{"id":4773,"date":"2016-03-15T18:09:07","date_gmt":"2016-03-15T18:09:07","guid":{"rendered":"http:\/\/www.samsi.info\/?page_id=4773"},"modified":"2016-03-30T20:23:22","modified_gmt":"2016-03-30T20:23:22","slug":"opening-workshop-massive-datasets-program-september-9-12-2012","status":"publish","type":"page","link":"https:\/\/www.samsi.info\/programs-and-activities\/research-workshops\/opening-workshop-massive-datasets-program-september-9-12-2012\/","title":{"rendered":"Opening Workshop, Massive Datasets Program – September 9-12, 2012"},"content":{"rendered":"

Partial list of research topics<\/h2>\n

Data visualization and analytics<\/h3>\n

High-speed visualization of high-dimensional datasets; data representation, extraction, integration and transformation; real-time visual interaction; spatio-temporal data mining<\/p>\n

Online streaming and sketching<\/h3>\n

Algorithm paradigms for massive datasets (streaming, online, randomized); scalability; filtering; anomaly detection; data structures for fast computation of statistics; database enabled machine learning tools; computing environments and programming models (GPU’s, cloud computing, custom chips)<\/p>\n

Large-scale optimization<\/h3>\n

Convex optimization (sparse modeling and compressed sensing, matrix completion); online optimization (streaming data, on-line learning, control theory); distributed optimization (parallel and GPU computation, data fusion); machine learning; high-dimensional models<\/p>\n

Inference<\/h3>\n

Dimension reduction for high-dimensional data (feature selection, sub-sampling and screening, sparse PCA); predictive inference and multiple testing (false discovery rates, uncertainty in prediction); high-dimensional MCMC methods for posterior inference (particle filters, hybrids with optimization methods)<\/p>\n

Imaging<\/h3>\n

Rapid registration and segmentation (GPU’s, distributed computing); multiple testing and inference for large-scale imaging data (sky surveys, satellite images, false discovery rate with dependence); dynamic imaging (streaming data, spatio-temporal models)<\/p>\n

Systems and architectures<\/h3>\n

Reliability; resilience; probabilistic computing, multiple precision; real-time methods; variable data flows; hardware platforms<\/p>\n

High-energy physics<\/h3>\n

Reconstruction and analysis of particle collisions from the LHC; pattern recognition and parameter extraction; simulations to estimate error rates; parameter estimation for large numbers of parameters; maximum likelihood estimators<\/p>\n

Astronomy<\/h3>\n

Statistics on remote resources; computations on special purpose architectures and GPUs; communication avoiding methods; randomized and online algorithms; detection and classification of transient events and outliers; Bayesian inference and machine learning; high dimensional models with empirical priors; non-parametric models; visualization of large high-dimensional datasets<\/p>\n

Environment and climate<\/h3>\n

Production, validation, processing, distribution and integration of data; data fusion and remote sensing; algorithms for large distributed datasets; spatial or spatio-temporal statistics<\/p>\n

\n

Schedule and Supporting Media<\/h2>\n

Sunday, September 9, 2012
\n<\/strong>Radisson RTP<\/a><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Time<\/th>\nDescription<\/th>\nSpeaker<\/th>\nSlides<\/th>\nVideos<\/th>\n<\/tr>\n<\/thead>\n
8:30-9:00<\/td>\nRegistration and Continental Breakfast<\/td>\n<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
8:50-9:00<\/td>\nWelcome and Introduction<\/td>\nIlse Ipsen<\/strong>, N.C. State University\/SAMSI<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
<\/td>\nTutorials<\/td>\n<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
9:00-10:00<\/td>\nStatistical Methods in Astronomy<\/em><\/td>\nTamas Budavari<\/strong>, Johns Hopkins University<\/td>\n\u00a0<\/i><\/a><\/td>\n\u00a0<\/i><\/a><\/td>\n<\/tr>\n
10:00-10:30<\/td>\nBreak<\/td>\n<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
10:30-11:30<\/td>\nMining Massive Datasets: A (randomized) Linear Algebraic Perspective<\/em><\/td>\nPetros Drineas<\/strong>, Rensselaer Polytechnic Institute<\/td>\n\u00a0<\/i><\/a><\/td>\n\u00a0<\/i><\/a><\/td>\n<\/tr>\n
11:30-1:00<\/td>\nLunch<\/td>\n<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
1:00-2:00<\/td>\nVisual Analytics for Knowledge Discovery in High Dimensional Data<\/em><\/td>\nHaesun Park<\/strong>, Georgia Institue of Technology<\/td>\n<\/td>\n\u00a0<\/i><\/a><\/td>\n<\/tr>\n
2:00-2:30<\/td>\nBreak<\/td>\n<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
2:30-3:30<\/td>\nOptimization Techniques for Statistical Analysis on Large Datasets<\/em><\/td>\nStephen Wright<\/strong>, University of Wisconsin<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
3:30-4:00<\/td>\nBreak<\/td>\n<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
4:00-5:00<\/td>\nResampling Methods for Massive Data<\/em><\/td>\nMichael Jordan<\/strong>, Univ. of California-Berkeley<\/td>\n\u00a0<\/i><\/a><\/td>\n\u00a0<\/i><\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Monday, September 10, 2012
\n<\/strong>Radisson RTP<\/a><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Time<\/th>\nDescription<\/th>\nSpeaker<\/th>\nSlides<\/th>\nVideos<\/th>\n<\/tr>\n<\/thead>\n
8:30-8:55<\/td>\nRegistration and Continental Breakfast<\/td>\n<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
8:55-9:00<\/td>\nWelcome<\/td>\n<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
<\/td>\nSession: Inference<\/em><\/td>\n<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
9:00-9:45<\/td>\nStability<\/em><\/td>\nBin Yu<\/strong>, University of California, Berkeley<\/td>\n\u00a0<\/i><\/a><\/td>\n<\/td>\n<\/tr>\n
9:45-10:30<\/td>\nOn Personalized Information Filtering<\/em><\/td>\nXiaotong Shen<\/strong>, University of Minnesota<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
10:30-11:00<\/td>\nBreak<\/td>\n<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
11:00-11:45<\/td>\nResting State Brain Functional Connectivity Data: progress, future challenges and data<\/em><\/td>\nBrian Caffo<\/strong>, Johns Hopkins University<\/td>\n\u00a0<\/i><\/a><\/td>\n<\/td>\n<\/tr>\n
11:45-12:15<\/td>\n<\/td>\nPanel Chair:
\nBill Eddy<\/strong>, Carnegie Mellon University
\nPanelists:
\nAlex Gray<\/strong>, Georgia Tech,
\nKaren Kafadar<\/strong>, Indiana University,
\nBo Li<\/strong>, Purdue University<\/td>\n		\u00a0<\/i><\/a>
\n\u00a0<\/i><\/a>
\n\u00a0<\/i><\/a><\/td>\n		<\/td>\n<\/tr>\n
12:15-1:30<\/td>\nLunch<\/td>\n<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
<\/td>\nSession: Imaging<\/em><\/td>\n<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
1:30-2:15<\/td>\nNumerical Methods for Large Scale Inverse Problems in Image Reconstruction<\/em><\/td>\nJim Nagy<\/strong>, Emory University<\/td>\n\u00a0<\/i><\/a><\/td>\n<\/td>\n<\/tr>\n
2:15-3:00<\/td>\nIterative Screening and Estimation<\/em><\/td>\nJianqing Fan<\/strong>, Princeton University<\/td>\n\u00a0<\/i><\/a><\/td>\n<\/td>\n<\/tr>\n
3:00-3:30<\/td>\nBreak<\/td>\n<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
3:30-4:15<\/td>\nSupernova Discovery in the Era of Data-Intensive Science<\/em><\/td>\nRollin Thomas<\/strong>, Lawrence Berkeley National Lab<\/td>\n\u00a0<\/i><\/a><\/td>\n<\/td>\n<\/tr>\n
4:15-4:45<\/td>\n<\/td>\nPanel Co-Chairs:
\nDaniela Ushizima<\/strong>,
\nLawrence Berkeley National Lab and\u00a0Jiayang Sun<\/strong>, Case Western Reserve
\nPanelists:
\nPeihua Qiu<\/strong>, University of Minnesota,
\nErkki Somersalo<\/strong>, Case Western<\/td>\n		<\/td>\n<\/td>\n<\/tr>\n
4:45-5:15<\/td>\nPoster blitz (2 minutes per poster)<\/td>\n<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
5:15-5:30<\/td>\nBreak<\/td>\n<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
5:30-7:30<\/td>\nPoster Session and Reception<\/td>\n<\/td>\n<\/td>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Tuesday, September 11, 2012
\n<\/strong>Radisson RTP<\/a><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Time<\/th>\nDescription<\/th>\nSpeaker<\/th>\nSlides<\/th>\nVideos<\/th>\n<\/tr>\n<\/thead>\n
8:30-9:00<\/td>\nRegistration and Continental Breakfast<\/td>\n<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
<\/td>\nSession: Environment & Climate<\/em><\/td>\n<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
9:00-9:45<\/td>\nA Bird\u2019s Eye View of the Carbon Cycle: Spatiotemporal tools for constraining the CO2 budget from atmospheric observations<\/em><\/td>\nAnna Michalak<\/strong>, Stanford University<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
9:45-10:30<\/td>\nArchitecting Highly Scalable Scientific Data Management and Discovery Systems<\/em><\/td>\nDan Crichton<\/strong>, Jet Propulsion Lab<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
10:30-11:00<\/td>\nBreak<\/td>\n<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
11:00-11:45<\/td>\nUncertainty Quantification for Regional-Climate-Model Output<\/em><\/td>\nNoel Cressie<\/strong>, University of Wollongong and The Ohio State University<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
11:45-12:15<\/td>\n<\/td>\nPanel:
\nChair:
\nJessica Matthews<\/strong>, CICS-NC
\nPanelists:
\nAmy Braverman<\/strong>, Jet Propulsion Lab,
\nSteve Sain<\/strong>, NCAR,
\nRichard Smith<\/strong>, SAMSI\/UNC-CH<\/td>\n		<\/td>\n<\/td>\n<\/tr>\n
12:15-1:30<\/td>\nLunch<\/td>\n<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
<\/td>\nSession: High Energy Physics<\/em><\/td>\n<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
1:30-2:15<\/td>\nRecreating the Big Bang in the Laboratory: The Scientific, Computational and Data Challenges of High Energy Nuclear Physics<\/em><\/td>\nSteffen Bass<\/strong>, Duke University<\/td>\n\u00a0<\/i><\/a><\/td>\n<\/td>\n<\/tr>\n
2:15-3:00<\/td>\nStatistical Aspects of the Discovery of the Higgs Boson at the Large Hadron Collider<\/em><\/td>\nKyle Cranmer<\/strong>, New York University<\/td>\n\u00a0<\/i><\/a><\/td>\n<\/td>\n<\/tr>\n
3:00-3:30<\/td>\nBreak<\/td>\n<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
3:30-4:15<\/td>\nSearches and Measurements in High Energy Physics<\/em><\/td>\nLuc Demortier<\/strong>, Rockefeller University<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
4:15-4:45<\/td>\n<\/td>\nPanel
\nChair:
\nRobert Wolpert<\/strong>, Duke University
\nPanelists:
\nMandeep Gill<\/strong>, SLAC;
\nCosma Shalizi<\/strong>, Carnegie Mellon University;
\nDaniel Whiteson<\/strong>, University of California, Irvine<\/td>\n		<\/td>\n<\/td>\n<\/tr>\n
4:45-6:00<\/td>\nOpen Mic and Refreshments<\/td>\n<\/td>\n<\/td>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Wednesday, September 12, 2012
\n<\/strong>Radisson RTP<\/a><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Time<\/th>\nDescription<\/th>\nSpeaker<\/th>\nSlides<\/th>\nVideos<\/th>\n<\/tr>\n<\/thead>\n
8:30-9:00<\/td>\nRegistration and Continental Breakfast<\/td>\n<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
<\/td>\nSession: Streaming, Sketching & Datamining<\/em><\/td>\n<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
9:00-9:45<\/td>\nImplementing Randomized Matrix Algorithms in Parallel and Distributed Environments<\/em><\/td>\nMichael Mahoney<\/strong>, Stanford University<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
9:45-10:30<\/td>\nConvex Relaxations for Recovery of Models with Simultaneous Structures<\/em><\/td>\nMaryam Fazel<\/strong>, University of Washington<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
10:30-11:00<\/td>\nBreak<\/td>\n<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
11:00-11:45<\/td>\nSparse Inverse Covariance Matrix Estimation Using Quadratic Approximation<\/em><\/td>\nInderjit Dhillon<\/strong>, University of Texas, Austin<\/td>\n\u00a0<\/i><\/a><\/td>\n<\/td>\n<\/tr>\n
11:45-12:15<\/td>\n<\/td>\nPanel
\nChair:
\nPiotr Indyk<\/strong>, MIT
\nPanelists:
\nGraham Cormode<\/strong>, AT&T Labs-Research,
\nAshish Goel<\/strong>, Stanford University,
\nMichael Mahoney<\/strong>, Stanford University<\/td>\n		\u00a0<\/i><\/a>
\n\u00a0<\/i><\/a>
\n\u00a0<\/i><\/a>
\n\u00a0<\/i><\/a><\/td>\n		<\/td>\n<\/tr>\n
12:15-1:30<\/td>\nLunch<\/td>\n<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
<\/td>\nWorking Groups<\/em><\/td>\n<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
1:30-3:00<\/td>\nWorking Group Formation and Initial Meeting<\/td>\n<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
3:00<\/td>\nAdjourn<\/td>\n<\/td>\n<\/td>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n","protected":false},"excerpt":{"rendered":"

Partial list of research topics Data visualization and analytics High-speed visualization of high-dimensional datasets; data representation, extraction, integration and transformation; real-time visual interaction; spatio-temporal data mining Online streaming and sketching Algorithm paradigms for massive datasets (streaming, online, randomized); scalability; filtering; anomaly detection; data structures for fast computation of statistics; database enabled machine learning tools; computing […]<\/p>\n","protected":false},"author":3,"featured_media":0,"parent":998,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":[],"_links":{"self":[{"href":"https:\/\/www.samsi.info\/wp-json\/wp\/v2\/pages\/4773"}],"collection":[{"href":"https:\/\/www.samsi.info\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.samsi.info\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.samsi.info\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/www.samsi.info\/wp-json\/wp\/v2\/comments?post=4773"}],"version-history":[{"count":9,"href":"https:\/\/www.samsi.info\/wp-json\/wp\/v2\/pages\/4773\/revisions"}],"predecessor-version":[{"id":5943,"href":"https:\/\/www.samsi.info\/wp-json\/wp\/v2\/pages\/4773\/revisions\/5943"}],"up":[{"embeddable":true,"href":"https:\/\/www.samsi.info\/wp-json\/wp\/v2\/pages\/998"}],"wp:attachment":[{"href":"https:\/\/www.samsi.info\/wp-json\/wp\/v2\/media?parent=4773"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}