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Key:
Applications
Computational Statistics
Computing Science
Data Science
Data Visualization
Machine Learning
Thursday, May 17
CS04 -
Best Practices in Data Science Education
Invited
Thu, May 17, 10:30 AM - 12:00 PM
Grand Ballroom G
Organizer(s): Ben Baumer, Smith College
Chair(s): Ben Baumer, Smith College
10:30 AM
Start with Data Science as an Introduction to Statistical Thinking
Presentation
Mine Cetinkaya-Rundel, Duke University & RStudio
11:00 AM
Data Science for Everybody: Building and Characterizing Student-Driven Pathways in Introductory Statistics Courses
Rebecca Nugent, Carnegie Mellon Statistics & Data Science
11:30 AM
Data-Driven Curriculum Development
David Robinson, DataCamp
CS11 -
Big Data Analytics Using R and Spark
Invited
Thu, May 17, 1:30 PM - 3:00 PM
Grand Ballroom G
Organizer(s): Brad Price, West Virginia University
Chair(s): Brad Price, West Virginia University
1:30 PM
Data Science Workflows
Jim Harner, West Virginia University
2:00 PM
Data Science at Scale With R and Sparklyr: Architecture, Ecosystem, and Current Developments
Kevin Kuo, Rstudio
2:30 PM
Interacting with Distributed Data from R using SparkR
Presentation
Hossein Falaki, Databricks
CS17 -
Data Science at the National Institute of Statistical Sciences
Invited
Thu, May 17, 3:30 PM - 5:00 PM
Grand Ballroom G
Organizer(s): Jim Rosenberger, NISS and Pennsylvania State University
Chair(s): Jim Rosenberger, NISS and Pennsylvania State University
3:30 PM
Using Administrative Data to Produce Official Statistics: An Application to End-Of-Season Acreage Estimation
Presentation
Andreea L Erciulescu, National Institute of Statistical Sciences and USDA National Agricultural Statistics Service
4:00 PM
Future of Integer Calibration Weighting Methods
Presentation
Luca Sartore, National Institute of Statistical Sciences
4:30 PM
The NCES/NISS Partnership: Data Collection Efforts/Structures/New Initiatives
Nell Sedransk, National Institute of Statistical Sciences
CS23 -
Data Science Platforms I
Invited
Thu, May 17, 5:15 PM - 6:15 PM
Grand Ballroom G
Organizer(s): Jim Harner, West Virginia University
Chair(s): Jim Harner, West Virginia University
5:15 PM
Automating Data Science Processes with H2O Driverless AI
Presentation
Patrick Hall, H2O.ai
5:45 PM
Building Data Science Platforms Using Docker
Jim Harner, West Virginia University
Friday, May 18
CS30 -
Data Science Programs
Invited
Fri, May 18, 10:30 AM - 12:00 PM
Grand Ballroom G
Organizer(s): Tim Hesterberg, Google
Chair(s): Tim Hesterberg, Google
10:30 AM
NYU Master of Science in Data Science
Presentation
Arthur Spirling, New York University
10:55 AM
Columbia University Master of Science in Data Science
Presentation
Tian Zheng, Columbia University
11:20 AM
WVU Master of Science in Business Data Analytics: Challenges and Experiences with Online Data Science Programs
Presentation
Brad Price, West Virginia University
11:45 AM
Floor Discussion
CS37 -
Statistical Analytics for Data Science
Invited
Fri, May 18, 1:30 PM - 3:00 PM
Grand Ballroom G
Organizer(s): Lynne Billard, University of Georgia
Chair(s): Seyed Yaser Samadi, Southern Illinois University Carbondale
1:30 PM
Time Series Analysis for Symbolic Interval-valued Data
Seyed Yaser Samadi, Southern Illinois University Carbondale
2:00 PM
Privacy Analytics via Aggregate Data: Trade-off between Statistical Efficiency and Privacy
Anand N. Vidyashankar, George Mason University
2:30 PM
Clustering Histogram-valued Data
Lynne Billard, University of Georgia
CS40 -
Data Science Foundations
Contributed
Fri, May 18, 1:30 PM - 3:00 PM
Lake Fairfax B
Chair(s): Snehalata Huzurbazar, West Virginia University
1:30 PM
A Grammar for Reproducible and Painless Extract-Transform-Load Operations on Medium Data
Ben Baumer, Smith College
1:45 PM
Perspectives on Deep Learning and Deep Reasoning
Presentation
Rich Haney, Big Data2 Consulting
2:00 PM
Defining the AIM: An Abstraction for Improving Machine Learning Prediction
VICTORIA STODDEN, University of Illinois Urbana-Champaign
2:15 PM
Sensemaking and Five Problems with Big Data Science
Presentation
Michael Latta, Coastal Carolina University - YTMBA Research & Consulting
2:30 PM
Painless Computing Models for Ambitious Data Science
Presentation
Hatef Monajemi, Stanford University
2:45 PM
A Paradigm for Research in Data Science
Presentation
Vardan Papyan, Stanford
CS44 -
Data Science Platforms II
Invited
Fri, May 18, 3:30 PM - 5:00 PM
Grand Ballroom G
Organizer(s): Jim Harner, West Virginia University
Chair(s): Jim Harner, West Virginia University
3:30 PM
The Unified Analytics Platform: Unifying Big Data Workloads in Apache Spark
Presentation
Hossein Falaki, Databricks
4:00 PM
Using Microsoft ML Server and Spark for Distributed Computation of Massive Computational Experiments in Data Science and Statistical Inference
Ali Zaidi, Microsoft AI and Research
4:30 PM
The SAS® Platform: Where Point and Click Users and Coders of All Languages Collaborate Seamlessly
Carlos Pinheiro, SAS & Data Science Tech Institute, France
CS50 -
Data Science Platforms III
Invited
Fri, May 18, 5:15 PM - 6:15 PM
Grand Ballroom G
Organizer(s): Jim Harner, West Virginia University
Chair(s): Soren Harner, MuleSoft
5:15 PM
Intelligent Application Networks with MuleSoft and TensorFlow
Presentation
Soren Harner, MuleSoft
5:45 PM
An Introduction to the Watson Data Platform
Bernie Beekman, IBM
Saturday, May 19
CS56 -
Data Science and Machine Learning in Naval Applications
Invited
Sat, May 19, 8:30 AM - 10:00 AM
Grand Ballroom G
Organizer(s): Jeffrey L. Solka, Naval Surface Warfare Center
Chair(s): Avory Bryant, Naval Surface Warfare Center
8:30 AM
Using Found Data – A Cautionary Tale
Presentation
David A. Johannsen, Naval Surface Warfare Center - Dahlgren
9:00 AM
NLP-assisted Scientometric Horizon Scanning
Stuart Bingham, NSWCDD AM&DA, Code A43
9:30 AM
Human Motion Analysis Using Deep Learning for Potential Threats
Alex Feild, Naval Surface Warfare Center
CS60 -
Time-based Models
Contributed
Sat, May 19, 8:30 AM - 10:00 AM
Lake Fairfax B
Chair(s): Suchismita Goswami, Computational Data Science, George Mason University
8:30 AM
Bankruptcy Prediction Using Selective Under-Sampling and Multiple-Year Data: A Study on North American Companies
Son Nguyen, Bryant University
8:45 AM
Looking Into Recurrent Event Data
Bommae Kim, University of Virginia Health System
9:00 AM
Artificial Neural Networks and Time Series Decomposition for the Flood Prediction in Mohawk Watershed, New York
Katerina Tsakiri, Rider University
9:15 AM
Causal Inference from Observational Time Series Data
Iris Tu, LinkedIn
9:30 AM
Detection of Excessive Activities in Time Series of Graphs Using Scan Statistics
Suchismita Goswami, Computational Data Science, George Mason University
9:45 AM
Floor Discussion
CS63 -
Data Science in Practice
Contributed
Sat, May 19, 10:30 AM - 12:00 PM
Grand Ballroom G
Chair(s): Soren Harner, MuleSoft
10:30 AM
From Statistics to Data Science Startup: Transformation Within a Large Research Organization
Presentation
Gayle S Bieler, RTI International
10:45 AM
Applied Techniques for Machine Learning with Limited Data
Andrew Hoblitzell, IUPUI; Andrew Hoblitzell, Purdue University
11:00 AM
Data Moves in Data Science Education
Presentation
Tim Erickson, Epistemological Engineering
11:15 AM
Spatial Analysis of Crowdsourced Mobile Data
Presentation
Arnab Chakraborty, North Carolina State Univeristy
11:30 AM
The SOBER Algorithm: How to Squeeze Out Huge but Sparse Data for Making Individual Predictions
Barbara Hildegard Wolf, GfK SE
11:45 AM
Floor Discussion
CS68 -
Data Science in Health
Contributed
Sat, May 19, 1:15 PM - 2:45 PM
Grand Ballroom G
Chair(s): Kelly S Marczynski, SUNY Buffalo State
1:15 PM
Classifying Health Insurance Type from Survey Responses Using Enrollment Data
Presentation
Joanne Pascale, US Census Bureau
1:30 PM
The Story of Goldilocks and Three Twitter APIs
Yoonsang Kim, NORC at the University of Chicago
1:45 PM
An Analysis of Crash-Safety Ratings and the True Assessment of Injuries by Vehicle
Cody Philips, Indiana University
2:00 PM
Association of Primary Tumor Site With Mortality in Patients Receiving Bevacizumab and Cetuximab for Metastatic Colorectal Cancer
Presentation
Mayada Aljehani, Loma Linda University
2:15 PM
A Proposed Framework to Assess the Sensitivity of Network-Based Estimands to Non-Ignorable Non-Response, for Networks Ascertained With Non-Ignorable Sampling
Kenneth J Wilkins, National Institutes of Health, National Institute of Diabetes & Digestive & Kidney Diseases
2:30 PM
Floor Discussion
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