\, applications of causal thinking to practical high-dimensional settings that resist stylized causal models\, and AI ethics. He is the founder of the Approximately Correct blog (approximatelycorrect.com) and a co-author of Dive Into Deep Learning\, an interactive open-source book drafted entirely through Jupyter notebooks. He can be found on Twitter (@zacharylipton)\, GitHub (@zackchase)\, or his lab’s website (acmilab.org).
URL:https://datascience.ucsd.edu/event/hdsi-seminar-series-some-results-on-label-shift-and-label-noise/
CATEGORIES:Guest Lecture,HDSI Event,Seminar
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20210512T080000
DTEND;TZID=America/Los_Angeles:20210512T130000
DTSTAMP:20260607T081759
CREATED:20210505T211324Z
LAST-MODIFIED:20210505T211324Z
UID:10000323-1620806400-1620824400@datascience.ucsd.edu
SUMMARY:Data Science Day
DESCRIPTION:
URL:https://bit.ly/3eZYr1s#new_tab
CATEGORIES:Seminar,Webinar,Workshops
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20210506T130000
DTEND;TZID=America/Los_Angeles:20210506T140000
DTSTAMP:20260607T081759
CREATED:20210504T195111Z
LAST-MODIFIED:20210504T195111Z
UID:10000321-1620306000-1620309600@datascience.ucsd.edu
SUMMARY:HDSI Seminar Series: Unavoidable Tensions in Explaining Algorithmic Decisions
DESCRIPTION:Title: Unavoidable Tensions in Explaining Algorithmic Decisions \nAbstract: Recent developments in methods for explaining the decisions of machine learning models have been widely embraced for their ability to provide transparency and accountability without limiting model complexity or compelling model disclosure. Yet applying these methods is far from straightforward and they rarely prove a cure all. This talk identifies a number of unavoidable tensions that decision makers must navigate as they seek to employ these methods—and the deeply subjective judgment that must go into these considerations. \nPapers: This presentation is based–and builds–on two papers\, which are joint work with Andrew Selbst and Manish Raghavan: \n\nBarocas\, Solon\, Andrew D. Selbst\, and Manish Raghavan. “The hidden assumptions behind counterfactual explanations and principal reasons.” In Proceedings of the 2020 Conference on Fairness\, Accountability\, and Transparency\, pp. 80-89. 2020. https://dl.acm.org/doi/abs/10.1145/3351095.3372830\nSelbst\, Andrew D.\, and Solon Barocas. “The Intuitive Appeal of Explainable Machines.” Fordham Law Review 87\, no. 3 (2018): 1085. https://ir.lawnet.fordham.edu/cgi/viewcontent.cgi?article=5569&context=flr\n\nBio:  Solon Barocas is a Principal Researcher in the New York City lab of Microsoft Research\, an Adjunct Assistant Professor in the Department of Information Science at Cornell\, and Faculty Associate at the Berkman Klein Center for Internet & Society at Harvard. His research explores ethical and policy issues in artificial intelligence\, particularly fairness in machine learning\, methods for bringing accountability to automated decision-making\, and the privacy implications of inference. Solon co-founded the ACM conference on Fairness\, Accountability\, and Transparency (FAccT).
URL:https://datascience.ucsd.edu/event/hdsi-seminar-series-unavoidable-tensions-in-explaining-algorithmic-decisions/
CATEGORIES:Guest Lecture,HDSI Event,Seminar
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20210503T170000
DTEND;TZID=America/Los_Angeles:20210503T180000
DTSTAMP:20260607T081759
CREATED:20210423T214856Z
LAST-MODIFIED:20210423T214856Z
UID:10000176-1620061200-1620064800@datascience.ucsd.edu
SUMMARY:Seminar: Machine Learning for Spatio-Temporal Problems
DESCRIPTION:Or Cohen\, Lyft \nMachine Learning for Spatio-Temporal Problems \nMay 3\, 2021\, 5pm: \nJoin Zoom Meeting \nhttps://ucsd.zoom.us/j/95036249217?pwd=a2kwSFg2ZXMvc21yMHZLRVR2c0pqUT09  \n  \nAbstract: \nMachine learning is used widely at Lyft. A few examples include predicting where and when ride requests will happen\, predicting travel time between two locations or predicting the probability for a passenger to cancel his/her ride. Such applications of machine learning are one of the key factors that differentiate Lyft from traditional taxi companies. In this talk\, I will present a few of these use-cases in detail. I will describe the unique challenges that come when applying machine learning for such spatio-temporal problems\, in which the main features are location (e.g. where the passenger is) and time (e.g. when the ride is requested). I will describe the different techniques for discretizing these variables\, which models work best for such problems and what challenges still remain. \n  \nBio: \nOr Cohen is Staff Data Scientist at Lyft focusing on ETA (Expected Time of Arrival). He has a PhD in statistical physics. \nhttps://www.deeplearning.ai/blog/working-ai-at-the-office-with-research-scientist-or-cohen/ \n 
URL:https://datascience.ucsd.edu/event/seminar-machine-learning-for-spatio-temporal-problems/
CATEGORIES:Seminar
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DTSTART;TZID=America/Los_Angeles:20210503T130000
DTEND;TZID=America/Los_Angeles:20210503T140000
DTSTAMP:20260607T081759
CREATED:20210428T220805Z
LAST-MODIFIED:20210428T220805Z
UID:10000178-1620046800-1620050400@datascience.ucsd.edu
SUMMARY:HDSI Seminar Series: Causal Effect Inference: A Machine Learning Approach by Mihaela van der Schaar
DESCRIPTION:Title: Causal Effect Inference: A Machine Learning Approach \nAbstract \nA major challenge in the domain of healthcare is ascertaining whether a given treatment influences or determines an outcome—for instance\, whether there is a survival benefit to prescribing a certain medication. Current treatment guidelines have been developed with the “average” patient in mind\, but the reality is that treatments result in different effects and outcomes from one individual to another. \nUsing AI and machine learning\, we can endeavor to understand the effect of specific treatments on specific patients at specific times\, given their unique characteristics. This is what we call causal effect inference\, or individualized treatment effect inference. This is far from a straightforward undertaking\, however. In this seminar\, I will offer an introduction to individualized treatment effect inference for healthcare. I will explain the importance of this research area\, while also highlighting some key challenges\, formalisms\, methodologies\, and applications. \nBio \n \nMihaela van der Schaar is the John Humphrey Plummer Professor of Machine Learning\, Artificial Intelligence and Medicine at the University of Cambridge\, a Fellow at The Alan Turing Institute in London\, and a Chancellor’s Professor at UCLA. \nMihaela was elected IEEE Fellow in 2009. She has received numerous \nawards\, including the Oon Prize on Preventative Medicine from the University of Cambridge (2018)\, a National Science Foundation CAREER Award (2004)\, 3 IBM Faculty Awards\, the IBM Exploratory Stream Analytics Innovation Award\, the Philips Make a Difference Award and several best paper awards\, including the IEEE Darlington Award. \nMihaela’s work has also led to 35 USA patents (many widely cited and adopted in standards) and 45+ contributions to international standards for which she received 3 International ISO (International Organization for Standardization) Awards. \nIn 2019\, she was identified by National Endowment for Science\, Technology and the Arts as the most-cited female AI researcher in the UK. She was also elected as a 2019 “Star in Computer Networking and Communications” by N²Women. Her research expertise spans signal and image processing\, communication networks\, network science\, multimedia\, game theory\, distributed systems\, machine learning and AI. \nMihaela’s research focus is on machine learning\, AI and operations research for healthcare and medicine. \nIn addition to leading the van der Schaar Lab\, Mihaela is founder and director of the Cambridge Centre for AI in Medicine (CCAIM).
URL:https://youtu.be/yhUItwTP02o#new_tab
CATEGORIES:Guest Lecture,HDSI Event,Seminar
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20210430T100000
DTEND;TZID=America/Los_Angeles:20210430T110000
DTSTAMP:20260607T081759
CREATED:20210219T230141Z
LAST-MODIFIED:20210219T230141Z
UID:10000314-1619776800-1619780400@datascience.ucsd.edu
SUMMARY:Seminar: Bioinspired AI for Hierarchical Material Design: Modeling\, Design\, and Manufacturing
DESCRIPTION:Zoom ID: https://cuboulder.zoom.us/j/2251625831\nPassword: i-aim \nMarkus Buehler\, MIT\n“Bioinspired AI for Hierarchical Material Design: Modeling\, Design\, and Manufacturing” \nAbstract:\nTBA \nAbout the Speaker:\nTBA 
URL:https://datascience.ucsd.edu/event/seminar-manifold-learning-for-free-energy-surface-exploration-2021-04-30/
CATEGORIES:Seminar
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20210429T130000
DTEND;TZID=America/Los_Angeles:20210429T140000
DTSTAMP:20260607T081759
CREATED:20210427T184915Z
LAST-MODIFIED:20210427T184915Z
UID:10000177-1619701200-1619704800@datascience.ucsd.edu
SUMMARY:HDSI Seminar Series: A Modern Take on Huber Regression by Po Ling Loh
DESCRIPTION:Title: A modern take on Huber regression \nAbstract: \nIn the first part of the talk\, we discuss the use of a penalized Huber M-estimator for high-dimensional linear regression. We explain how a fairly straightforward analysis yields high-probability error bounds that hold even when the additive errors are heavy-tailed. However\, the parameter governing the shape of the Huber loss must be chosen in relation to the scale of the error distribution. We discuss how to use an adaptive technique\, based on Lepski’s method\, to overcome the difficulties traditionally faced by applying Huber M-estimation in a context where both location and scale are unknown. \nIn the second part of the talk\, we turn to a more complicated setting where both the covariates and responses may be heavy-tailed and/or adversarially contaminated. We show how to modify the Huber regression estimator by first applying an appropriate “filtering” procedure to the data based on the covariates. We prove that in low-dimensional settings\, this filtered Huber regression estimator achieves near-optimal error rates. We further show that the commonly used least trimmed squares and least absolute deviation estimators may similarly be made robust to contaminated covariates via the same covariate filtering step. This is based on joint work with Ankit Pensia and Varun Jog. \nBio: \nPo-Ling Loh received her PhD in Statistics from UC Berkeley in 2014. From 2014-2016\, she was an Assistant Professor of Statistics at the University of Pennsylvania. From 2016-2018\, she was an Assistant Professor of Electrical & Computer Engineering at UW-Madison\, and from 2019-2020\, she was an Associate Professor of Statistics at UW-Madison and a Visiting Associate Professor of Statistics at Columbia University. She began a position as a Lecturer in the Department of Pure Mathematics and Mathematical Statistics at the University of Cambridge in January 2021. Po-Ling’s current research interests include high-dimensional statistics\, robustness\, and differential privacy. She is a recipient of an NSF CAREER Award\, an ARO Young Investigator Award\, the IMS Tweedie and Bernoulli Society New Researcher Awards\, and a Hertz Fellowship.
URL:https://youtu.be/5f-5psF6XbA#new_tab
CATEGORIES:Guest Lecture,HDSI Event,Seminar
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20210421T173000
DTEND;TZID=America/Los_Angeles:20210421T190000
DTSTAMP:20260607T081759
CREATED:20210408T232049Z
LAST-MODIFIED:20210408T232049Z
UID:10000173-1619026200-1619031600@datascience.ucsd.edu
SUMMARY:Data Science Insights: Take a Hack at COVID!
DESCRIPTION:
URL:https://www.meetup.com/San-Diego-Machine-Learning/events/277120924/#new_tab
CATEGORIES:Seminar
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20210416T100000
DTEND;TZID=America/Los_Angeles:20210416T110000
DTSTAMP:20260607T081759
CREATED:20210219T230141Z
LAST-MODIFIED:20210219T230141Z
UID:10000313-1618567200-1618570800@datascience.ucsd.edu
SUMMARY:Seminar: Extracting the Structural\, Mechanical\, and Transport Properties of Nanostructured Materials
DESCRIPTION:Zoom ID: https://cuboulder.zoom.us/j/2251625831\nPassword: i-aim \nMargaret Murnane\, CU Boulder\n“Extracting the Structural\, Mechanical\, and Transport Properties of Nanostructured Materials” \nAbstract:\nTBA \nAbout the Speaker:\nTBA
URL:https://datascience.ucsd.edu/event/seminar-manifold-learning-for-free-energy-surface-exploration-2021-04-16/
CATEGORIES:Seminar
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