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X-WR-CALDESC:Events for Halıcıoğlu Data Science Institute - UC San Diego
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250423T130000
DTEND;TZID=America/Los_Angeles:20250423T143000
DTSTAMP:20260528T005748
CREATED:20250404T162241Z
LAST-MODIFIED:20250404T162241Z
UID:10000517-1745413200-1745418600@datascience.ucsd.edu
SUMMARY:LIVED EXPERIENCE RESEARCH SUMMIT
DESCRIPTION:Location: HDSI MPR \nTo watch via zoom please contact: hdsiassistant@ucsd.edu \nFormerly Incarcerated professor speaking on his lived experience in research. Researchers from\nSmarr Lab and MOSAIC lab. \nNoel Vest\, PhD\, is an Assistant Professor at the Boston University School of Public Health. His research interests include mental health\, substance use disorders\, and addiction recovery. As a formerly incarcerated scholar and a person in long-term recovery\, Dr. Vest is an advocate for social justice issues and public policy concerning substance use disorder recovery and prison reentry. He completed his PhD in Experimental Psychology from Washington State University and did his postdoctoral fellowship at Stanford University. \n 
URL:https://datascience.ucsd.edu/event/lived-experience-research-summit/
LOCATION:Halıcıoğlu Data Science Institute (HDSI)\, Room 123\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Guest Lecture,Seminar
ATTACH;FMTTYPE=image/jpeg:https://datascience.ucsd.edu/wp-content/uploads/2025/04/TUS_HS_HDSI_Collaboration_V4_Flyer-scaled.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250331T110000
DTEND;TZID=America/Los_Angeles:20250331T120000
DTSTAMP:20260528T005748
CREATED:20250318T195914Z
LAST-MODIFIED:20250318T195914Z
UID:10000513-1743418800-1743422400@datascience.ucsd.edu
SUMMARY:Seminar - Jeremy Bernstein - Metrized Deep Learning
DESCRIPTION:Jeremy Bernstein\n\nMIT CSAIL\n \n\n\nMonday\, March 31\n11:00 AM – 12:00 PM (PST) \nCSE 1242\n\nTitle: Metrized Deep Learning\n\n\nAbstract:\nWe build neural networks in a modular and programmatic way using software libraries like PyTorch and JAX. But optimization theory has not caught up to the flexibility of this paradigm\, and practical advances in neural net optimization are largely driven by heuristics. In this talk\, I will argue that to treat deep learning rigorously\, we must build our optimization theory programmatically and in lockstep with the neural network itself. To instantiate this idea we propose the “modular norm”\, which is a norm on the weight space of general neural architectures. The modular norm is constructed by stitching together norms on individual tensor spaces as the architecture is constructed. The modular norm has several applications: automatic Lipschitz certificates for general architectures in both weights and inputs; automatic learning rate transfer across scale; and most recently we built the duality theory for the modular norm\, leading to fast optimizers like “Muon”\, which set speed records for training transformers. We are building the theory of the modular norm into a software library called Modula to ease the development and deployment of metrized deep learning algorithms—you can find out more at https://modula.systems/.\n\n\n\nBiosketch:\n\nJeremy Bernstein is a postdoc in CSAIL at MIT advised by Phillip Isola. His goal is to uncover the computational and statistical laws of natural and artificial intelligence\, and thereby design learning systems that are more efficient\, more automatic and more useful in practice. He has a PhD in Computation & Neural Systems from Caltech and Bachelor’s and Master’s degrees in Physics from the University of Cambridge. He was a recipient of the NVIDIA graduate fellowship.
URL:https://datascience.ucsd.edu/event/seminar-jeremy-bernstein-metrized-deep-learning/
LOCATION:Computer Science & Engineering Building (CSE)\, Room 1242\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Guest Lecture,Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250221T140000
DTEND;TZID=America/Los_Angeles:20250221T153000
DTSTAMP:20260528T005748
CREATED:20250219T201049Z
LAST-MODIFIED:20250219T201049Z
UID:10000509-1740146400-1740151800@datascience.ucsd.edu
SUMMARY:HDSI Seminar - Victor Minces - The Sound of Data
DESCRIPTION:When Friday\, February 21st\nWhere: HDSI MPR 123\n\n\n\n\n\n\n\n\nTitle: The Sound of Data\n\nSpeaker: Victor Minces\n\nAbstract: In this talk\, Dr. Minces will give an overview of his career and how it led to the development of Listening to Waves\, a program that creates playful activities and web applications that connect music with science through data visualization and sonification. He will demonstrate how to use the applications created by his team to create surprising sounds and how the applications can help people understand the science of waves\, signal processing\, and music. He will discuss the impact of his program on children’s attitudes toward science and the education system. Further\, he will demonstrate new projects for sonifying data\, such as ‘the talking hand\,’ an application transforming hand movements into phonemes.\n\nBio: Dr. Minces is a neuroscientist of music\, sound artist\, performer\, and developer of educational programs centered on the STEM of music. He studied fine arts and physics at the University of Buenos Aires and obtained his Ph.D. in Computational Neurobiology at the University of California\, San Diego\, in Andrea Chiba’s laboratory. He is now a research scientist in the Department of Cognitive Science. He has studied how large neural networks in the brain encode sensory information and how the brain processes musical rhythm. He has created Listening to Waves\, a widely adopted program that develops web applications and activities for people to learn about the science of sound through playful exploration.
URL:https://datascience.ucsd.edu/event/hdsi-seminar-victor-minces-the-sound-of-data/
LOCATION:Halıcıoğlu Data Science Institute Room 123\, 3234 Matthews Ln\, La Jolla\, CA 92093\, USA
CATEGORIES:Guest Lecture,Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20241108T110000
DTEND;TZID=America/Los_Angeles:20241108T120000
DTSTAMP:20260528T005748
CREATED:20241104T224916Z
LAST-MODIFIED:20241104T224916Z
UID:10000505-1731063600-1731067200@datascience.ucsd.edu
SUMMARY:EnCORE Public Lecture -  Jon Kleinberg
DESCRIPTION:ucsd_encore-zoom is inviting you to a scheduled Zoom meeting.\nJoin Zoom Meeting\nhttps://ucsd.zoom.us/j/98016992761\nMeeting ID: 980 1699 2761\nOne tap mobile\n+16699006833\,\,98016992761# US (San Jose)\n+12133388477\,\,98016992761# US (Los Angeles)\nDial by your location\n+1 669 900 6833 US (San Jose)\n+1 213 338 8477 US (Los Angeles)\n+1 669 219 2599 US (San Jose)\nMeeting ID: 980 1699 2761\nFind your local number: https://ucsd.zoom.us/u/ab2INvbFzw
URL:https://datascience.ucsd.edu/event/encore-public-lecture-jon-kleinberg/
LOCATION:https://ucsd.zoom.us/j/98016992761
CATEGORIES:Guest Lecture
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240221T140000
DTEND;TZID=America/Los_Angeles:20240221T153000
DTSTAMP:20260528T005748
CREATED:20240205T171619Z
LAST-MODIFIED:20240220T172031Z
UID:10000436-1708524000-1708529400@datascience.ucsd.edu
SUMMARY:The Synergy between Machine Learning and the Natural Sciences | Max Welling
DESCRIPTION:Abstract: Traditionally machine learning has been heavily influenced by neuroscience (hence the name artificial neural networks) and physics (e.g. MCMC\, Belief Propagation\, and Diffusion based Generative AI). We have recently witnessed that the flow of information has also reversed\, with new tools developed in the ML community impacting physics\, chemistry and biology. Examples include faster DFT\, Force-Field accelerated MD simulations\, PDE Neural Surrogate models\, generating druglike molecules\, and many more. In this talk I will review the exciting opportunities for further cross fertilization between these fields\, ranging from faster (classical) DFT calculations and enhanced transition path sampling to traveling waves in artificial neural networks. \nBio: Prof. Max Welling is a research chair in Machine Learning at the University of Amsterdam and a Distinguished Scientist at MSR. He is a fellow at the Canadian Institute for Advanced Research (CIFAR) and the European Lab for Learning and Intelligent Systems (ELLIS) where he also serves on the founding board. His previous appointments include VP at Qualcomm Technologies\, professor at UC Irvine\, postdoc at U. Toronto and UCL under supervision of prof. Geoffrey Hinton\, and postdoc at Caltech under supervision of prof. Pietro Perona. He finished his PhD in theoretical high energy physics under supervision of Nobel laureate prof. Gerard ‘t Hooft. \nMax Welling has served as associate editor in chief of IEEE TPAMI from 2011-2015\, he serves on the advisory board of the Neurips foundation since 2015 and has been program chair and general chair of Neurips in 2013 and 2014 respectively. He was also program chair of AISTATS in 2009 and ECCV in 2016 and general chair of MIDL 2018. Max Welling is recipient of the ECCV Koenderink Prize in 2010 and the ICML Test of Time award in 2021. He directs the Amsterdam Machine Learning Lab (AMLAB) and co-directs the Qualcomm-UvA deep learning lab (QUVA) and the Bosch-UvA Deep Learning lab (DELTA).
URL:https://datascience.ucsd.edu/event/distinguished-colloquium-max-welling/
LOCATION:Halıcıoğlu Data Science Institute (HDSI)\, Room 123\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Guest Lecture
ATTACH;FMTTYPE=image/png:https://datascience.ucsd.edu/wp-content/uploads/2024/02/Max_Welling_DLS_1240x650.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20231011T140000
DTEND;TZID=America/Los_Angeles:20231011T153000
DTSTAMP:20260528T005748
CREATED:20231003T171716Z
LAST-MODIFIED:20231004T200415Z
UID:10000403-1697032800-1697038200@datascience.ucsd.edu
SUMMARY:Steampunk Data Science
DESCRIPTION:Abstract: How did scientists make sense of data before statistics and computing? This talk will explore this question by focusing on the discovery of vitamins\, which occurred in the early 20th century just before the advent of modern statistical methodology. I will describe the varied practices in experimentation and reporting and highlight the sorts of insights required to uncover what “works.” Through this discussion\, I will draw connections to contemporary data science tools to illustrate their pros and cons in facilitating discovery. \nBio: Benjamin Recht is a Professor in the Department of Electrical Engineering and Computer Sciences at the University of California\, Berkeley. His research has focused on applying mathematical optimization and statistics to problems in data analysis and machine learning. He is currently studying histories\, methods\, and theories of scientific validity and experimental design.
URL:https://datascience.ucsd.edu/event/steampunk-data-science/
LOCATION:3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Guest Lecture,Seminar
ATTACH;FMTTYPE=image/png:https://datascience.ucsd.edu/wp-content/uploads/2023/10/benrecht-social-flyer.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230626T120000
DTEND;TZID=America/Los_Angeles:20230626T130000
DTSTAMP:20260528T005748
CREATED:20230629T160446Z
LAST-MODIFIED:20230629T160646Z
UID:10000396-1687780800-1687784400@datascience.ucsd.edu
SUMMARY:The Emergence of General AI for Medicine | Dr. Peter Lee
DESCRIPTION:Dr. Peter Lee is Corporate Vice President of Research and Incubations at Microsoft where he leads Microsoft Research and incubates new research-powered products and lines of business in areas such as artificial intelligence\, computing foundations\, health\, and life sciences. He speaks and writes widely on science and technology trends\, including the attached NEJM article “Benefits\, Limits\, and Risks of GPT-4 as an AI Chatbot for Medicine” and recently published a book with Dr. Isaac Kohane\, “The AI Revolution in Medicine: GPT-4 and Beyond.” \nBefore joining Microsoft in 2010\, he was at DARPA\, where he established a new technology office that created operational capabilities in machine learning\, data science\, and computational social science. Prior to that\, he was a professor and the head of the computer science department at Carnegie Mellon University. Dr. Lee is a member of the National Academy of Medicine and serves on the Boards of Directors of several institutes for the Allen Institute for Artificial Intelligence\, the Brotman Baty Institute for Precision Medicine\, and the Kaiser Permanente Bernard J. Tyson School of Medicine. He served on President Obama’s Commission on Enhancing National Cybersecurity and led studies for PCAST and the National Academies. He has testified before both the US House Science and Technology Committee and the US Senate Commerce Committee.
URL:https://datascience.ucsd.edu/event/the-emergence-of-general-ai-for-medicine-dr-peter-lee/
LOCATION:SDSC\, The Auditorium\, 9836 Hopkins Dr\, La Jolla\, San Diego\, CA\, United States
CATEGORIES:Guest Lecture
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230519T120000
DTEND;TZID=America/Los_Angeles:20230519T140000
DTSTAMP:20260528T005748
CREATED:20230403T224454Z
LAST-MODIFIED:20230407T172236Z
UID:10000374-1684497600-1684504800@datascience.ucsd.edu
SUMMARY:Beyond 'The Algorithm': Fields\, Drama\, and Extreme Content Among Vegan Influencers
DESCRIPTION:Abstract: Existing research on polarization on social media platforms emphasizes the role of algorithmic “filter bubbles” and platform failure in amplifying extreme attitudes among online audiences. This article provides a different approach by focusing on online creators rather than audiences. Christin adapts field theory to examine the dynamics structuring exchanges between social media influencers\, which she analyzes as contentious position-takings within fields created and mediated by social media platforms. To demonstrate the relevance of this framework\, Christin draws on a qualitative study of vegan influencers on YouTube and Instagram. Two pathways shape the structuration of fields of social media production: drama\, or highly publicized scandals and interpersonal conflicts between influencers; and extreme content\, in which influencers and users reinforce their shared worldviews through niche and inflammatory content. Christin concludes by discussing the relevance of field theory for the study of social media and online disinformation more broadly.\n\n\nBiography: Angèle Christin is an assistant professor in the Department of Communication and affiliated faculty in the Sociology Department\, the Program in Science\, Technology\, and Society\, and the Center for Work\, Technology\, and Organization at Stanford University. She studies how algorithms and analytics transform professional values\, expertise\, and work practices.\n\n\nThe meeting will be held in person at PEB 721\, on the 7th floor of the UC San Diego Social Sciences Public Engagement Building. Lunch will be served. Vegan\, vegetarian\, and gluten-free options will be available. Kindly RSVP by May 17 at 2 p.m. if you are planning to attend (limited number of seats available!).\n\nRSVP here
URL:https://datascience.ucsd.edu/event/beyond-the-algorithm-fields-drama-and-extreme-content-among-vegan-influencers/
LOCATION:Public Engagement Building (PEB) 721\, 9625 Scholars Drive North MC 0305\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Guest Lecture
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230518T140000
DTEND;TZID=America/Los_Angeles:20230518T150000
DTSTAMP:20260528T005748
CREATED:20230323T181111Z
LAST-MODIFIED:20230513T000759Z
UID:10000364-1684418400-1684422000@datascience.ucsd.edu
SUMMARY:Scaling and Generalizing Approximate Bayesian Inference | David Blei
DESCRIPTION:Abstract: A core problem in statistics and machine learning is to approximate difficult-to-compute probability distributions. This problem is especially important in Bayesian statistics\, which frames all inference about unknown quantities as a calculation about a conditional distribution. In this talk I review and discuss innovations in variational inference (VI)\, a method that approximates probability distributions through optimization. VI has been used in myriad applications in machine learning and Bayesian statistics. It tends to be faster than more traditional methods\, such as Markov chain Monte Carlo sampling. \nAfter quickly reviewing the basics\, I will discuss two lines of research in VI. I first describe stochastic variational inference\, an approximate inference algorithm for handling massive datasets\, and demonstrate its application to probabilistic topic models of millions of articles. Then I discuss black box variational inference\, a generic algorithm for approximating the posterior. Black box inference easily applies to many models but requires minimal mathematical work to implement. I will demonstrate black box inference on deep exponential families—a method for Bayesian deep learning—and describe how it enables powerful tools for probabilistic programming. \n  \nBio: David Blei is a Professor of Statistics and Computer Science at Columbia University\, and a member of the Columbia Data Science\nInstitute. He studies probabilistic machine learning\, including its theory\, algorithms\, and application. David has received several awards for his research. He received a Sloan Fellowship (2010)\, Office of Naval Research Young Investigator Award (2011)\, Presidential Early Career Award for Scientists and Engineers (2011)\, Blavatnik Faculty Award (2013)\, ACM-Infosys Foundation Award (2013)\, a Guggenheim fellowship (2017)\, and a Simons Investigator Award (2019). He is the co-editor-in-chief of the Journal of Machine Learning Research. He is a fellow of the ACM and the IMS. \nWebsite : http://www.cs.columbia.edu/~blei/ \n  \nZoom Link : http://bit.ly/HDSI-Seminars
URL:https://datascience.ucsd.edu/event/david-blei/
LOCATION:SDSC\, The Auditorium\, 9836 Hopkins Dr\, La Jolla\, San Diego\, CA\, United States
CATEGORIES:Colloquium,Guest Lecture
ATTACH;FMTTYPE=image/jpeg:https://datascience.ucsd.edu/wp-content/uploads/2023/03/professordavisblei_headshot-scaled.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230511T120000
DTEND;TZID=America/Los_Angeles:20230511T140000
DTSTAMP:20260528T005748
CREATED:20230403T224301Z
LAST-MODIFIED:20230407T172325Z
UID:10000373-1683806400-1683813600@datascience.ucsd.edu
SUMMARY:Just Opt Out? Lessons Learned From a Decade of Evasion
DESCRIPTION:Abstract: With the rise of techlash\, an increasing number of users wish they could just say no to data tracking\, surveillance capitalism\, and the socially divisive effects of creepy technologies in our daily lives. But can we truly walk away from these systems? And what do we learn when we do? In this talk\, Vertesi tells the zany stories and practical tips that emerged from my extreme experiments in living digitally off Big Tech’s grid. Vertesi uncovers the sociological mechanisms that fuel these companies’ effective monetization of our lives and shares the hard-won tools and fresh insights Vertesi developed to help us all disable toxic tech and restore our right to choose.\n\nBio: Dubbed “Margaret Mead among the Starfleet” in the Times Literary Supplement\, Janet Vertesi is an associate professor of Sociology at Princeton University. She has spent fifteen years embedded with NASA’s robotic spacecraft teams as a sociologist of science and technology. Her publications range from the books Shaping Science and Seeing Like a Rover (both University of Chicago Press)\, edited collections digitalSTS (Princeton Press) and Representation in Scientific Practice Revisited (MIT Press)\, and top ranked journals and conference proceedings in the fields of the sociology of science and technology\, and human-computer and human-robot interaction. Currently co-editor of MIT Press’ Infrastructures series\, Vertesi is well known for her “Opt Out Experiments” evading capture in the personal data economy\, including a famous obfuscated pregnancy and trip to Disneyland. More at http://janet.vertesi.comand https://optoutproject.net\n\nThe meeting will be held in person at PEB 721\, on the 7th floor of the UC San Diego Social Sciences Public Engagement Building. Lunch will be served. Vegan\, vegetarian\, and gluten-free options will be available. Kindly RSVP by May 9 at 2 p.m. if you are planning to attend (limited number of seats available!).\n\nRSVP here
URL:https://datascience.ucsd.edu/event/just-opt-out-lessons-learned-from-a-decade-of-evasion/
LOCATION:Public Engagement Building (PEB) 721\, 9625 Scholars Drive North MC 0305\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Guest Lecture
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230414T120000
DTEND;TZID=America/Los_Angeles:20230414T140000
DTSTAMP:20260528T005748
CREATED:20230403T223545Z
LAST-MODIFIED:20230407T172438Z
UID:10000372-1681473600-1681480800@datascience.ucsd.edu
SUMMARY:The Interplay of Technology\, Ethics\, and Policy
DESCRIPTION:Abstract: Technology is often designed and deployed without critical reflection of the values that it embodies. Value trade-offs—between security and privacy\, free speech and dignity\, autonomy and human agency\, and different conceptions of fairness—abound in many technologies that are now achieving great scale in commonly used tech platforms. The decisions made by the people inside the companies deploying those technologies impose their value choices upon millions of users\, often with negative externalities that are now on full display.\n\n\nIn Reich’s work with policy experts and technologists (particularly “System Error: Where Big Tech Went Wrong and How We Can Reboot”)\, Reich tries to provide a multidisciplinary view—the perspectives of a philosopher\, a political scientist\, and a computer scientist\, respectively—to disentangle the systematic drivers that we believe have led to the ethical reckoning that Big Tech is now facing. Reich examines the value trade-offs arising in systems for algorithmic decision-making\, questions related to data gathering and privacy\, the impacts of AI and automation\, and the power of private platforms to control our information eco-system. Reich then discusses the ways we can all play a role in helping to shape technology and the policies that govern it with an eye toward achieving better outcomes for society. Case studies will be used to engage the audience in the conversation.\n\n\nBio: Rob Reich is the Professor of Political Science\, director of the Center for Ethics in Society\, co-director of the Center on Philanthropy and Civil Society\, and associate director of the Institute for Human-Centered AI. He is the author of “System Error: Where Big Tech Went Wrong and How We Can Reboot” (with Mehran Sahami and Jeremy M. Weinstein) and “Just Giving: Why Philanthropy is Failing Democracy and How It Can Do Better” (2018); “Digital Technology and Democratic Theory” (edited with Lucy Bernholz and Hélène Landemore\, 2021). His teaching and writing these days focuses on ethics\, policy\, and technology.\n\nThe meeting will be held in person at PEB 721\, on the 7th floor of the UC San Diego Social Sciences Public Engagement Building. Lunch will be served. Vegan\, vegetarian\, and gluten-free options will be available. Kindly RSVP by Apr. 12 at 2 p.m. if you are planning to attend (limited number of seats available!).\n\nRSVP
URL:https://datascience.ucsd.edu/event/the-interplay-of-technology-ethics-and-policy/
LOCATION:Public Engagement Building (PEB) 721\, 9625 Scholars Drive North MC 0305\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Guest Lecture
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230406T140000
DTEND;TZID=America/Los_Angeles:20230406T150000
DTSTAMP:20260528T005748
CREATED:20230302T000631Z
LAST-MODIFIED:20240402T224726Z
UID:10000354-1680789600-1680793200@datascience.ucsd.edu
SUMMARY:Intelligent mobile systems for equitable healthcare
DESCRIPTION:Access to even basic medical resources is greatly influenced by factors like an individual’s birth country and zip code. In this talk\, I will present my work on designing AI-based mobile systems for equitable healthcare. I will showcase three systems that are not only interesting from an AI standpoint but are also having real-world medical impact. The first system can detect ear infections using only a smartphone and a paper cone. The second system enables low-cost newborn hearing screening using inexpensive earphones. Lastly\, I will present an ambient sensing system that employs smart devices to detect emergent and life-threatening medical events such as cardiac arrest. Through these examples\, I will demonstrate how new applied machine learning and sensing approaches that generalize across hardware and work in real-world environments can help to address pressing societal problems. \nBio: Justin Chan is a Ph.D. candidate at the Paul G. Allen School of Computer Science and Engineering at the University of Washington. His work on smartphone-based ear infections is now FDA-listed and is available to select early access healthcare systems. His work on new-born hearing screening has led to an international effort called TUNE with the goal of bringing universal newborn hearing screening across Kenya as well as collaborations with NGOs such as the Global Foundation for Children with Hearing Loss to deploy this technology in Nepal and Mongolia. His work on contactless cardiac arrest detection has been licensed to a startup which has recently been acquired by Google. He was also a lead contributor for CovidSafe (now WA Notify)\, a COVID-19 contact tracing and symptom tracking app\, which became part of official efforts by the WA Department of Health to manage the pandemic. He has authored publications in interdisciplinary journals like Nature Biomedical Engineering\, Science Translational Medicine\, Nature Communications as well as Computer Science and Engineering venues like MobiSys\, MobiCom\, SIGCOMM\, SIGGRAPH Asia and UIST.
URL:https://datascience.ucsd.edu/event/justin-chan/
LOCATION:SDSC\, The Auditorium\, 9836 Hopkins Dr\, La Jolla\, San Diego\, CA\, United States
CATEGORIES:Guest Lecture,Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230306T100000
DTEND;TZID=America/Los_Angeles:20230306T100000
DTSTAMP:20260528T005748
CREATED:20230302T000627Z
LAST-MODIFIED:20230303T163910Z
UID:10000347-1678096800-1678096800@datascience.ucsd.edu
SUMMARY:Towards the Statistically Principled Design of ML Algorithms | Frederic Koehler
DESCRIPTION:What are the optimal algorithms for learning from data? Have we found them already\, or are better ones out there to be discovered? Making these questions precise\, and answering them\, requires taking on the mathematically deep interplay between statistical and computational constraints. It also requires reconciling our theoretical toolbox with surprising new phenomena arising from practice\, which seem to violate conventional rules of thumb regarding algorithm and model design. I will discuss progress along these lines: in terms of designing new algorithms for basic learning problems\, controlling generalization in large statistical models\, and understanding key statistical questions for generative modeling. \nBio: Frederic is currently a Motwani Postdoctoral Fellow in the Department of Computer Science at Stanford University. He was previously a research fellow at the Simons Institute\, and before that received his PHD in Mathematics and Statistics.
URL:https://datascience.ucsd.edu/event/frederic-koehler/
LOCATION:SDSC\, The Auditorium\, 9836 Hopkins Dr\, La Jolla\, San Diego\, CA\, United States
CATEGORIES:Guest Lecture
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20220509T180000
DTEND;TZID=America/Los_Angeles:20220509T193000
DTSTAMP:20260528T005748
CREATED:20220503T201421Z
LAST-MODIFIED:20220503T201421Z
UID:10000342-1652119200-1652124600@datascience.ucsd.edu
SUMMARY:Data Science and Biology - Speaker Series
DESCRIPTION:Data Science and Biology – Speaker Series\nHosted by Diversity in Data Science & CELLebrate\n  \nEvent Description\nData Science and Biology – Speaker Series provides an exciting opportunity to hear from experienced professionals about various data science techniques with applications to the biological sciences. All undergraduate/graduate students\, faculty\, and staff interested in STEM are encouraged to attend! RSVP Here! \nGain insight on cutting edge academic research pertaining to early detection of COVID-19 infection and unsupervised data analysis in neuroscience\, as well as an industry perspective of data analysis in biopharmaceutical manufacturing. See speaker bios on the next page! \nEach speaker will give a short presentation of their background and current research\, followed by a brief Q&A session. Attendees will have the opportunity to ask questions to each speaker. The speakers offer a wealth of experience from both academic and industry perspectives so we encourage everyone to attend and make the most of this opportunity! \n  \nEvent Details\n\nDate & Time: Monday\, May 9th\, 2022 @ 6:00-7:30 pm PST\n\n\n\n\nClick Here to Add Google Calendar Event Link\n\n\n\n\n\n\nHybrid Modality\n\n\n\n\nIn-Person Location: UCSD Main Campus\, Price Center West (Level 2)\, Thurgood Marshall College Room\nZoom ID: 204 876 8506\n\n\n\n  \nEvent Speakers\nBenjamin Smarr\, PhD\nAssistant Professor\, Bioengineering Department and Halicioglu Data Science Institute\, UCSD. \nSmarr Lab website \n  \nThe Smarr lab leverages domain expertise in biological rhythms and neuroendocrinology to uncover patterns in diverse sets of time series data that carry actionable information to impact health and cognitive performance. In 2020 he became the technical lead of the global collaborative TemPredict study\, which developed algorithms for early detection of COVID-19 infection\, and unique cyberinfrastructure to serve rapid\, collaborative explorations of population-scale\, personal time series data. Beyond the pandemic\, Dr. Smarr contributes broadly through science outreach\, popular media\, and industry liaisons. His personal passions lie in advancing women’s health\, and in increasing participant engagement to map physiological diversity in service to precision individual and public health. \n  \nGal Mishne\, PhD\nAssistant Professor\, Halicioglu Data Science Institute\, UCSD. \nUCSD website \n  \nDr. Mishne’s research is at the intersection of signal processing and machine learning for graph-based modeling\, processing and analysis of large-scale high-dimensional real-world data. She develops unsupervised and generalizable methods that allow the data to reveal its own story in an unbiased manner. Her research on unsupervised data analysis in neuroscience\, includes processing of raw neuroimaging data through discovery of neural manifolds to visualization of learning in artificial and biological neural networks. \n  \nDavid Conant\, PhD\nPrincipal Data Scientist at Resilience. \nResilience website \n  \nRSVP here! \n(All up to date information can be found here.)
URL:https://datascience.ucsd.edu/event/data-science-and-biology-speaker-series/
LOCATION:3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Guest Lecture,Seminar,Student Event
ATTACH;FMTTYPE=:
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20211117T140000
DTEND;TZID=America/Los_Angeles:20211117T150000
DTSTAMP:20260528T005748
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/
LOCATION:CA
CATEGORIES:Colloquium,Guest Lecture,HDSI Event,Industry,Seminar,Webinar,Workshops
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20210825T173000
DTEND;TZID=America/Los_Angeles:20210825T190000
DTSTAMP:20260528T005748
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/
LOCATION:CA
CATEGORIES:Guest Lecture,Industry,Seminar
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20210623T173000
DTEND;TZID=America/Los_Angeles:20210623T190000
DTSTAMP:20260528T005748
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:20210527T130000
DTEND;TZID=America/Los_Angeles:20210527T140000
DTSTAMP:20260528T005749
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:20260528T005749
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:20260528T005749
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:20210513T130000
DTEND;TZID=America/Los_Angeles:20210513T140000
DTSTAMP:20260528T005749
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:20210506T130000
DTEND;TZID=America/Los_Angeles:20210506T140000
DTSTAMP:20260528T005749
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:20210503T130000
DTEND;TZID=America/Los_Angeles:20210503T140000
DTSTAMP:20260528T005749
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:20210429T130000
DTEND;TZID=America/Los_Angeles:20210429T140000
DTSTAMP:20260528T005749
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:20210224T173000
DTEND;TZID=America/Los_Angeles:20210224T183000
DTSTAMP:20260528T005749
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:20210119T110000
DTEND;TZID=America/Los_Angeles:20210119T122000
DTSTAMP:20260528T005749
CREATED:20210114T004656Z
LAST-MODIFIED:20210114T004656Z
UID:10000291-1611054000-1611058800@datascience.ucsd.edu
SUMMARY:Data Science at Disney
DESCRIPTION:Talk title: “Data Science at Disney”\nEmily Kubicek\, Ph.D.\n \nBio: \nHi there  \nMy name is Emily Kubicek and I’m a Data Scientist at The Walt Disney Company. I am San Diego born and raised\, have two dogs named Nani and Loki\, and now live in Los Angeles. I graduated from San José State University with a BA in Communication Sciences and from Gallaudet University (“Deaf U”) in Washington D.C. with a Ph.D. in Cognitive Neuroscience. My academic research focuses on the brain and behavioral differences between signers of different levels\, with an emphasis in spatial cognition. I taught myself to code starting in 2017 and appreciate the unique perspectives both traditional and non-traditional backgrounds bring to tech. \nAbstract: “I didn’t realize Disney had data scientists!” Data in entertainment is no new thing. What is new\, and what is resulting in a large wave of new positions in this field\, are the tools that can be used in conjunction with this data. Digital streaming services\, cloud capabilities\, and high level machine learning techniques are but a few of the reasons entertainment companies are seeing the value\, or better yet\, the revenue\, in leveraging data intelligently. As digital platforms begin to grow in popularity\, so does the need for qualified individuals to not only manage\, but leverage this wealth of data. From theme parks to streaming\, tech and entertainment can come together to create personalized\, optimized\, and efficient experiences for clients and consumers. I’m here to share with you all of the cool things about working in data science at Disney\, but also want to show that choosing to pursue data science is really a choice that opens up doors to wherever you want to go. \nTuesday\, January 19\, COGS 9 – Introduction to Data Science class has its first guest speaker. \nThe lecture runs from 11:00a-12:20p. You need to be logged into your UCSD email account to join: \nhttps://ucsd.zoom.us/j/96921267727
URL:https://datascience.ucsd.edu/event/data-science-at-disney/
LOCATION:CA
CATEGORIES:Guest Lecture
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20210113T170000
DTEND;TZID=America/Los_Angeles:20210113T180000
DTSTAMP:20260528T005749
CREATED:20201130T220001Z
LAST-MODIFIED:20230712T092601Z
UID:10000144-1610557200-1610560800@datascience.ucsd.edu
SUMMARY:Re-Imagine Education with AI
DESCRIPTION:Talk Contents\n\nHow to apply AI Tech to Education?\nIntroduction of Riiid Labs & Technology\nFuture of AI-enabled Education\n$100K Riiid! AIEd Challenge on Kaggle\n\nDate/Time\n\nJanuary 13 (Wed)\, 5:00PM-6:00PM\nOpen to all members of HDSI Community\nPrerequisite: Intellectual curiosity to explore the future of education with AI\n\nGet More Info\n\nriiid.co\nednetchallenge.ai\nkaggle.com/c/riiid-test-answer-prediction\naaai.org/Conferences/AAAI-21\n\nHow To Join\n\nZoom Webinar Link: https://qrco.de/bbqd5M\nPlease RSVP through HDSI in order to best customize the talk to UCSD student needs
URL:https://datascience.ucsd.edu/event/re-imagine-education-with-ai/
LOCATION:CA
CATEGORIES:Guest Lecture,HDSI Event
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20201204T150000
DTEND;TZID=America/Los_Angeles:20201204T160000
DTSTAMP:20260528T005749
CREATED:20201203T183014Z
LAST-MODIFIED:20201203T183014Z
UID:10000150-1607094000-1607097600@datascience.ucsd.edu
SUMMARY:Seminar: Programmatically Building & Managing Training Data with Snorkel by Alex Ratner
DESCRIPTION:For the final Database Seminar of this quarter on Friday (Dec 4) at 3pm PT\, we have another exciting external speaker\, Alex Ratner. He is going to tell us about programmatic approaches to label data for modern ML/AI applications that reduce the burden of manual hand labeling. \nPlease find the talk details and Zoom details below. \nIf you’d like to get DB-related talk notices in future quarters\, please subscribe to the db-talks mailing list as described here: https://dbucsd.github.io/seminar \n— \nTitle:\nProgrammatically Building & Managing Training Data with Snorkel \nAbstract: \nOne of the key bottlenecks in building machine learning systems is creating and managing the massive training datasets that today’s models require. In this talk\, I will describe our work on Snorkel (snorkel.org)\, an open-source framework for building and managing training datasets\, and describe three key operators for letting users build and manipulate training datasets: labeling functions\, for labeling unlabeled data; transformation functions\, for expressing data augmentation strategies; and slicing functions\, for partitioning and structuring training datasets. These operators allow domain expert users to specify machine learning (ML) models entirely via noisy operators over training data\, expressed as simple Python functions—or even via higher level NL or point-and-click interfaces—leading to applications that can be built in hours or days\, rather than months or years\, and that can be iteratively developed\, modified\, versioned\, and audited. I will describe recent work on modeling the noise and imprecision inherent in these operators\, and using these approaches to train ML models that solve real-world problems\, including recent state-of-the-art results on benchmark tasks and real-world industry\, government\, and medical deployments. \nSpeaker bio: \nAlex Ratner is the co-founder and CEO of Snorkel AI\, Inc.\, which supports the open source Snorkel library and develops Snorkel Flow\, an end-to-end system for building machine learning applications\, and an Assistant Professor of Computer Science at the University of Washington. Prior to Snorkel AI and UW\, he completed his PhD in CS advised by Christopher Ré at Stanford\, where his research focused on applying data management and statistical learning techniques to emerging machine learning workflows\, such as creating and managing training data\, and applying this to real-world problems in medicine\, knowledge base construction\, and more. \n— \nArun Kumar is inviting you to a scheduled Zoom meeting. \nPlease download and import the following iCalendar (.ics) files to your calendar system.\nWeekly: https://ucsd.zoom.us/meeting/tJwqceGpqjMvGNy7Td1vjiCqmP4Rb3_iCDIG/ics?icsToken=98tyKuCgqT0iG9CdtRuPRow-B4_oWevwiFxYjY1EyyvhUjZZayDnO9IWALAsL9Hz \nJoin Zoom Meeting\nhttps://ucsd.zoom.us/j/98768148528?pwd=NFFrSVpySWlEOU9WT3FhWVlkTmZFUT09 \nMeeting ID: 987 6814 8528\nPassword: 827714 \nOne tap mobile\n+12133388477\,\,98768148528# US (Los Angeles)\n+16692192599\,\,98768148528# US (San Jose) \nDial by your location\n+1 213 338 8477 US (Los Angeles)\n+1 669 219 2599 US (San Jose)\n+1 669 900 6833 US (San Jose)\n833 548 0276 US Toll-free\n833 548 0282 US Toll-free\n877 853 5257 US Toll-free\n888 475 4499 US Toll-free\nMeeting ID: 987 6814 8528\nFind your local number: https://ucsd.zoom.us/u/ajIyGW1ve
URL:https://datascience.ucsd.edu/event/seminar-programmatically-building-managing-training-data-with-snorkel-by-alex-ratner/
LOCATION:CA
CATEGORIES:Guest Lecture,Seminar
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DTSTART;TZID=America/Los_Angeles:20201201T130000
DTEND;TZID=America/Los_Angeles:20201201T140000
DTSTAMP:20260528T005749
CREATED:20201124T214441Z
LAST-MODIFIED:20201124T214441Z
UID:10000140-1606827600-1606831200@datascience.ucsd.edu
SUMMARY:Data Systems for Machine Learning Lecture
DESCRIPTION:We have 3 exciting invited talks from industry in my graduate CSE course titled “Data Systems for Machine Learning” (coursewebpage). The topics span ML deployment platforms\, deep learning training platforms\, and GPU acceleration for data science workloads. I’d like to open up these talks to the wider CSE and HDSI talks audiences. \n  \nThe full 3-talk schedule is attached for your convenience. Each talk’s details will be emailed a few days ahead of time. All talks will be recorded. The speakers have consented to the recordings being posted publicly on Youtube. \n  \nJoin Zoom Meeting \nhttps://ucsd.zoom.us/j/92636219457?pwd=bWpLTFJackdzUXVlenVXcTJHU0Q3dz09 \n  \nMeeting ID: 926 3621 9457\nPassword: 579720 \n  \nOnetap mobile\n+12133388477\,\,92636219457# US (Los Angeles)\n+16692192599\,\,92636219457# US (San Jose) \n  \nDialby your location\n+1 213 338 8477 US (Los Angeles)\n+1 669 219 2599 US (San Jose)\n+1 669 900 6833 US (San Jose)\nMeeting ID: 926 3621 9457\nFind your local number: https://ucsd.zoom.us/u/aeuwWB6NW7
URL:https://datascience.ucsd.edu/event/data-systems-for-machine-learning-lecture/
LOCATION:CA
CATEGORIES:Guest Lecture
ATTACH;FMTTYPE=:
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20201124T130000
DTEND;TZID=America/Los_Angeles:20201124T140000
DTSTAMP:20260528T005749
CREATED:20201124T213831Z
LAST-MODIFIED:20201124T213831Z
UID:10000138-1606222800-1606226400@datascience.ucsd.edu
SUMMARY:Systems Problems in Deep Learning by Dr. Angela Jiang
DESCRIPTION:Title: \nSystems problems in deep learning \nAbstract:\n\nAdvances in deep learning are enabling novel applications like autonomous vehicles and advanced medical imaging. But despite the overwhelming promise of deep learning\, ML engineers still struggle with performing basic tasks like distributing training\, running hyperparameter searches\, and reproducing experiment results. This is likely due to the fact that deep learning presents a new programming paradigm that requires us to rethink how we build our infrastructure and tooling. In this talk\, I describe how progress in machine learning systems from academia and industry have been integral in powering deep learning. I’ll share my own experiences as an ML systems researcher in graduate school about the variety of infrastructure challenges I faced\, and how rapid progress in DL software systems are addressing these challenges. \nSpeaker bio:  \nAngela Jiang is a product manager at Determined AI. Earlier this year\, she graduated from the Computer Science Department in Carnegie Mellon University with a Ph.D.\, where she focused on optimizing training and inference software systems for deep learning. \n— \nArun Kumar is inviting you to a scheduled Zoom meeting.\nTopic: UCSD CSE 291D/234 Fall 2020: Industry Guest Lectures \nTime: Nov 17\, 2020 01:00 PM Pacific Time (US and Canada)\nEvery week on Tue\, 3 occurrence(s)\nNov 17\, 2020 01:00 PM\nNov 24\, 2020 01:00 PM\nDec 1\, 2020 01:00 PM \nPlease download and import the following iCalendar (.ics) files to your calendar system.\nWeekly: https://ucsd.zoom.us/meeting/tJYrdO-qrzIuH9Orred9KVdsK-xg6kSMgDnI/ics?icsToken=98tyKuCqqDgsGNWctByARowQBIjCM-rzmFhbgvpyjg3gB3l4VAflHa9aNeR0I_XX \nJoin Zoom Meeting \nhttps://ucsd.zoom.us/j/92636219457?pwd=bWpLTFJackdzUXVlenVXcTJHU0Q3dz09 \nMeeting ID: 926 3621 9457\nPassword: 579720 \nOne tap mobile\n+12133388477\,\,92636219457# US (Los Angeles)\n+16692192599\,\,92636219457# US (San Jose) \nDial by your location\n+1 213 338 8477 US (Los Angeles)\n+1 669 219 2599 US (San Jose)\n+1 669 900 6833 US (San Jose)\nMeeting ID: 926 3621 9457\nFind your local number: https://ucsd.zoom.us/u/aeuwWB6NW7
URL:https://datascience.ucsd.edu/event/systems-problems-in-deep-learning-by-dr-angela-jiang/
LOCATION:CA
CATEGORIES:Guest Lecture
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