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-WR-CALNAME:Halıcıoğlu Data Science Institute - UC San Diego
X-ORIGINAL-URL:https://datascience.ucsd.edu
X-WR-CALDESC:Events for Halıcıoğlu Data Science Institute - UC San Diego
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/Los_Angeles
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:20200308T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20201101T090000
END:STANDARD
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
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20210623T173000
DTEND;TZID=America/Los_Angeles:20210623T190000
DTSTAMP:20260607T183534
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/
LOCATION:CA
CATEGORIES:Guest Lecture,Seminar
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20210614T150000
DTEND;TZID=America/Los_Angeles:20210614T170000
DTSTAMP:20260607T183534
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/
LOCATION:CA
CATEGORIES:HDSI Event
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20210611T100000
DTEND;TZID=America/Los_Angeles:20210611T110000
DTSTAMP:20260607T183534
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/
LOCATION:CA
CATEGORIES:Seminar
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20210601T140000
DTEND;TZID=America/Los_Angeles:20210601T153000
DTSTAMP:20260607T183534
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/
LOCATION:CA
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20210528T100000
DTEND;TZID=America/Los_Angeles:20210528T110000
DTSTAMP:20260607T183534
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/
LOCATION:CA
CATEGORIES:Seminar
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20210527T130000
DTEND;TZID=America/Los_Angeles:20210527T140000
DTSTAMP:20260607T183534
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/
LOCATION:CA
CATEGORIES:Guest Lecture,Seminar
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20210527T100000
DTEND;TZID=America/Los_Angeles:20210527T170000
DTSTAMP:20260607T183534
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
LOCATION:CA
CATEGORIES:Guest Lecture,Webinar
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20210526T173000
DTEND;TZID=America/Los_Angeles:20210526T190000
DTSTAMP:20260607T183534
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
LOCATION:CA
CATEGORIES:Guest Lecture,Seminar
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20210514T100000
DTEND;TZID=America/Los_Angeles:20210514T110000
DTSTAMP:20260607T183534
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/
LOCATION:CA
CATEGORIES:Seminar
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20210513T130000
DTEND;TZID=America/Los_Angeles:20210513T140000
DTSTAMP:20260607T183534
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/
LOCATION:CA
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:20260607T183534
CREATED:20210505T211324Z
LAST-MODIFIED:20210505T211324Z
UID:10000323-1620806400-1620824400@datascience.ucsd.edu
SUMMARY:Data Science Day
DESCRIPTION:
URL:https://bit.ly/3eZYr1s#new_tab
LOCATION:CA
CATEGORIES:Seminar,Webinar,Workshops
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20210506T130000
DTEND;TZID=America/Los_Angeles:20210506T140000
DTSTAMP:20260607T183534
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/
LOCATION:CA
CATEGORIES:Guest Lecture,HDSI Event,Seminar
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20210503T170000
DTEND;TZID=America/Los_Angeles:20210503T180000
DTSTAMP:20260607T183534
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/
LOCATION:CA
CATEGORIES:Seminar
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20210503T130000
DTEND;TZID=America/Los_Angeles:20210503T140000
DTSTAMP:20260607T183534
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
LOCATION:CA
CATEGORIES:Guest Lecture,HDSI Event,Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20210430T100000
DTEND;TZID=America/Los_Angeles:20210430T110000
DTSTAMP:20260607T183534
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/
LOCATION:CA
CATEGORIES:Seminar
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20210429T130000
DTEND;TZID=America/Los_Angeles:20210429T140000
DTSTAMP:20260607T183534
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
LOCATION:CA
CATEGORIES:Guest Lecture,HDSI Event,Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20210421T173000
DTEND;TZID=America/Los_Angeles:20210421T190000
DTSTAMP:20260607T183534
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
LOCATION:CA
CATEGORIES:Seminar
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20210416T100000
DTEND;TZID=America/Los_Angeles:20210416T110000
DTSTAMP:20260607T183534
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/
LOCATION:CA
CATEGORIES:Seminar
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20210415T130000
DTEND;TZID=America/Los_Angeles:20210415T140000
DTSTAMP:20260607T183534
CREATED:20210413T001611Z
LAST-MODIFIED:20260421T193637Z
UID:10000174-1618491600-1618495200@datascience.ucsd.edu
SUMMARY:Distinguished Lecture Series: Thoughts and Efforts on AI Meeting Production
DESCRIPTION:[vc_row][vc_column][vc_column_text css=””] \nThoughts and Efforts on AI Meeting Production\nAbstract: \nMachine Learning systems for complex tasks – such as controlling industrial manufacturing processes in real-time; or writing medical imaging case reports – are becoming increasingly sophisticated and consist of a large number of data\, model\, algorithm\, and system elements and modules. Traditional benchmark/leaderboard-driven bespoke approaches in the ML community are not suited to meet highly demanding industrial standards beyond algorithmic performance\, such as safety\, energy efficiency\, and scalability typically expected in production systems in the industry. In this talk\, I discuss some technical issues toward production- and industrial-AI from the following aspects: theoretical foundation for trustworthy and panoramic learning with all experiences; compositional strategies for building Pan-ML programs from Lego-like blocks; optimization methods for auto-tunning and federated learning; and systems framework for scaling up and scaling out ML productions. I will provide a few examples of our effects to address each of these challenges in the form of first principle formula\, new algorithms\, and software toolkits. \nBio: \nEric P. Xing is the President of the Mohamed bin Zayed University of Artificial Intelligence\, a Professor of Computer Science at Carnegie Mellon University\, and the Founder and Chairman of Petuum Inc.\, a 2018 World Economic Forum Technology Pioneer company that builds standardized artificial intelligence development platform and operating system for broad and general industrial AI applications. He completed his PhD in Computer Science at UC Berkeley. His main research interests are the development of machine learning and statistical methodology; and composable\, automatic\, and scalable computational systems\, for solving problems involving automated learning\, reasoning\, and decision-making in artificial\, biological\, and social systems. Prof Xing is a board member of the International Machine Learning Society; he has served as the Program Chair (2014) and General Chair (2019) of the International Conference of Machine Learning (ICML).[/vc_column_text][/vc_column][/vc_row]
URL:https://datascience.ucsd.edu/event/distinguished-lecture-series-thoughts-and-efforts-on-ai-meeting-production/
LOCATION:CA
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20210408T130000
DTEND;TZID=America/Los_Angeles:20210408T140000
DTSTAMP:20260607T183534
CREATED:20210331T191442Z
LAST-MODIFIED:20210331T191442Z
UID:10000172-1617886800-1617890400@datascience.ucsd.edu
SUMMARY:HDSI Seminar Series: Deep Learning for Market Design: Fairness\, Robustness\, and Expressiveness by John Dickerson
DESCRIPTION:Title\nDeep Learning for Market Design: Fairness\, Robustness\, and Expressiveness \nJohn Dickerson\, Assistant Professor of Computer Science\, University of Maryland; Chief Scientist\, Arthur AI \nAbstractThe design of revenue-maximizing auctions with strong incentive guarantees is a core concern of economic theory. Computational auctions enable online advertising\, sourcing\, spectrum allocation\, and myriad financial markets. Analytic progress in this space is notoriously difficult; since Myerson’s 1981 work characterizing single-item optimal auctions\, there has been limited progress outside of restricted settings. A recent paper by Dütting et al. circumvents analytic difficulties by applying deep learning techniques to\, instead\, approximate optimal auctions. Their RegretNet architecture can represent auctions with arbitrary numbers of items and participants; it is trained to be empirically strategyproof\, but the property is never exactly verified leaving potential loopholes for market participants to exploit. In parallel\, new research from Ilvento et al. and other groups has developed notions of fairness in the context of auction design. Inspired by these advances\, in this talk\, we discuss extensions of these techniques for approximating auctions using deep learning to address concerns of* fairness while maintaining high revenue and strong incentive guarantees;\n* certified robustness\, that is\, verification of claimed strategyproofness of deep learned auctions; and\n* expressiveness via different demand functions and other constraints.To enable that last point\, we propose a new architecture to learn incentive compatible\, revenue-maximizing auctions from sampled valuations\, which uses the Sinkhorn algorithm to perform a differentiable bipartite matching. Our new framework allows the network to learn strategyproof revenue-maximizing mechanisms in settings not learnable by the previous RegretNet architecture. \nThis talk covers hot-off-the-presses work led by PhD students Michael Curry\, Ping-yeh Chiang\, and Samuel Dooley\, and undergraduate students Kevin Kuo\, Uro Lyi\, Anthony Ostuni\, and Elizabeth Horishny. Papers have appeared at NeurIPS-20 or are currently under review; please check arXiv or get in touch for drafts. \nBio\nJohn P Dickerson is an Assistant Professor of Computer Science at the University of Maryland as well as Chief Scientist of Arthur AI\, an enterprise-focused AI/ML model monitoring firm. He is a recipient of awards such as the NSF CAREER Award\, IEEE Intelligent Systems AI’s 10 to Watch\, Google Faculty Research Award\, Google Research Scholar Award\, and paper awards and nominations at venues such as AAAI. His research centers on solving practical economic problems using techniques from computer science\, stochastic optimization\, and machine learning. He has worked extensively on theoretical and empirical approaches to organ exchange where his work has set policy at the UNOS nationwide kidney exchange; worldwide blood donation markets with Facebook; game-theoretic approaches to counter-terrorism and negotiation\, where his models have been deployed; and market design problems in industry (e.g.\, online advertising) through various startups. Dickerson received his PhD in computer science from Carnegie Mellon University.
URL:https://youtu.be/nWmI2bs_mcs#new_tab
LOCATION:CA
CATEGORIES:Colloquium,HDSI Event,Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20210402T100000
DTEND;TZID=America/Los_Angeles:20210402T110000
DTSTAMP:20260607T183534
CREATED:20210219T230141Z
LAST-MODIFIED:20210219T230141Z
UID:10000319-1617357600-1617361200@datascience.ucsd.edu
SUMMARY:Seminar: Manifold Learning for Free Energy Surface Exploration
DESCRIPTION:Zoom ID: https://cuboulder.zoom.us/j/2251625831\nPassword: i-aim \nYannis Kevrekidis\, Johns Hopkins University\n“Manifold Learning for Free Energy Surface Exploration” \nAbstract:\nTBA \nAbout the Speaker:\nTBA
URL:https://datascience.ucsd.edu/event/seminar-manifold-learning-for-free-energy-surface-exploration/2021-04-02/
LOCATION:CA
CATEGORIES:Seminar
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20210401T130000
DTEND;TZID=America/Los_Angeles:20210401T140000
DTSTAMP:20260607T183534
CREATED:20210331T183837Z
LAST-MODIFIED:20210331T183837Z
UID:10000171-1617282000-1617285600@datascience.ucsd.edu
SUMMARY:HDSI Seminar Series: On the Implicit Bias of Stochastic Gradient Descent with Moderate Learning Rate by Quanquan Gu
DESCRIPTION:Title: On the Implicit Bias of Stochastic Gradient Descent with Moderate Learning Rate \nHDSI Seminar Series \nQuanquan Gu\, Assistant Professor of Computer Science at UCLA \nAbstract: Understanding the algorithmic bias of stochastic gradient descent (SGD) is one of the key challenges in modern machine learning and deep learning theory. Most of the existing works\, however\, focus on very small or even infinitesimal learning rate regimes and fail to cover practical scenarios where the learning rate is moderate and annealing. In this talk\, I will introduce our attempt to characterize the particular regularization effect of SGD in the moderate learning rate regime by studying its behavior for optimizing an overparameterized linear regression problem. In this case\, SGD and GD are known to converge to the unique minimum-norm solution; however\, with the moderate and annealing learning rate\, we show that they exhibit different directional bias: SGD converges along the large eigenvalue directions of the data matrix\, while GD goes after the small eigenvalue directions. Furthermore\, we show that such directional bias does matter when early stopping is adopted\, where the SGD output is nearly optimal but the GD output is suboptimal. Our analysis explains several folk arts in practice used for SGD hyperparameter tuning\, such as (1) linearly scaling the initial learning rate with batch size; and (2) overrunning SGD with a high learning rate even when the loss stops decreasing. \nThis talk is based on joint work with Jingfeng Wu\, Difan Zou\, and Vladimir Braverman. \nBio: Quanquan Gu is an Assistant Professor of Computer Science at UCLA. His current research is in the area of artificial intelligence and machine learning\, with a focus on developing and analyzing nonconvex optimization algorithms for machine learning and building the theoretical foundations of deep learning. He received his Ph.D. degree in Computer Science from the University of Illinois at Urbana-Champaign in 2014. He is a recipient of the Yahoo! Academic Career Enhancement Award\, NSF CAREER Award\, Simons Berkeley Research Fellowship\, Adobe Data Science Research Award\, Salesforce Deep Learning Research Award\, and AWS Machine Learning Research Award.
URL:https://youtu.be/qLGvn4RJwl4#new_tab
LOCATION:CA
CATEGORIES:Colloquium,HDSI Event,Seminar
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20210324T173000
DTEND;TZID=America/Los_Angeles:20210324T190000
DTSTAMP:20260607T183534
CREATED:20210319T224647Z
LAST-MODIFIED:20210319T224647Z
UID:10000169-1616607000-1616612400@datascience.ucsd.edu
SUMMARY:Seminar: AutoNet: Automated Network Construction from Massive Text Corpora
DESCRIPTION:
URL:https://www.meetup.com/San-Diego-Machine-Learning/events/276913260/#new_tab
LOCATION:CA
CATEGORIES:Seminar
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20210319T100000
DTEND;TZID=America/Los_Angeles:20210319T110000
DTSTAMP:20260607T183534
CREATED:20210216T234644Z
LAST-MODIFIED:20210216T234644Z
UID:10000310-1616148000-1616151600@datascience.ucsd.edu
SUMMARY:Seminar: Cerebro: A Layered Data Platform for Scalable Deep Learning
DESCRIPTION:ZoomID: https://cuboulder.zoom.us/j/96007553384 \nPassword:i-aim \n\nArun Kumar\, UCSD\n“Cerebro: A Layered Data Platform for Scalable Deep Learning” \nAbstract:\nDeep learning (DL) is gaining popularity across many domains thanks to tools such as TensorFlow and easier access to GPUs. But building large-scale DL applications is still too resource-intensive and painful for all but the big tech firms. A key reason for this pain is the expensive model selection process needed to get DL to work well. Existing DL systems treat this process as an afterthought\, leading to massive resource wastage and a usability mess. To tackle these issues\, we present our vision of a first-of-its-kind data platform for scalable DL\, Cerebro\, inspired by lessons from the database world (https://adalabucsd.github.io/cerebro.html ). We elevate the DL model selection process with higher-level APIs already inherent in practice and devise a series of novel multi-query optimization techniques to substantially raise resource efficiency. In turn\, this can help improve scalability\, reduce resource costs and energy usage\, and improve DL user productivity. This talk will present our system design rationales and architecture\, our recent research and empirical results\, a discussion on how Cerebro is being applied to use cases in public health and enterprises\, and our future research plans. \nOverview paper (9pg): http://cidrdb.org/cidr2021/papers/cidr2021_paper25.pdf; Short video (10min): https://www.youtube.com/watch?v=8QfMvdlmdic \nAbout the Speaker:\nArun Kumar is an Assistant Professor in the Department of Computer Science and Engineering and the Halicioglu Data Science Institute at the University of California\, San Diego (https://adalabucsd.github.io). He is a member of the Database Lab and Center for Networked Systems and an affiliate member of the AI Group. His primary research interests are in data management and systems for machine learning/artificial intelligence-based data analytics. Systems and ideas based on his research have been released as part of the Apache MADlib open-source library\, shipped as part of products from Cloudera\, IBM\, Oracle\, and Pivotal\, and used internally by Facebook\, Google\, LogicBlox\, Microsoft\, and other companies. He is a recipient of two SIGMOD research paper awards\, a SIGMOD Research Highlight Award\, three distinguished reviewer awards from SIGMOD/VLDB\, the PhD dissertation award from UW-Madison CS\, an NSF CAREER Award\, a Hellman Fellowship\, a UCSD oSTEM Faculty of the Year Award\, and research award gifts from Google\, Oracle\, and VMware.
URL:https://datascience.ucsd.edu/event/seminar-cerebro-a-layered-data-platform-for-scalable-deep-learning/
LOCATION:CA
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20210310T170000
DTEND;TZID=America/Los_Angeles:20210310T180000
DTSTAMP:20260607T183534
CREATED:20210126T002015Z
LAST-MODIFIED:20230929T214012Z
UID:10000300-1615395600-1615399200@datascience.ucsd.edu
SUMMARY:STEMtorship Network: Lessons Learned: STEM Graduate Panel
DESCRIPTION:
URL:https://datascience.ucsd.edu/event/stemtorship-network-challenging-imposter-syndrome-2021-03-10/
LOCATION:CA
CATEGORIES:Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20210305T100000
DTEND;TZID=America/Los_Angeles:20210305T170000
DTSTAMP:20260607T183534
CREATED:20210224T180411Z
LAST-MODIFIED:20210224T180411Z
UID:10000318-1614938400-1614963600@datascience.ucsd.edu
SUMMARY:Webinar: NSF SCALE MoDL (Mathematical and Scientific Foundations of Deep Learning)
DESCRIPTION:Dear Colleagues\, \nThe Stimulating Collaborative Advances Leveraging Expertise in the Mathematical and Scientific Foundations of Deep Learning (SCALE MoDL) Management Team representing several NSF Divisions\, including MPS/DMS\, will be hosting a webinar scheduled for March 5\, 2021 starting at 1:00 PM Eastern Time. This is following up on the message we sent about last week regarding the NSF program solicitation NSF-21-561. Any members of the community who are interested in this funding opportunity are invited to participate in the webinar. \nThe webinar will have an open question and answer period for your questions submitted anonymously through the Zoom webinar Q&A feature. Participants may also submit questions in advance through the registration form or by sending an email to modl@nsf.gov. \nPlease use the registration link below to set up your participation. You are welcome to share this message with colleagues who might be interested. \n+++++++++++++++++++++++ \nParticipants should register (and may do so in advance) at the web page: \nhttps://nsf.zoomgov.com/webinar/register/WN_N9X5842dQ3-B6CkNvYXMwA \nAfter registering\, you will receive a confirmation email containing information about joining the webinar. \n+++++++++++++++++++++++ \nSCALE MoDL proposals are due by 5 p.m. submitter’s local time on May 12\, 2021.
URL:https://datascience.ucsd.edu/event/webinar-nsf-scale-modl-mathematical-and-scientific-foundations-of-deep-learning/
LOCATION:CA
CATEGORIES:Webinar
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20210301T100000
DTEND;TZID=America/Los_Angeles:20210301T170000
DTSTAMP:20260607T183534
CREATED:20210224T190853Z
LAST-MODIFIED:20210224T190853Z
UID:10000320-1614592800-1614618000@datascience.ucsd.edu
SUMMARY:HDSI 3rd Anniversary Symposium
DESCRIPTION:
URL:https://datascience.ucsd.edu/news-and-events/events/3rdanniversary/
LOCATION:CA
CATEGORIES:HDSI Event
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20210225T120000
DTEND;TZID=America/Los_Angeles:20210225T130000
DTSTAMP:20260607T183534
CREATED:20210216T231037Z
LAST-MODIFIED:20210216T231037Z
UID:10000307-1614254400-1614258000@datascience.ucsd.edu
SUMMARY:Seminar: A Deep Look into COVID and a New World of Innovation
DESCRIPTION:The next event in our public Deep Look series is coming up on Feb. 25 at noon with A Deep Look into COVID and a New World of Innovation. Chancellor Khosla will be joining a panel highlighting groundbreaking initiatives across the UC San Diego campus and research labs. Please help us spread the word — all are invited!… In addition to the chancellor\, the event features Associate Professor Omar Akbari\, Professor Rommie Amaro and Senior Diagnostic Adviser Dan Kolk… \nAlong with global health and economic devastation\, the COVID-19 pandemic has forged an unprecedented path to research and education innovation. Bold scientific advances and cooperation led to a novel vaccine developed in record time\, groundbreaking tools for detecting viruses and a pioneering vision for safely educating students. Join us on Feb. 25 as we discuss the trailblazing insights and innovations that led to the broad success of UC San Diego’s Return to Learn program with Chancellor Khosla\, along with scientists leading groundbreaking innovations related to detecting and analyzing SARS-CoV-2\, as well as the future of at-home diagnostic testing in response to COVID-19. \nRegistration: https://covid-innovation.eventbrite.com
URL:https://datascience.ucsd.edu/event/seminar-a-deep-look-into-covid-and-a-new-world-of-innovation/
LOCATION:CA
CATEGORIES:Seminar
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20210224T173000
DTEND;TZID=America/Los_Angeles:20210224T183000
DTSTAMP:20260607T183534
CREATED:20210126T004649Z
LAST-MODIFIED:20210126T004649Z
UID:10000308-1614187800-1614191400@datascience.ucsd.edu
SUMMARY:#Geisel50 Event at the Library | A Conversation with Kevin Young
DESCRIPTION:
URL:https://library.ucsd.edu/news-events/events/kevin-young/#new_tab
LOCATION:CA
CATEGORIES:Guest Lecture
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20210224T170000
DTEND;TZID=America/Los_Angeles:20210224T180000
DTSTAMP:20260607T183534
CREATED:20210126T002015Z
LAST-MODIFIED:20230929T213933Z
UID:10000299-1614186000-1614189600@datascience.ucsd.edu
SUMMARY:STEMtorship Network: Addressing Microaggressions in STEM
DESCRIPTION:
URL:https://datascience.ucsd.edu/event/stemtorship-network-challenging-imposter-syndrome-2021-02-24/
LOCATION:CA
CATEGORIES:Workshops
END:VEVENT
END:VCALENDAR