BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Data Science Initiative - ECPv6.3.7//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Data Science Initiative
X-ORIGINAL-URL:https://datascience.uci.edu
X-WR-CALDESC:Events for Data Science Initiative
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:20140309T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20141102T090000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:20150308T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20151101T090000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20150220T150000
DTEND;TZID=America/Los_Angeles:20150220T160000
DTSTAMP:20260517T120145
CREATED:20160502T110039Z
LAST-MODIFIED:20160502T110039Z
UID:1710-1424444400-1424448000@datascience.uci.edu
SUMMARY:Real Time Analytics @Twitter
DESCRIPTION:In this talk\, we will give an overview of real time analytics\, discuss the twitter real time data pipeline and how Storm is used for extracting analytics. We will also discuss the challenges we faced and lessons we have learned while building this infrastructure at Twitter. \n \nDescription: \n \nIn this talk\, we will give an overview of real time analytics\, discuss the twitter real time data pipeline and how Storm is used for extracting analytics. We will also discuss the challenges we faced and lessons we have learned while building this infrastructure at Twitter. \nDate: February 20\, 2015\nTime: 3:00 pm to 4:00 pm\nLocation: Donald Bren Hall\, Room 3011 (part of the ISG Seminar Series\nSpeaker: Dr. Karthik Ramasamy\, Twitter \nRSVP is not required. \nReal time analytics seems to be a buzz word these days. Twitter identified the need for real time analytics early on and invested in a massive data pipeline that collects\, aggregates\, processes large volumes of data in real time. At the heart of the pipeline is Twitter Storm\, a real-time stream processing engine widely used in Twitter. Storm is used for real-time data analytics\, time series aggregation\, and powering real-time features like trending topics. In this talk\, we will give an overview of real time analytics\, discuss the twitter real time data pipeline and how Storm is used for extracting analytics. We will also discuss the challenges we faced and lessons we have learned while building this infrastructure at Twitter. \nSPEAKER BIO:\nKarthik is the engineering manager and technical lead for Real Time Analytics at Twitter. He has two decades of experience working in parallel databases\, big data infrastructure and networking. He cofounded Locomatix\, a company that specializes in real timestreaming processing on Hadoop and Cassandra using SQL that was acquired by Twitter. Before Locomatix\, he had a brief stint with Greenplum where he worked on parallel query scheduling. Greenplum was eventually acquired by EMC for more than $300M. Prior to Greenplum\, Karthik was at Juniper Networks where he designed and delivered platforms\, protocols\, databases and high availability solutions for network routers that are widely deployed in the Internet. Before joining Juniper\, at the University of Wisconsin he worked extensively in parallel database systems\, query processing\, scale out technologies\, storage engine and online analytical systems. Several of those research results were spun out as a company later acquired by Teradata. Karthik is the author of several publications\, patents and one of the best selling book “Network Routing: Algorithms\, Protocols and Architectures.” He has a Ph.D. in Computer Science from UW Madison with a focus on databases.
URL:https://datascience.uci.edu/event/1710/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20150209T160000
DTEND;TZID=America/Los_Angeles:20150209T171500
DTSTAMP:20260517T120145
CREATED:20160502T105708Z
LAST-MODIFIED:20160502T105708Z
UID:1707-1423497600-1423502100@datascience.uci.edu
SUMMARY:Privacy in the Land of Plenty
DESCRIPTION:Privacy-preserving data analysis has a large literature that spans several disciplines.  “Differential privacy” — a notion tailored to situations in which data are plentiful — has provided a theoretically sound and powerful framework\, and given rise to an explosion of research.  We will review the definition of differential privacy\, describe some algorithmic contributions\, and conclude with a surprising application. \n \nDescription: \n \nA presentation by Dr. Cynthia Dwork\, Microsoft Research \nTime: 4:00 p.m. – 5:15 p.m. \nWhere: Donald Bren Hall\, Room 6011 (directions) \n>> View event flyer. \nPrivacy-preserving data analysis has a large literature that spans several disciplines.  “Differential privacy” — a notion tailored to situations in which data are plentiful — has provided a theoretically sound and powerful framework\, and given rise to an explosion of research.  We will review the definition of differential privacy\, describe some algorithmic contributions\, and conclude with a surprising application. \nBio \nDr. Cynthia Dwork\, Distinguished Scientist at Microsoft Research\, is renowned for placing privacy-preserving data analysis on a mathematically rigorous foundation. A cornerstone of this work is differential privacy\, a strong privacy guarantee frequently permitting highly accurate data analysis. Dr. Dwork has also made seminal contributions in cryptography and distributed computing\, and is a recipient of the Edsger W. Dijkstra Prize\, recognizing some of her earliest work establishing the pillars on which every fault-tolerant system has been built for decades. She is a member of the National Academy of Sciences and the National Academy of Engineering\, and a Fellow of the American Academy of Arts and Sciences. \nFor more information\, please visit the UCI Department of Computer Science Distinguished Lecture Series.
URL:https://datascience.uci.edu/event/privacy-in-the-land-of-plenty/
ATTACH;FMTTYPE=image/jpeg:https://datascience.uci.edu/wp-content/uploads/2015/01/2010-09-28_dwork.mov.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20150205T160000
DTEND;TZID=America/Los_Angeles:20150205T170000
DTSTAMP:20260517T120145
CREATED:20160502T105555Z
LAST-MODIFIED:20160502T105555Z
UID:1706-1423152000-1423155600@datascience.uci.edu
SUMMARY:Modeling wealth\, behavior\, and mobility with terabyte-scale network data
DESCRIPTION:A presentation by Joshua Blumenstock\, Assistant Professor\, Information School\, University of Washington\n \n \nDescription: \n\nA presentation by Joshua Blumenstock\, Assistant Professor\, Information School\, University of Washington\n \nDate: Thursday\, February 5\, 2015 \nTime: 4:00 – 5:00 p.m.\nLocation: Donald Bren Hall Room 4011 (directions)\nRSVP is not required\n \nCo-sponsored by the UCI Data Science Initiative (datascience.uci.edu) and the Center for Intelligent Systems and Machine Learning (cml.ics.uci.edu)\n \nAbstract:\nIn recent years\, the rapid proliferation of mobile phones in developing countries has provided billions of individuals with novel opportunities for social and economic interaction. Concurrently\, the data generated by mobile phone networks is enabling new data-intensive methods for studying the social and economic behavior of individuals in resource-constrained environments. After all\, these data reflect much more than simple communications activity: they capture the structure of social networks\, decisions about expenditures and consumption\, patterns of travel and mobility\, and the regularity of daily routines. In this talk\, I will discuss the results from two recent projects that derive behavioral insights from mobile phone data. The first study uses data on Mobile Money transfers in Rwanda and microeconomic models to better understand the motives that cause people to send money to friends and family in times of need. The second project combines call data with follow-up phone surveys to investigate the extent to which it is possible to predict an individual’s wealth and happiness based on his or her prior history of phone calls and several supervised learning models. These projects are enabled by generous support from the Institute for Money\, Technology\, and Financial Inclusion; Intel; the Gates Foundation; and the NSF.\n \nBio: \nJoshua Blumenstock is an Assistant Professor at the Information School\, an Adjunct Assistant Professor of Computer Science and Engineering\, and a founder of the Data Science and Analytics Lab at the University of Washington. His research develops theory and methods for the analysis of large-scale behavioral data\, with a focus on how such data can be used to better understand poverty and economic development. Recent projects combine field experiments with big spatiotemporal network data to model decision-making in poor and conflict-affected regions of the world. Prior to joining UW\, Joshua was a postdoc in the Department of Economics at Yale University. He has a Ph.D. in Information Science and a M.A. in Economics from U.C. Berkeley\, and Bachelor’s degrees in Computer Science and Physics from Wesleyan University. He is a recipient of the Intel Faculty Early Career Honor and a former fellow of the Thomas J. Watson Foundation and the Harvard Institutes of Medicine.
URL:https://datascience.uci.edu/event/modeling-wealth-behavior-and-mobility-with-terabyte-scale-network-data/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20150130T150000
DTEND;TZID=America/Los_Angeles:20150130T160000
DTSTAMP:20260517T120145
CREATED:20160502T105432Z
LAST-MODIFIED:20160502T105432Z
UID:1705-1422630000-1422633600@datascience.uci.edu
SUMMARY:Large-Scale Machine Learning with the SimSQL System
DESCRIPTION:A presentation by Professor Chris Jermaine\, Computer Science\,  Rice University\n\n\n\n \nDescription: \n\nA presentation by Professor Chris Jermaine\, Computer Science\,  Rice University\n \nDate: Friday\, January 30\, 2015\nTime: 3:00 to 4:00 pm\nLocation: Donald Bren Hall\, Room 3011 (directions)\n\nIn this talk\, Professor Chris Jermaine will describe the SimSQL system\, which is a platform for writing and executing statistical codes over large data sets\, particularly for machine learning applications. Codes that run on SimSQL can be written in a very high-level\, declarative language called Buds. A Buds program looks a lot like a mathematical specification of an algorithm\, and statistical codes written in Buds are often just a few lines long.At its heart\, SimSQL is really a relational database system\, and like other relational systems\, SimSQL is designed to support data independence. That is\, a single declarative code for a particular statistical inference problem can be used regardless of data set size\, compute hardware\, and physical data storage and distribution across machines. One concern is that a platform supporting data independence will not perform well. But we’ve done extensive experimentation\, and have found that SimSQL performs as well as other competitive platforms that support writing and executing machine learning codes for large data sets.Bio:Chris Jermaine is an associate professor of computer science at Rice University. He is the recipient of an Alfred P. Sloan Foundation Research Fellowship\, a National Science Foundation CAREER award\, and an ACM SIGMOD Best Paper Award. In his spare time\, Chris enjoys outdoor activities such as hiking\, climbing\, and whitewater boating. In one particular exploit\, Chris and his wife floated a whitewater raft (home-made from scratch using a sewing machine\, glue\, and plastic) over 100 miles down the Nizina River (and beyond) in Alaska.
URL:https://datascience.uci.edu/event/large-scale-machine-learning-with-the-simsql-system/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20150123T090000
DTEND;TZID=America/Los_Angeles:20150123T173000
DTSTAMP:20260517T120145
CREATED:20160502T104300Z
LAST-MODIFIED:20160502T104300Z
UID:1704-1422003600-1422034200@datascience.uci.edu
SUMMARY:Introduction to R Short Course
DESCRIPTION:This course provides an introduction to the fundamentals of the R language. \n\n \n\n\n \nDescription: \nThis course provides an introduction to the fundamentals of the R language. \nIn this course\, you will learn how to program in R and how to effectively use R to do data analysis. The course covers introduction to data/object types in R\, reading data into R\, creating data graphics\, accessing and installing R packages\, writing R functions\, fitting statistical models including regression models and performing statical tests as t-test\, and ANOVA. Practical examples will be provided during the course. \nDate: January 23\, 2015 \nTime: 9 a.m. to 5:30 p.m. with lunch provided \nLocation: Donald Bren Hall\, Room 4011 \nInstructors: Sepehr Akhavan and Homer Strong from the Department of Statistics\, UC Irvine. \nPre-requisites: 1) familiarity with basic statistical concepts\, and 2) basic programming knowledge. For the tutorial\, bring a laptop with R 3.1.2 and R Studio downloaded and installed. \n Application deadline is January 1\, 2015.
URL:https://datascience.uci.edu/event/1704/
ATTACH;FMTTYPE=image/jpeg:https://datascience.uci.edu/wp-content/uploads/2014/09/r-project-logo.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20150110T100000
DTEND;TZID=America/Los_Angeles:20150110T200000
DTSTAMP:20260517T120145
CREATED:20160427T143752Z
LAST-MODIFIED:20160427T143752Z
UID:1700-1420884000-1420920000@datascience.uci.edu
SUMMARY:Data Science Hackathon
DESCRIPTION:The UCI Data Science Initiative is hosting a Hackathon with the goal of analyzing data from Reddit\, a user-generated news platform. \nParticipants will work in teams to identify an interesting question and use data analytics to answer it. All teams will give a brief presentation at the end of the day for a chance to win a gift card. \n\n\n\n  \n \nThe UCI Data Science Initiative is hosting a Hackathon with the goal of analyzing data from Reddit\, a user-generated news platform. \nParticipants will work in teams to identify an interesting question and use data analytics to answer it. All teams will give a brief presentation at the end of the day for a chance to win a gift card. \nWhen: January 10\, 2015 from 10:00 a.m. to 8 p.m. (lunch and dinner will be provided).\nWho: Graduate students from all backgrounds are encouraged to participate\, though prior experience with programming or data analysis is preferred.\nWhere: Bren Hall\, Room 6011\, UC Irvine Campus\nWhy: Each member of the three winning teams will receive a $10 gift card. \nPossible tasks include: \n• Build models to predict which content will make it to the front page\n• Summarize and visualize Reddit data to show hidden aspects of the Reddit community\n• Rank users by influence\n• Anything else you can dream up! \nAbout Reddit\nReddit is an entertainment\, social networking\, and news website where registered community members can submit content\, such as posts or direct links. Only registered users can then vote submissions “up” or “down” to organize the posts and determine their position on the site’s pages. Reddit data is multifaceted and includes text\, URLs\, timestamps\, user IDs\, topics\, and popularity scores. \nDetails\nParticipants must bring there own computer with appropriate software downloaded. There are no restrictions on what programming language or tools you use (e.g. Python\, R\, Matlab). Each participant must register to attend the Hackathon. Participants may request a specific team during registration. Individual participants will be assigned a team the morning of the event. \nRegistration for the Data Science Initiative Hackathon is currently closed. Please contact datascience@uci.edu for further assistance. \n 
URL:https://datascience.uci.edu/event/data-science-hackathon-2/
ATTACH;FMTTYPE=image/jpeg:https://datascience.uci.edu/wp-content/uploads/2014/12/DSC00932.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20141117T090000
DTEND;TZID=America/Los_Angeles:20141117T170000
DTSTAMP:20260517T120145
CREATED:20160427T143553Z
LAST-MODIFIED:20160427T143553Z
UID:1699-1416214800-1416243600@datascience.uci.edu
SUMMARY:An Introduction to Linux on the High-Performance Computing Cluster
DESCRIPTION:This course is for researchers who have never used Linux and/or a compute cluster and introduces concepts and best practices for both. \nThe application deadline has passed.  \nTo find out about future one-day courses\, please join our mailing list. \n\n \n\n\n \nThis course is for researchers who have never used Linux and/or a compute cluster and introduces concepts and best practices for both. \nThe application deadline has passed.  \nTo find out about future one-day courses\, please join our mailing list. \nThis course covers how to best exploit the bash shell for both interactive work and batch jobs\, moving & simple manipulation of data\, as well very short introductions to programming in bash\, Perl\, and R. This is not computer science; this is a driver’s license. \nDate: November 17\, 2014\nTime: Tentatively scheduled from 9 a.m. to 5 p.m. with lunch provided\nLocation: Donald Bren Hall\, Room 3011\nInstructor: Harry Mangalam\, OIT/HPC\, UC Irvine\nPre-requisites: for the course\, none. For the tutorial\, a laptop with WiFi\, with a terminal application (Macs have the Terminal app; Windows need putty)\, and both would benefit from ‘x2go’.
URL:https://datascience.uci.edu/event/an-introduction-to-linux-on-the-high-performance-computing-cluster/
ATTACH;FMTTYPE=image/png:https://datascience.uci.edu/wp-content/uploads/2014/09/lock_data_science1.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20141117T090000
DTEND;TZID=America/Los_Angeles:20141117T170000
DTSTAMP:20260517T120145
CREATED:20160427T143241Z
LAST-MODIFIED:20160427T143241Z
UID:1697-1416214800-1416243600@datascience.uci.edu
SUMMARY:Analyzing Data/BigData on Linux
DESCRIPTION:This course extends the topics introduced in the Introduction to Linux class\, including a lightning review of the introduction topics and then extending to large scale data processing. \nThe application deadline has passed.  \nTo find out about future one-day courses\, please join our mailing list. \n\n\n\n \nThis course extends the topics introduced in the Introduction to Linux class\, including a lightning review of the introduction topics and then extending to large scale data processing. \nThe application deadline has passed.  \nTo find out about future one-day courses\, please join our mailing list. \nIt covers using foreign data formats on Linux\, stream processing\, using efficient and appropriate file formats\, considerations for simple parallel processing\, introduction to different families of applications\, dealing with BigData sets. \nDate: November 18\, 2014\nTime: Tentatively scheduled from 9 a.m. to 5 p.m. with lunch provided\nLocation: Donald Bren Hall\, Room 3011\nInstructor: Harry Mangalam\, OIT/HPC\, UC Irvine\nPre-requisites: for the course\, the Introduction to Linux class or equivalent experience with Linux on a cluster. For the tutorial\, same as the Introduction to Linux class
URL:https://datascience.uci.edu/event/1697/
ATTACH;FMTTYPE=image/jpeg:https://datascience.uci.edu/wp-content/uploads/2014/09/Big_Data_DARPA.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20141117T090000
DTEND;TZID=America/Los_Angeles:20141117T170000
DTSTAMP:20260517T120145
CREATED:20160427T142847Z
LAST-MODIFIED:20160427T142847Z
UID:1696-1416214800-1416243600@datascience.uci.edu
SUMMARY:Working with Big Data in Linux
DESCRIPTION:This course extends the topics introduced in the Introduction to Linux class\, including a lightning review of the introduction topics and then extending to large scale data processing. \nThe application deadline has passed.  \nTo find out about future one-day courses\, please join our mailing list. \n\n \n\n\n \nThis course extends the topics introduced in the Introduction to Linux class\, including a lightning review of the introduction topics and then extending to large scale data processing. \nThe application deadline has passed.  \nTo find out about future one-day courses\, please join our mailing list. \nIt covers using foreign data formats on Linux\, stream processing\, using efficient and appropriate file formats\, considerations for simple parallel processing\, introduction to different families of applications\, dealing with BigData sets. \nDate:October 12 \, 2015\nTime: Tentatively scheduled from 9 a.m. to 5 p.m. with lunch provided\nLocation: Donald Bren Hall\, Room 3011\nInstructor: Harry Mangalam\, OIT/HPC\, UC Irvine\nPre-requisites: for the course\, the Introduction to Linux class or equivalent experience with Linux on a cluster. For the tutorial\, same as the Introduction to Linux class
URL:https://datascience.uci.edu/event/working-with-big-data-in-linux/
ATTACH;FMTTYPE=image/jpeg:https://datascience.uci.edu/wp-content/uploads/2014/09/Big_Data_DARPA.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20141114T090000
DTEND;TZID=America/Los_Angeles:20141114T170000
DTSTAMP:20260517T120145
CREATED:20160427T142527Z
LAST-MODIFIED:20160427T142527Z
UID:1695-1415955600-1415984400@datascience.uci.edu
SUMMARY:Introduction to R
DESCRIPTION:This course provides an introduction to the fundamentals of the R language. \nThe application deadline has passed.  \nTo find out about future one-day courses\, please join our mailing list. \n \nThis course provides an introduction to the fundamentals of the R language. \nThe application deadline has passed.  \nTo find out about future one-day courses\, please join our mailing list.\nIn this course\, you will learn how to program in R and how to effectively use R to do data analysis. The course covers introduction to data/object types in R\, reading data into R\, creating data graphics\, accessing and installing R packages\, writing R functions\, fitting statistical models including regression models and performing statical tests as t-test\, and ANOVA. Practical examples will be provided during the course. \nDate: Friday\, November 14\, 2014 \nTime: Tentatively scheduled from 9 a.m. to 5 p.m. with lunch provided \nLocation: Donald Bren Hall\, Room 4011 \nInstructor: Sepehr Akhavan\, Department of Statistics\, UC Irvine \nPre-requisites: 1) familiarity with basic statistical concepts\, and 2) basic programming knowledge. For the tutorial\, bring a laptop with R downloaded and installed and WiFi. \n 
URL:https://datascience.uci.edu/event/introduction-to-r-2/
ATTACH;FMTTYPE=image/jpeg:https://datascience.uci.edu/wp-content/uploads/2015/07/16079640189_9733eba311_z.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20141107T150000
DTEND;TZID=America/Los_Angeles:20141107T161500
DTSTAMP:20260517T120145
CREATED:20160427T141634Z
LAST-MODIFIED:20160427T141634Z
UID:1694-1415372400-1415376900@datascience.uci.edu
SUMMARY:Public Health\, Personal Data
DESCRIPTION:Speakers: Kevin Patrick\, MD\, MS\, Professor\, Family and Preventive Medicine\, UC San Diego \nMatthew Bietz\, PhD\, Assistant Project Scientist\, Informatics Department\, UC Irvine\nDate: Friday\, November 7\, 2014\nTime: 3:00 p.m. – 4:15 p.m.\nLocation: 6011 Donald Bren Hall \n\n \n \nPUBLIC HEALTH\, PERSONAL DATA \nSpeakers: Kevin Patrick\, MD\, MS\, Professor\, Family and Preventive Medicine\, UC San Diego\nMatthew Bietz\, PhD\, Assistant Project Scientist\, Informatics Department\, UC Irvine\nDate: Friday\, November 7\, 2014\nTime: 3:00 p.m. – 4:15 p.m.\nLocation: 6011 Donald Bren Hall \nIndividuals are tracking a variety of health-related data via a growing number of wearable devices and smartphone apps. More and more data relevant to health are also being captured passively as people communicate with one another on social networks\, shop\, work\, or do any number of activities that leave “digital footprints.” Self-tracking data can provide better measures of everyday behavior and lifestyle and can fill in gaps in more traditional clinical or public health data collection\, giving us a more complete picture of health and human activity. In this talk\, we will discuss how new forms of personal data are transforming the ways we understand human health. Making use of personal data for the public good requires addressing a number of technical\, social\, and scientific challenges\, including developing new methods of analysis\, new mechanisms for data sharing\, and appropriate privacy and consent frameworks. We will discuss how we are addressing these challenges in our new Health Data Exploration project\, funded by the Robert Wood Johnson Foundation. \nBios: \nDr. Patrick is Professor of Family and Preventive Medicine at the University of California\, San Diego and Director of the Center for Wireless and Population Health Systems at the Qualcomm Institute/Calit2. He is Director of the Health Data Exploration project of the Robert Wood Johnson Foundation (RWJF). He served as Editor-in-Chief of the American Journal of Preventive Medicine from 1994-2013\, and has served on the Secretary’s Council for Health Promotion and Disease Prevention of the U.S. Department of Health and Human Services (HHS) and the Armed Forces Epidemiology Board. His research\, supported by the NIH\, NSF\, CDC\, and RWJF\, explores how to use mobile\, home and social technologies to measure and improve the health of individuals and populations. \nDr. Bietz (Assistant Project Scientist\, University of California\, Irvine) received his Ph.D. in Information from the University of Michigan in 2008. He is Lead Co-Investigator on the Robert Wood Johnson-funded Health Data Exploration project. He has studied collaboration\, data sharing\, and the development of cyberinfrastructure in various scientific and engineering fields including HIV/AIDS research\, genomics\, oceanography\, astronomy\, software engineering\, and planetary science. A primary research interest is understanding the negotiation and alignment work necessary to support large-scale data sharing.
URL:https://datascience.uci.edu/event/public-health-personal-data/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20141105T160000
DTEND;TZID=America/Los_Angeles:20141105T170000
DTSTAMP:20260517T120145
CREATED:20160427T141432Z
LAST-MODIFIED:20160427T141432Z
UID:1693-1415203200-1415206800@datascience.uci.edu
SUMMARY:Robust Principal Component Analysis - Professor Emmanuel Candès
DESCRIPTION:
URL:https://datascience.uci.edu/event/robust-principal-component-analysis-professor-emmanuel-candes/
ATTACH;FMTTYPE=image/jpeg:https://datascience.uci.edu/wp-content/uploads/2014/10/CandesFlyer.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20141027T180000
DTEND;TZID=America/Los_Angeles:20141027T200000
DTSTAMP:20260517T120145
CREATED:20160427T141227Z
LAST-MODIFIED:20160427T141227Z
UID:1692-1414432800-1414440000@datascience.uci.edu
SUMMARY:Ebola: What you Should Know
DESCRIPTION:
URL:https://datascience.uci.edu/event/ebola-what-you-should-know/
ATTACH;FMTTYPE=image/jpeg:https://datascience.uci.edu/wp-content/uploads/2014/12/Andrew-Noymer_4449_cropped.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20141024T133000
DTEND;TZID=America/Los_Angeles:20141024T170000
DTSTAMP:20260517T120145
CREATED:20160427T141013Z
LAST-MODIFIED:20160427T141013Z
UID:1691-1414157400-1414170000@datascience.uci.edu
SUMMARY:Data Science Kickoff Meeting
DESCRIPTION:Please join us on the afternoon of Friday\, October 24th to learn more about the UCI Data Science Initiative. \n \nPlease join us on the afternoon of Friday\, October 24th to learn more about the UCI Data Science Initiative. \n  \n  \nDirections and parking information: visit the Calit2 website. \nAgenda and list of speakers: view the event flyer. \nThis event is free and open to the public. An RSVP is required.
URL:https://datascience.uci.edu/event/data-science-kickoff-meeting/
ATTACH;FMTTYPE=image/jpeg:https://datascience.uci.edu/wp-content/uploads/2014/09/Padhraic-Smyth-11.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20140120T090000
DTEND;TZID=America/Los_Angeles:20140120T170000
DTSTAMP:20260517T120145
CREATED:20160502T104005Z
LAST-MODIFIED:20160502T104005Z
UID:1703-1390208400-1390237200@datascience.uci.edu
SUMMARY:Introduction to Linux Short Course
DESCRIPTION:This course is for researchers who have never used Linux and/or a compute cluster and introduces concepts and best practices for both. \n\n\n\n \nDescription: \nThis course is for researchers who have never used Linux and/or a compute cluster and introduces concepts and best practices for both. \nThis course covers how to best exploit the bash shell for both interactive work and batch jobs\, moving & simple manipulation of data\, as well very short introductions to programming in bash\, Perl\, and R. This is not computer science; this is a driver’s license. \nDate: January 20th\, 2014\nTime: 9 a.m. to 5 p.m. with lunch provided\nLocation: Donald Bren Hall\, Room 3011\nInstructor: Harry Mangalam\, OIT/HPC\, UC Irvine\nPre-requisites: For the course\, none. For the tutorial\, a laptop with WiFi\, with a terminal application (Macs have the Terminal app; Windows need putty)\, and both would benefit from ‘x2go’.
URL:https://datascience.uci.edu/event/introduction-to-linux-short-course/
ATTACH;FMTTYPE=image/png:https://datascience.uci.edu/wp-content/uploads/2014/09/lock_data_science1.png
END:VEVENT
END:VCALENDAR