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Conference Program Plenary, invited, and contributed presentations

We invite researchers from academia, industry, and government to participate in the conference, exchange ideas, organize sessions, and present their research related to the spirit of QPRC and its theme. QPRC welcomes contributed papers and posters that align with its mission and theme — please submit abstracts after registering:

  • Last day to register a contributed talk: April 15, 2019.
  • Last day to register a contributed poster: May 15, 2019.
  • View photos from the conference.

    View presentations from the conference.

Plenary Speakers

Barry Nussbaum Conference Guest of Honor

The 112th President of the American Statistical Association, Dr. Nussbaum was former Chief Statistician at the US Environmental Protection Agency.

James Rosenberger

Director of the National Institute of Statistical Sciences, Dr. Rosenberger is Professor Emeritus and former Head of the Department of Statistics at the Pennsylvania State University

William F. Guthrie

William Guthrie is a Mathematical Statistician at the National Institute of Standards and Technology.

Farid Khafizov

Dr. Khafizov is the Distinguished Data Scientist at Verizon.

Plenary Talks

Plenary Talk 1. I've Never Met a Datum I Didn't Like Speaker: Barry D. Nussbaum, the QPRC Guest of Honor (2017 President, American Statistical Association; Chief Statistician, US Environmental Protection Agency, retired)June 11, 1:30 - 2:15, in Constitution Hall 1-2-3 

Plenary Talk 2. A Chance to Deliver Truth, Justice, and the American Way! Speaker: Will Guthrie (National Institute of Standards and Technology)June 12, 8:35 - 9:20, in Constitution Hall 1-2-3 

Plenary Talk 3. Quality Matters for Big Data Science Speaker: James Rosenberger (National Institute of Statistical Science, Pennsylvania State University)June 13, 8:05 - 8:50, in Constitution Hall 1-2-3 

Invited Sessions

Special session. Panel Discussion: Data Science in Industry and Government June 11, 8:45 - 9:45, in Constitution Hall 1-2-3 

  • Erica Groshen (Cornell University)
  • John L. Eltinge (U. S. Census Bureau)
  • Rafiq Mohammadi (iManage)
  • Andy Ravenna (SAS Institute, Inc.)

Special session. Software demonstrations -- SAS, Minitab, Trilobyte June 11, 10:10 - 10:40, in Constitution Hall 

  • Scott Kowalski (Minitab)
  • Ruth M. Hummel (JMP)
  • Marcela Salficka and Karel Kupka (Trilobyte)

Invited session 1. Recent Advances in Statistical Process Control Monitoring June 11, 10:50 - 12:20, in Constitution Hall 1 

  • Model-free monitoring and anomaly detection in dynamic networks, by Srijan Sengupta, Lata Kodali, and Leanna House (Virginia Tech)
  • Exploring and assessing the landscape of network monitoring techniques, by James Wilson (University of San Francisco)
  • Visualizing when prior information can reduce the necessary size of a reference sample when estimating in-control parameters for Shewhart and CUSUM charts for a normal process, by Daniel Jeske and Yijian Wang (University of California, Riverside)

Invited session 2. New Results in Sequential Analysis June 11, 10:50 - 12:20, in Constitution Hall 2 

  • Bounded-Length Confidence Interval Estimation Problems, by Nitis Mukhopadhyay (University of Connecticut)
  • A Sequential Stochastic Assignment Problem with Random Number of Jobs, by Yaakov Malinovsky (University of Maryland, Baltimore County), Alexander Goldenshluger (University of Haifa, Israel), and Assaf Zeevi (Columbia University)
  • Proportional Closeness Estimation of Probability of Contamination Under Group Testing, by Shelemyahu Zacks (Binghamton University) and Yaakov Malinovsky (Universty of Maryland, Baltimore County)

Invited session 3. New Frontiers of Time Series and Data Analysis June 11, 10:50 - 12:20, in Constitution Hall 3 

  • From Zero-Crossings to Quantile-Frequency Analysis of Time Series with an Application to Nondestructive Evaluation, by Ta-Hsin Li (IBM Research)
  • Interpoint Distances: Properties, Applications and Visualization, by Reza Modarres (George Washington University)
  • Estimation of Small Tail Probabilities by Repeated Out-of-Sample Fusion, by Benjamin Kedem (University of Maryland)

Invited session 4. Applied Data Mining June 11, 2:30 - 4:00, in Constitution Hall 1 

  • Unbiased AI through Data Sampling, by Kenton White (Advanced Analytics)
  • Power-Grid Controller Anomaly Detection with Enhanced Temporal Deep Learning, by Aswin Raghavan (SRI International)
  • Learning for the future – A Case Study on Reproducibility in Applied Data Science, by Colin Bellinger (NRC, Canada)

Invited session 5. New Models for Time-Varying Processes with Applications June 11, 2:30 - 4:00, in Constitution Hall 3 

  • Efficient Computation of Distributions of Pattern Statistics in Sparse Markov Models, by Donald Martin (North Carolina State University)
  • The Analysis of Periodic Point Processes, by Stephen Casey (American University)
  • Predicting Events from Longitudinal Data: The Imputed Cox Model, by James Troendle (NIH)

Invited session 6. Functional Data Analysis June 11, 2:30 - 4:00, in Constitution Hall 2 

  • Functional Data Analysis for Design of Experiments, by Tom Donnelly and Chris Gotwalt (SAS institute, Inc.)
  • Machine Learning Prediction with Streamed Sensor Data: Fitting Neural Networks using Functional Principal Components, by Chris Gotwalt (SAS Institute Inc.)
  • The Treachery of Images, by Theresa Utlaut and David Shykind (Logic Technology Development, Intel Corporation)

Invited session 7. Data Science and its Applications I June 11, 4:30 - 6:00, in Constitution Hall Kerwin Hall - 1; across Nebraska Ave., lower [terrace] level 

  • Statistical Analysis of Noise Multiplied Data Based on Multiple Imputation, by Bimal Sinha (University of Maryland - Baltimore County)
  • Generating Realistic Network Trace Data in the Absence of Users, Time, and Money: Methodology and Challenges, by Brian Ricks, Bhavani Thuraisingham (University of Texas at Dallas)
  • Robust high dimensional data stream classification with novelty detection, by Zhuoyi Wang (University of Texas at Dallas)

Invited session 8. Data Visualization June 11, 4:30 - 6:00, in Constitution Hall Kerwin Hall - 2; across Nebraska Ave., lower [terrace] level 

  • Effective Story Telling with Dynamic Data Visualizations, by Ruth M. Hummel (SAS Institute / JMP Division)
  • Geospatial Data Visualization: Depicting the Power of Where, by Matthew Berra (Environmental Systems Research Institute)
  • Advanced Visualization Techniques for Big Data, by Scott Lee Wise (SAS Institute/JMP Divsion)

Invited session 9. Large-Scale Machine Learning June 11, 4:30 - 6:00, in Constitution Hall Kerwin Hall - 3; across Nebraska Ave., lower [terrace] level 

  • Learning Semiparametric Regression with Missing Covariates Using Gaussian Process Models, by Dipak K. Dey, Abhishek Bishoyi, and Xiaojing Wang (University of Connecticut)
  • Computational Topology and Semistructured Data, by Faraz Ahmad (Teradata)
  • Classification of longitudinal data with haphazardly spaced intervals: Mixture-based mixed models vs clustering BLUPs from linear mixed models, by Jobayer Hossain (Nemours/Alfred I. duPont Hospital for Children and University of Delaware)

Invited session 10. Innovative Statistical Inference Methodologies with Interesting Applications - I June 12, 9:35 - 11:05, in Constitution Hall 1 

  • Data-Centric Approach to Rheumatoid Arthritis (RA): How Interactions Between Comorbidities Influence RA Incidence, by Grayden Shand (Clarkson University), Daniel Fuller (Clarkson University), Carly Lovelett (St. Lawrence Health System), Maren Wolf ((St. Lawrence Health System), Eyal Kedar (St. Lawrence Health System), Shantanu Sur (Clarkson University), and Sumona Mondal (Clarkson University)
  • A Class of Purely Sequential Minimum Risk Point Estimation Methodologies with Second-Order Properties, by Jun Hu (University of Vermont) and Nitis Mukhopadhyay (University of Connecticut)
  • Sequential Fixed-Width Confidence Interval Estimation for the Mean of a Normal Population: Sampling in Groups, by Zhe Wang and Nitis Mukhopadhyay (University of Connecticut)

Invited session 11. Cybersecurity and Anomaly Detection June 12, 9:35 - 11:05, in Constitution Hall 2 

  • Online Adaptive Metric Learning and Domain Adaptation for Cyber-Security, by Latifur Khan (University of Texas at Dallas)
  • A divide-and-conquer method for improving detection in cyber-security problems, by Nathalie Japkowicz (American University)
  • Deception-Enhanced Anomaly Detection, by Frederico Araujo (IBM)
  • Evolving Cyber Threat Detection Using Stream Analytics, by Pallabi Parveen (AT\&T)

Invited session 12. Novel Approaches for Structured Complex Data June 12, 9:35 - 11:05, in Constitution Hall 3 

  • Detection of structural breaks for panel data via the epsilon-complexity of continuous vector functions, by Alexandra Piryatinska (San Francisco State University)
  • Deep Learning Interpretation and its Connections to the Irrepresentability Condition, by Emre Barut (George Washington University)
  • Principal components analysis for heavy tailed data, by John Nolan (American University)

Invited session 13. Data Science and its Applications II June 12, 1:30 - 3:00, in Constitution Hall 2 

  • System to Identify, Classify and Manage Patients with Structural Heart Defects, by James P. McGlothlin (Fusion Consulting, Inc.), Ilija Stojic (Fusion Consulting, Inc.), and Timothy Martens (Loma Linda University Children's Hospital)
  • Will Big Data Kill Off Official Statistics?, by Erica Groshen (Cornell University)
  • Blockchain Data Analytics: A New Frontier in Data Science, by Cuneyt G. Akcora, Murat Kantarcioglu, and Yulia Gel (University of Texas at Dallas)

Invited session 14. Innovative Statistical Inference Methodologies with Interesting Applications - II June 12, 1:30 - 3:00, in Constitution Hall 1 

  • Change-Points in LSHD Data: Testing and Estimation with an Application to Large Sensor Arrays in Environmetrics, by Ansgar Steland (Institute of Statistics, RWTH Aachen University, Germany)
  • Two-Sample Tests of Hypotheses to Compare Normal Distributions: Unknown and Unequal Variances, by Yan Zhuang (Connecticut College)
  • Sequential Experimental Designs for Big Data Inference Problems, by Chen Zhang and Nitis Muhopadhyay (University of Connecticut)

Invited session 15. Mary G. and Joseph Natrella Session June 12, 1:30 - 3:00, in Constitution Hall 3 

  • Connect Model to Reality: On-site Surrogates for Large-Scale Calibration, by Jiangeng Huang (Virginia Tech), Bobby B. Gramacy (Virginia Tech), and Mickael Binois (Argonne National Laboratory)
  • A Generic Framework for Multisensor Degradation Modeling based on Supervised Classification and Failure Surface, by Changyue Song (University of Wisconsin--Madison), Kaibo Liu (University of Wisconsin--Madison), and Xi Zhang (Peking University, China)

Invited session 16. Text Analytics June 13, 9:00 - 10:30, in Constitution Hall 1 

  • Every Day Text Analysis, by Mark Bailey (SAS Institute Inc.)
  • Traveling from the DTM to the LSA, by Health Rushing (Adsurgo LLC)
  • Waste Not, Want Not: A Methodological Illustration of Quantitative Text Analysis, by Laura Castro-Schilo (SAS Institute Inc.)

Invited session 17. Machine Learning and its Applications June 13, 9:00 - 10:30, in Constitution Hall 2 

  • ALMOND: Adaptive Latent Modeling and Optimization via Neural Networks and Langevin Diffusion, by Yixuan Qiu (Carnegie Mellon University) and Xiao Wang (Purdue University)
  • Geometric Shape Deviation Modeling Across Different Processes and Shapes in Additive Manufacturing Systems, by Arman Sabbaghi (Purdue University)
  • Machine Learning Interpretability: An Examination of Diagnostic Procedures, by Zhishi Wang, Xiaoyu Liu, Jie Chen, and Vijayan Nair (Wells Fargo)

Invited session 18. Recent Advances in Statistical Machine Learning June 13, 9:00 - 10:30, in Constitution Hall 3 

  • Web Scraping-Natural Language Processing (NLP) for Disease Outbreak Detection and Information Extraction, by Yijun Frank Wei, Luca Sartore, and Nell Sedransk (National Institute of Statistical Sciences)
  • Gradient Boosting Decision Tree Techniques for Quality Improvement When Regression or Logistic Regression Fails to Work, by Dan Steinberg (Minitab) and Adam Russell (Tate \& Lyle)
  • Applications of Statistical Network Analysis and Neural Networks to Healthcare and Environmental Research, by Amal Agarwal (Pennsylvania State University)

Invited session 19. Advances in Spatial Data Science June 13, 10:50 - 12:20, in Constitution Hall 1-2 

  • Diagnosing and Modeling Spatial Dependence under Random Spatial Assignment, by David Darmofal (University of South Carolina), Charles J. Finocchiaro (Universiy of Oklahoma), and Indridi Indridason (University of California, Riverside)
  • Mining for Votes in the Golden State: A Political Application of Kriging, by Jamie Monogan (University of Georgia) and Jeff Gill (American University)
  • The Politics of Middletown: The Benefits, Challenges and Methods of Using Precincts in Spatial Analysis, by Chad Kinsella (Ball State University)
  • Effects of Space, Environment, and Terrain on Varieties of Homicide in Mexico, by Matthew Ingram (University at Albany)

Invited session 20. Data Science in Finance June 13, 10:50 - 12:20, in Constitution Hall 3 

  • Estimation of Expected Shortfall and Optimal Trading Execution with Constrained SMC, by Min Lin (Xiamen University (China), Chencheng Cai (Rutgers University), and Rong Chen (Rutgers University)
  • Biclustering Procedures for High-frequency Financial Time Series, by Nalini Ravishanker (University of Connecticut), Haitao Liu (Worcester Polytechnic Institute), and Jian Zou (Worcester Polytechnic Institute)
  • Financial forecasting based on alternative data sources, by Yada Zhu (IBM)

Deep Neural Network Success Stories and Applications. 

Speaker: Farid Khafizov (Distinguished Data Scientist, Verizon Communications Inc.)
Tuesday, June 11, 7:30 - 8:20, in Constitution Hall 1-2-3 

Contributed Sessions

Contributed Session A. Optimization of Statistical Experiments 
June 12, 11:30 - 12:30, in Constitution Hall 1 

  • Design of Cost-Effective Experiments, by Alexandra Kapatou (American University)
  • Laplace deconvolution with dependent errors: an application to the analysis of Dynamic Contrast Enhanced imaging data, by Rida Benhaddou (Ohio University)
  • On Supplementing Training Data, by William D. Heavlin (Google Accelerated Science)

Contributed Session B. Statistical process control 
June 12, 11:30 - 12:30, in Constitution Hall 2 

  • Efficient Global Monitoring Statistics for High-Dimensional Data, by Jun Li (University of California - Riverside)
  • Novel Nonparametric and Semiparametric Control Charts Utilizing Residuals, by Abdel-Salam G. Abdel-Salam (Qatar University)
  • Nonparametric Online Change Detection, by Lingzhe Guo and Reza Modarres (George Washington University)
  • The Shewhart chart with variable charting statistic, by Antonio Fernando Branco Costa (Institute of Industrial Engineering and Management, Federal University of Itajubá, Brazil)

Contributed Session C. Applications of data science in diverse fields 
June 12, 11:30 - 12:30, in Constitution Hall 3 

  • Generative Adversarial Network Stability and Performance for Low Dimensional Data, by Felix M. Jimenez (National Institute of Standards and Technology, University of Colorado Boulder), Amanda Koepke, Michael Frey (National Institute of Standards and Technology)
  • Hunting Trolls with Deep NLP, by Evan Crothers (University of Ottawa and Government of Canada)
  • New developments in modeling hogs production and growth at a finer temporal resolution, by Luca Sartore (NISS)

Contributed Posters

Special poster session is on June 11, 1:00 - 1:30, in Constitution Hall Atrium.

  • Use of Genetic Algorithms for Breast Phantom optimization, by Mosammat A. Tanbin (Delaware State University), David Pokrajac (Boeing), Tomasz Smolinski (Delaware State University)
  • Improved calculation of fire resistance for composite concrete slabs with sequential experimentation, by Adam L. Pintar, Jian Jiang, Jonathan M. Weigand, Joseph A. Main, and Fahim Sadek (National Institute of Standards and Technology)
  • Generative Art via Neural Networks, by Evan Russenberger-Rosica (American University)
  • Meta-Learning in Dynamic Cybersecurity Patterns and Anomalies Detection and Simulating Data with Concept Drift, by Sid Ryan (University of Ottawa), Iluju Kiringa (University of Ottawa), Nathalie Japkowicz (American University)
  • Policies Review Concerning Development of Big Data Industry in South Asia with Reference to Nepal, by Dila Ram Bhandari (Tribhuvan University, Nepal)
  • An Overview and Application of Quality Control Methodology, by Chelsea Mitchell (Virginia Commonwealth University)
  • Predictive Modeling of Shared Ride Modes in New York City: A Case Study Using Dynamic Compositions of Time Series, by Patrick T. Toman (The University of Connecticut)
  • EWMA chart with a Dynamic Sampling Scheme, by Samuel Anyaso-Samuel and Partha Mukherjee (Boise State University)
  • Data Science Methodology Frameworks to Increase Validity in Statistical Analyses, by Natascha Bolden (Johns Hopkins University)
  • Modeling and Monitoring Utility Time Series at the University of Connecticut, by Henry Linder (The University of Connecticut)
  • Clustering Travel Behavior Time Series using Topological Data Analysis, by Renjie Chen, Jingyue Zhang, and Nalini Ravishanker (University of Connecticut)