Speed Dating/Matchmaking Event September 19, 2014 John%Beieler - PowerPoint PPT Presentation

BDSS IGERT Speed Dating/Matchmaking Event September 19, 2014

John%Beieler PhD$Student,$Poli/cal$Science jub270@psu.edu johnbeieler.org

Event&Data Who$did$what$to$whom • Python,)R • Natural)language)processing • Forecas7ng • Poli7cal)violence

Wanghuan'Chu' • 4 th +year'Ph.D.'student'in'Sta6s6cs' • Key'strength:'Sta6s6cal'modeling' – Nonparametric'regressions,'mixed+effects/mul6level' models,'discrete'choice'models,'sta6s6cal'learning' algorithms,'causal'inference'techniques,'etc.' • Research'experience' – Thesis&research :'Feature'screening'methods'for'ultrahigh' dimensional'longitudinal'data.' • e.g.'Gene6c'data'with'870,000'SNPs'from'540'subjects'(p'>>'n)' – 1 st &IGERT&rota1on :'Causal'media6on'analysis'for'clustering' data'using'mixed+effects'models,'propensity'score' modeling'and'inverse'probability'weigh6ng.'

Wanghuan'Chu' • Poten6al'components'for'the'ideal'project' – Parallel'compu6ng'to'Big'Data'(e.g.'MapReduce).' – Sta6s6cal'methodologies'at'data'analy6cs'layer.' – Interes6ng'social'science'ques6ons'to'be'explored.' • SoVware:'R'and'SAS'(MACRO'and'SQL)' • Interested'in'learning' – New'programming'language'(e.g.'Python).' – New'methodology'and'domain'knowledge.'

An Introduction: Cindy Cook cmc496@psu.edu B.S. in Mathematics ! • Graph Theory • Parallel Computing with MPI: Recommender Systems • M.S. in Applied Statistics ! • R, SAS, Stata, C++ • Machine Learning • Survival Analysis Cox Models • Ph.D. in Statistics ! • No particular advisor or research

Research Interests: ! Big Data ◦ With statistical applications in the Social Sciences ◦ Python, parallel computing in R, and broadening my overall computing skills ! Data that has spatial/temporal trends ! Networks on a large scale ! Any combination of these

Timmy Huynh ■ Sociology & Demography Advisor: John Iceland tnh133@psu.edu  Education  Research interests  B.A., Geography / Economics, The  Urban sociology University of Texas at Austin, 2010  Spatial demography  M.A., Social Sciences, The University of  Economic geography Chicago, 2011  Networks  Research experience (selected)  (Geo)Visualization  REU Summer Institute in Minority Group  Skills Demography – Austin, TX, 2009  Summer Institute in LGBT Population  Statistics (Stata, SPSS, R) Health – Boston, MA, 2010  GIS (ArcGIS, GeoDa, ERDAS)  Asian Americans Advancing Justice –  Programming (Python, JavaScript) Chicago, IL, 2011-2012  Oak Ridge National Laboratory – Oak Ridge, TN, 2012-2013

Christopher Inkpen Sociology and Demography Broad Interests - global migration patterns - assimilation - population processes Tools - Statistical Models : linear regression, GLM, HLM, fixed and random effects, spatial econometrics - Computing : Stata, R, Python, SQL - Mapping : ArcGIS, CartoDB Recent Projects - determinants of student migration - visualizing global migration patterns - assessing impact of recession on internal migration

Areas to explore Population estimation and data fusion Mapping of social networks

Department of Human Development and Family Studies Rachel Koffer rek183@psu.edu 3rd year Ph.D student in the Department of Human Development and Family Studies Concentrations: Individual Development, Methodology Advisors: Nilam Ram, David Almeida B.A. Psychology, Economics; Minor: Environmental Studies Skills I Bring to the Skills I Hope to Rotation: Develop/Improve SAS, R, LISREL, SPSS, STATA During the Rotation: Statistical skills: General linear, Python; Data visualization, multilevel, structural Machine Learning equation modeling, PCA and Factor Analysis

Department of Human Development and Family Studies Rachel Koffer rek183@psu.edu Substantive Interests: Methodological Interests: Analysis of: Association between daily Intensive longitudinal data (many experiences and well-being. measurements across short time Effects of daily stressors on span); daily and long-term affective Multiple time scales (intensive (mood) and physical longitudinal data w/in longer-term data); well-being. Potential Interests for Research Rotation: Machine learning techniques for developmental time series data Application of interdisciplinary methods to stress concepts

September 19, 2014 Fridolin Linder Department: Political Science (2 nd Year PhD) Fields: Methodology, Comparative Politics, Statistics (Grad. Minor) Interests: Predictive Modeling/Machine Learning, Text Analysis (Classification,Scaling), Political Representation, Research Design/Causal Inference/Epistemology Skills: Statistics, R (substantial), Python Current Projects: Datamining as Exploratory Data Analysis (w/ Zach Jones), Rationalization of candidate choice through missreporting of ideological self-placement (experiment) Fridolin Linder BDSS IGERT Matchmaking Event 1 / 1

Jonathan K. Nelson 
 Department of Geography 
 jkn128@psu.edu " Abstract —I am a Ph.D student in the department of geography. Prior to coming to Penn State I was a cartographer for National Geographic. I study spatial data representation and explore patterns and relationships in geographic phenomena, using spatial statistics and visual analytics approaches. I am particularly interested in interactive multi-scale visual and data abstraction techniques for making sense of BIG DATA. " My current research rotation is in the GeoVISTA Center and involves leveraging geo- social media data to support crisis management. Other projects I am working on include: a visual analysis of 1200 student maps from a massive open online course titled “Maps and the Geospatial Revolution;” an exploratory analysis on multiscalar effects of the modifiable areal unit problem on cancer diagnosis rates and median income; and a human-pet-computer interaction study that aims to build healthy relationships between pet owners and their dogs using personal visualization and quantification. " Tools I commonly use for carrying out and conveying my research include: Adobe Creative Suite, Avenza MAPublisher, Final Cut Pro; ESRI ArcGIS, GeoDaA, R; CSS, HTML, JavaScript D3. " ! Keywords — spatial data, visualization, cartography, map, scale, aggregation, information design "

Deeper Learning in Large-Scale Text Alexander G. Ororbia II, IST PhD Student INTELLIGENT SYSTEMS LABORATORY APPLIED COGNITIVE SCIENCES LABORATORY

What do I do? Build:  Deep models for learning from Scholarly Big Data   Multilayer neural networks, learning kernels  Boltzmann Machines  Convolutional Networks — text recognition in-the- wild (“Text in the Wild”) Active Learning Algorithms   Bayesian Network Lattice for error-correcting Amazon Mechanical Turker annotations (Ororbia et. al, 2014, Under Review) Investigate:  Can deep architectures discover/model inherent hierarchical structure in text?  How can intelligent systems work in tandem with humans to solve complex problems?  Can intelligent tools be built that harvest and organize vast amounts of scholarly data?   What insights can these same algorithms extract from the data?

BDSS-IGERT Joshua Snoke snoke@psu.edu • About Me: • 2nd Year PhD Student, Department of Statistics • 1st Year BDSS-IGERT Trainee • B.S. in Mathematics and Economics • Current Research: • Data Privacy, Disclosure Limitation Methods • Synthetic Data for Public Use (in Sociology Studies), Parametric and Non-Parametric Methods

BDSS-IGERT Joshua Snoke snoke@psu.edu • Currently Seeking a Research Project Outside of the Statistics Dept. • Interests and Applications: • Policy, Politics (National and Global) • Social Networks, Relationships • Methodology, Causal Inference, Bayesian Methods • Computational Proficiency: • Significant Experience in R • Some Experience in Python, Java, and SQL

Sam Stehle Geography Background • B.S. University of Utah; geography, minor in Computer Science • M.S. Penn State; geography Methods experience Previous activities • Java • Data management for mobile GIS • Python • Matching space-time patterns for • C++ political/social comparison • R • Visual analytic software • Spatial analysis, GIS design/implementation • SQL • Text classification of Twitter data • Time series analysis • Event data collection/classification • Raster/image analysis • Time series intervention modeling • Machine learning with Weka

Sam Stehle Interests for Future Work Dissertation considerations Topics • Geography/politics of international sport • Political geography • British Commonwealth Games • Understanding events • Multi-scale spatio-temporal modeling • Spatio-temporal patterns • RSS feed data + geo/social/political context • Sport • Data-driven vs. dictionary event classification • Media representations • Spatio-temporal diffusion patterns • Multi-scale issues

A System of Systems Clio Andris (Assistant Professor of GIScience) Dept. of Geography, clio@psu.edu. Courses: Fall GEOG560: Interpersonal Relationships in Geographic Space , Spring GEOG363: GIS

Example 2 Example 3 Example 4 Network thanks to Paul Hooper, Emory U.

MID/DLE: An End to Data Collection Participant Measure Measure Measure Participant Analyze Measure Collect Researcher Measure Computer Measure Participant Measure Measure Measure Maintain Tim Brick, HDFS