Name: 2018 Joint Statistical Meetings
Start: 2018-07-28T07:00:00+00:00
End: 2018-08-02
Location: Vancouver Convention Centre

Keyword Search

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CC = Vancouver Convention Centre F = Fairmont Waterfront Vancouver
* = applied session ! = JSM meeting theme

Keyword Search Criteria: learning returned 229 record(s)

Sunday, 07/29/2018

Deep Learning for Statistical Inference in Infectious Disease Systems
Rob Deardon, University of Calgary; Carolyn Augusta, University of Guelph; Graham Taylor, University of Guelph

Statistical Learning on Next-Generation Sequencing of T Cell Repertoire Data
Li Zhang, UCSF School of Medicine, UCSF; Tao He, San Francisco State University; Alan Paciorek, University of California, San Franciscornia ; Jason Cham, University of California, San Francisco; David Oh, University of California, San Francisco; Lawrence Fong, University of California, San Francisco

Novel Methods for Gene Set Enrichment Analysis with Empirical Memberships for Overlapping Genes
Yun Zhang, University of Rochester; Xing Qiu, University of Rochester

A Deep Learning Approach to the Estimation of Bias and Variance in HARDI
Allison Hainline, Vanderbilt University; Hakmook Kang, Vanderbilt University Medical Center; Bennett Landman, Vanderbilt University

Advantageous Statistical Tools for Stock Market Investing
Kenneth Davis

Big Data Detectives: Improving Human Health Through Informing Policy
Kristin Linn, University of Pennsylvania; Laura Hatfield, Harvard Medical School; Julian Wolfson, University of Minnesota; Sherri Rose, Harvard Medical School
2:05 PM

High-dimensional Cost-constrained Regression via Non-convex Optimization
Yufeng Liu, University of North Carolina at Chapel Hill
2:05 PM

Advantageous Statistical Tools for Stock Market Investing
Kenneth Davis
2:05 PM

A Four-Part Introduction to Deep Learning
Christopher Manning, Stanford University; Ruslan Salakhutdinov, Carnegie Mellon University
2:05 PM

Statistics at Consumer Reports
Michael Saccucci, Consumer Reports
2:45 PM

Exploring Clustering Applications in Outlier Detection for Administrative Data Sources
Elizabeth Ayres, Statistics Canada
2:50 PM

Novel Methods for Gene Set Enrichment Analysis with Empirical Memberships for Overlapping Genes
Yun Zhang, University of Rochester; Xing Qiu, University of Rochester
3:05 PM

Regularized Aggregation of Statistical Parametric Maps
Cheolwoo Park, University of Georgia; Li-Yu Wang, University of Georgia; Jongik Chung, University of Georgia; Hosik Choi, University of Georiga; Amanda Rodrigue, University of Georgia; Jordan Pierce, University of Georgia; Brett Clementz, University of Georgia; Jennifer McDowell, University of Georgia
3:20 PM

Data Adaptive Evaluation of Preprocessing Methods Using Ensemble Machine Learning
Rachael Phillips, Biostatistics, UC Berkeley
4:05 PM

Mixing Active Learning and Lecturing: Using Interactive Visualization as a Teaching Tool
Jessica Minnier, Oregon Health & Science University; Ted Laderas, Oregon Health & Science University
4:20 PM

Entity Resolution with Societal Impacts in Statistical Machine Learning
Rebecca C. Steorts, Duke University
4:30 PM

Q-Learning for Dynamic Treatment Regimes on CODIACS Vanguard Randomized Controlled Trial
Eun Jeong Oh, Columbia; Min Qian, Columbia University; Ying Kuen Ken Cheung, Columbia University
4:35 PM

Predictive and Interpretable Bayesian Machine Learning Models for Understanding Microbiome Dynamics
Georg Kurt Gerber, Harvard Medical School / Brigham and Women's Hospital
4:45 PM

Precision Medicine in Dynamic-Time Systems
Michael Lawson
5:05 PM

Monday, 07/30/2018

Tools, Resources and Skills for Statistics Distance Learning/Blended Learning
Xiaofang Shi, University of Kentucky

Machine-Learning Approach to Defining Covariates to Increase Study Power in ALS Clinical Trials and Other Multifactorial Heterogeneous Disease Areas
Danielle Beaulieu, Origent Data Sciences; Albert Taylor, Origent Data Sciences; Samad Jahandideh, Origent Data Sciences; David Ennist, Origent Data Sciences; Andrew Conklin, Origent Data Sciences; Mike Keymer, Origent Data Sciences

Improving Health Outcomes on the Last Mile of a Learning Healthcare System - the Importance of Leading with Statistics
Daniel Byrne, Vanderbilt University; Henry Domenico, Vanderbilt; Li Wang, Vanderbilt

Learning an Interpretable Behavioral Intervention Policy Using MHealth Data
Xinyu Hu, Columbia University; Min Qian, Columbia University; Ying Kuen Ken Cheung, Columbia University

Computing Mean Partition and Assessing Uncertainty for Clustering Analysis
Beomseok Seo, Penn State University; Lin Lin, The Pennsylvania State University; Jia Li, Penn State University

Classroom Demonstration: Deep Learning for Classification and Prediction, Introduction to GPU Computing
Eric Suess, CSU East Bay

Predicting Hospital Readmission for Diabetes Patients by Classical and Machine Learning Approaches
Gabrielle LaRosa, University of Pittsburgh; Chathurangi Pathiravsan, Southern Illinois University Carbondale; Rajapaksha Wasala M Anusha Madushani, University of Florida

The Classification of Stellar Systems Through Singular Spectrum Analysis
Kevin Matheson, Western Washington University; Kevin Covey, Western Washington University; Kimihiro Noguchi, Western Washington University

Machine Learning with Ensemble Feature Selections for Mass Spectrometry Data in Cancer Study
Yulan Liang, University of Maryland Baltimore; Amin Gharipour, Griffith University; Arpad Kelemen, University of Maryland Baltimore; Adam Kelemen, University of Maryland College Park; Hui Zhang, Johns Hopkins Medical Institutions

Bayesian and Unsupervised Machine Learning Machines for Jazz Music Analysis
Qiuyi Wu, ASA; Ernest Fokoue, ASA

A Generalization of Convolutional Neural Networks to Graph-Structured Data
Yotam Hechtlinger, Carnegie Mellon Univ; Purvasha Chakravarti, Carnegie Mellon University; Jining Qin, Carnegie Mellon University

Classification Accuracy of Unsupervised Learning Methods with Discrete and Mixture Distributed Indicators: a Monte Carlo Simulation Study
Chi Chang

Comparison of Methods for Predicting High-Cost Patients Captured Within the Oncology Care Model (OCM): a Simulation Study
Jung-Yi Lin, Icahn School of Medicine at Mount Sinai; Wei Zhang, UALR; Mark Liu, Mount Sinai Health System; Mark Sanderson, Mount Sinai Health System; Luis Isola, Mount Sinai Health System; Madhu Mazumdar, Icahn School of Medicine at Mount Sinai; Liangyuan Hu, Icahn School of Medicine at Mount Sinai

Shiny Dashboards to Help Students Improve Performance
Robert Carver, Brandeis International Business School

Efficacy of 'the Islands'-Based Projects Compared to Student-Collected Data Projects in Introductory Statistics Courses
Ryne VanKrevelen, Elon University; Kirsten Doehler, Elon University; Andrea Metts, Elon University; Lisa Rosenberg, Elon University; Laura Taylor, Elon University

Multiple Imputation Using Denoising Autoencoders
Lovedeep Gondara

Deep Learning on Small Data - Experiences in Transfer Learning for Healthcare
Dennis Murphree

Model Class Reliance: Variable Importance Measures for Any Machine Learning Model Class, from the
Aaron Fisher, Harvard University; Cynthia Rudin, Duke University; Francesca Dominici, Harvard T. H. Chan School of Public Health

BENCHMARKING the EFFECTIVENESS of CATEGORICAL RESPONSE VARIABLE MODELS and THEIR VISUALIZATIONS on WEATHER DATA
Kristen Bystrom; Zhi Yuh Ou Yang, Simon Fraser University; Lei Chen, Simon Fraser University

The Novel Communication Tool: Mathematics Classroom Collaborator (MC2)
Sohee Kang, University of Toronto Scarborough; Marco Pollanen, Trent University; Sotirios Damouras , University of Toronto Scarborough

New Applications of Machine Learning to Estimating Large Physician Demand Models
Bryan Sayer, Social & Scientific Systems, Inc.; William Encinosa, Agency for Health Care Quality and Research

Affordable and Open Educational Resources (OER) in Statistical Education
Suhwon Lee, Univ of Missouri

How Students Make Sense of Data on an E-Learning Platform
Philipp Burckhardt, Carnegie Mellon University; Christopher Genovese, Carnegie Mellon University; Rebecca Nugent, Carnegie Mellon University

Battle Royale: Machine Learning vs. Mechanistically Motivated Spatio-Temporal Models for Atmospheric and Oceanic Processes
Christopher K. Wikle, University of Missouri
8:35 AM

Deep Learning on Small Data - Experiences in Transfer Learning for Healthcare
Dennis Murphree
8:40 AM

Transitioning Statistical Consultation Training Away from the Classroom
Viviana Rodriguez, Virginia Commonwealth University; Adam Sima, Virginia Commonwealth University; Brian S Di Pace, Virginia Commonwealth University
8:50 AM

Shiny Dashboards to Help Students Improve Performance
Robert Carver, Brandeis International Business School
8:55 AM

Breaking Computational Chicken-And-Egg Loop in Adaptive Sampling and Estimations Using Locality Sensitive Sampling (LSS)
Anshumali Shrivastava, Rice University
8:55 AM

Learning Individualized Treatment Rules from Electronic Health Records Data
Yuanjia Wang, Columbia University
9:00 AM

Nonparametric Regression Models of Multilevel, Heterogeneous Treatment Effects: The National Study of Learning Mindsets
Carlos Carvalho, University of Texas; Jared S Murray, University of Texas at Austin; Paul Richard Hahn, Arizona State University ; David Yeager, The University of Texas at Austin; Elizabeth Tipton, Columbia University
9:00 AM

Learning from Dynamical Systems
Ingo Steinwart, University of Stuttgart
9:05 AM

Building a Genomic Signature via Transfer Learning on Both Labelled and Unlabelled High-Dimensional Data: a Case Study in Predicting Prostate Cancer Metastasis
Yang Liu, GenomeDx Biosciences; Hossein Sharifi-Noghabi, Simon Fraser University; Nicholas Erho, GenomeDX Biosciences; Raunak Shrestha, Vancouver Prostate Centre; Mohammed Alshalalfa, GenomeDX Biosciences; Elai Davicioni, GenomeDX Biosciences; Colin Collins, Vancouver Prostate Centre; Martin Ester, Simon Fraser University
9:05 AM

Statistics Projects in a PIC-MATH Course
Debra Hydorn, University of Mary Washington
9:20 AM

Multiple Imputation Using Denoising Autoencoders
Lovedeep Gondara
9:20 AM

Variance of Treatment Effect, an Important Yet Difficult Parameter
Jonathan Levy, UC Berkeley
9:20 AM

An Analysis on the Accuracy of Weather Forecasts
Benjamin William Schweitzer, Miami University; Nichole Rook, Miami University; Ryan Estep, Miami University; Robert Garrett, Miami University; Thomas Fisher, Miami University
9:30 AM

Visual Analytics in the Real World Evidence Data Realm
Melvin Munsaka, AbbVie, Inc.; Kefei Zhou, Theravance Biopharma; Krishan P. Singh, GlaxoSmithKline
9:35 AM

A Venn-Diagram Analysis of the Role of Statistics in Data Science
John McKenzie, Babson College
9:50 AM

Affordable and Open Educational Resources (OER) in Statistical Education
Suhwon Lee, Univ of Missouri
9:50 AM

New Applications of Machine Learning to Estimating Large Physician Demand Models
Bryan Sayer, Social & Scientific Systems, Inc.; William Encinosa, Agency for Health Care Quality and Research
10:05 AM

Effective Data Competition Hosting: Strategic Design and Analysis to Maximize Learning
Christine M Anderson-Cook, Los Alamos National Laboratory; Kary Myers, Los Alamos National Laboratory
10:35 AM

ESTIMATING TREATMENT IMPORTANCE in MULTIDRUG-RESISTANT TUBERCULOSIS USING TARGETED LEARNING: AN OBSERVATIONAL INDIVIDUAL PATIENT DATA NETWORK META-ANALYSIS
Guanbo Wang, McGill University; Mireille Schnitzer, University of Montreal; Andrea Benedetti, Respiratory Epidemiology and Clinical Research Unit, McGill University Health Centre
10:35 AM

Model Selection, Contingency Tables and Human Mobility
Adrian Dobra, University of Washington
10:35 AM

Risk Analysis in Banking
Vijayan Nair, 215157
10:55 AM

Forecasting of Grape Powdery Mildew Disease Risk in Vineyards Using a Bayesian Learning Network Model
Nathaniel Newlands, Agriculture and Agri-Food Canada (Science and Technology Branch); Weixun Lu, Agriculture and Agri-Food Canada (Science and Technology Branch); Odile Carisse, Agriculture and Agri-Food Canada (Science and Technology Branch); David E. Atkinson, University of Victoria
11:05 AM

Improved Doubly Robust Estimation in Learning Individualized Treatment Rules
Yinghao Pan, Fred Hutchinson Cancer Research Center; Yingqi Zhao, Fred Hutchinson Cancer Research Center
11:05 AM

General Techniques for Successful Data Science Competitions
Ian Michael Mouzon, Iowa State University
11:35 AM

Edward: a Library for Probabilistic Machine Learning and Statistics
Dustin Tran, Columbia University; David Blei, Columbia University
11:35 AM

Infer the in Vivo Point of Departure with ToxCast in Vitro Assay Data Using a Robust Learning Approach
Dong Wang, FDA National Center for Toxicological Research (NCTR)
11:35 AM

Deep Feature Selection and Causal Inference for Alzheimer's Disease
Yuanyuan Liu, The University of Texas Health Science Center at Houston; Qiyang Ge, Fudan University; Nan Lin, The University of Texas Health Science Center at Houston; Wenjia Peng, Bengbu Medical College; Rong Jiao, The University of Texas Health Science Center at Houston; Xuesen Wu, Bengbu Medical College; Momiao Xiong, The University of Texas Health Science Center at Houston
11:50 AM

Using Concomitant and Nested Simulation for Tail Risk Measure Estimation
Mingbin Feng, University of Waterloo
11:55 AM

Comparison of Interval Estimation in Machine Learning
Dai Feng, Merck; Andy Liaw, Merck & Co., Inc.; Vladimir Svetnik, Merck
2:05 PM

Structure Learning for Phylogenetic Tree with Quantitative Characters
Chaoyu Yu; Mathias Drton, University of Washington
2:20 PM

A Weighted Learning Approach for Sufficient Dimension Reduction in Binary Classification
Seung Jun Shin, Korea University
2:20 PM

Nonparametric Variable Importance Assessment Using Machine Learning Techniques
Brian Williamson, University of Washington; Peter Gilbert, Fred Hutchinson Cancer Research Center; Noah Simon, University of Washington; Marco Carone, University of Washington
2:25 PM

Greedy Active Learning Algorithm for Logistic Regression Models
Ray-Bing Chen, National Cheng Kung University, Taiwan; Hsiang-Ling Hsu, National University of Kaohsiung; Yuan-Chin Ivan Chang, Academia Sinica
2:35 PM

A Hierarchical Bayesian Cognitive Diagnostic Factor Model for Learning Trajectories
Albert Man, UIUC; Steven Culpepper, University of Illinois at Urbana-Champaign
2:50 PM

PULasso: High-Dimensional Variable Selection with Presence-Only Data
Hyebin Song, UW-Madison
3:05 PM

Iterative Quantile Nearest-Neighbors
Karsten Maurer, Miami University
3:05 PM

Using Predictive Modeling in Survey Methodology to Identify Panel Nonresponse
Bernd Weiss, GESIS - Leibniz-Institute for the Social Sciences; Jan-Philipp Kolb, GESIS - Leibniz-Institute for the Social Sciences; Christoph Kern, University of Mannheim
3:05 PM

Prediction Using Machine Learning Algorithms by Small Sample Size Data
Yan Wang, Field School of Public Health, UCLA; Honghu Liu, UCLA; Jian L Zhang, Kaiser Permanente
3:20 PM

Ensemble of Iterative Classifier Chains for Multi-Label Classification
Zhoushanyue He, University of Waterloo; Matthias Schonlau, University of Waterloo
3:35 PM

Tuesday, 07/31/2018

Advanced Methods in Quantitative Imaging Analysis
Hongtu Zhu, University of Texas M.D. Anderson

Renewable Estimation and Incremental Inference in Generalized Linear Models with Streaming Data Sets
Lan Luo; Peter X.-K. Song, University of Michigan

Online Local Q-Learning
Lili Wu, NCSU; Eric Laber, North Carlina State University

SuperLearning and Tree-Regression for Developing Treatment Rules That Optimize Health Outcomes
Andre Kurepa Waschka, University of California, Berkeley

Interpretable Statistical Machine Learning for Validation and Uncertainty Quantification of Complex Models
Ana Kupresanin, Lawrence Livermore National Laboratory

Identifying Morphologies of Precancerous Cells
Theresa Gebert, Carnegie Mellon University

An Application of Machine Learning for 3D IC Defect Detection
Meihui Guo, National Sun Yat-Sen University; Yu-Jung Huang, I-Shou University

Performance Comparison of Post-Hoc Subgroup Search Algorithms for Clinical Trials
Victor Talisa, University of Pittsburgh; (Joyce) Chung-Chou H. Chang, University of Pittsburgh

A Machine Learning (ML) Approach to Prognostic and Predictive Covariate Identification for Subgroup Analysis and Hypotheses Generation
David A James, Novartis

Claim-Level Models Using Statistical Learning Techniques and Risk Analysis
Mathieu Pigeon, Université du Québec à Montréal; Francis Duval, Université du Québec à Montréal

Targeted Maximum Likelihood Estimation of Causal Effects Based on Observing a Single Time Series
Ivana Malenica; Mark van der Laan, UC Berkeley

Machine Learning to Evaluate the Quality of Patient Reported Epidemiological Data
Robert L. Wood, Resonate & Wichita State University; Futoshi Yumoto, Resonate; Rochelle Tractenberg, Georgetown University

Predicting Overflow: A Novel Application of Latrine Sensors and Machine Learning for Optimizing Sanitation Services in Informal Settlements
Phillip Turman-Bryant, Portland State University; Evan Thomas, Portland State University

A Modified Approach to Component-Wise Gradient Boosting for High-Dimensional Regression Models
Brandon Butcher, University of Iowa; Brian J. Smith, University of Iowa

Committee on Law and Justice Statistics
Joel Hunt, National Institute of Justice; Patryk Miziula, deepsense.ai; George Mohler, IUPUI; Tuanjie Tong, Intuidex, Inc.; Dylan Fitzpatrick, Carnegie Mellon University
8:35 AM

Personalization Through Uplift Modeling: Techniques and Business Applications
Victor Lo, Fidelity Investments
8:35 AM

A Comparison of Similarity Scores Between Bullet Casings: Forensic Analysts Versus an Algorithm
Maria Cuellar, Carnegie Mellon University
8:35 AM

Random Forests for Big Data
Jean-Michel Poggi, LMO, University Paris Sud; Robin Genuer, ISPED, Univ. Bordeaux ; Nathalie Villa-Vialaneix, MIA-T, INRA of Toulouse; Christine Tuleau-Malot , University Nice, CNRS, LJAD
8:55 AM

Double Time: Integrating Online Learning Tools with a Flipped Classroom in a Public Health Statistics Course
Brandon George, Thomas Jefferson University
8:55 AM

Improving the Value of Public Data with Recount2 and Phenotype Prediction
Shannon Ellis, Johns Hopkins University, Bloomberg School of Public Health
8:55 AM

Analyzing Large Scale Genomics Data with Apache Spark and ADAM
Frank Nothaft, Databricks
9:15 AM

Active Learning Approaches for a Large Online Biostatistics Course
Rebecca Andridge, The Ohio State University College of Public Health
9:15 AM

Results from a Multi-Institution Study of the Progression and Retention of Student Learning Using Simulation-Based Inference
Nathan Tintle, Dordt College
9:35 AM

Machine Learning Methods for Animal Movement
Dhanushi A Wijeyakulasuriya, Pennsylvania State University; Ephraim Hanks, The Pennsylvania State University; Benjamin Shaby, Penn State University
9:35 AM

Flipping Online: Creating an Active Learning Classroom in an Online Biostatistics Course
Ann M Brearley, University of Minnesota; Laura J Le, University of Minnesota
9:35 AM

Performance Comparison of Post-Hoc Subgroup Search Algorithms for Clinical Trials
Victor Talisa, University of Pittsburgh; (Joyce) Chung-Chou H. Chang, University of Pittsburgh
9:40 AM

New Approaches Towards Translational Neuroimaging
Martin A Lindquist, Johns Hopkins University
9:50 AM

Budget-Constrained Feature Selection for Binary Classification: a Neyman-Pearson Approach
Yiling Chen, University of California, Los Angeles; Xin Tong, University of Southern California; Jingyi Li, University of California, Los Angeles
10:05 AM

The Reduced PC-Algorithm: Improved Causal Structure Learning in Large Random Networks
Arjun Sondhi, University of Washington; Ali Shojaie, University of Washington
10:35 AM

Claim-Level Models Using Statistical Learning Techniques and Risk Analysis
Mathieu Pigeon, Université du Québec à Montréal; Francis Duval, Université du Québec à Montréal
10:35 AM

Targeted Maximum Likelihood Estimation of Causal Effects Based on Observing a Single Time Series
Ivana Malenica; Mark van der Laan, UC Berkeley
10:35 AM

Using Q-Learning Method in Identify Optimal Treatment Regime
Haocheng Li, Hoffmann-La Roche Limited (Roche Canada); Vincent Shen, Hoffmann-La Roche Limited (Roche Canada); Hao Xu, Hoffmann-La Roche Limited (Roche Canada); Sylvia Hu, Roche-Genentech
10:35 AM

Structural Learning and Integrative Decomposition of Multi-View Data
Irina Gaynanova, Texas A&M Univeristy; Gen Li, Columbia University
10:55 AM

Analyzing Cosmic Webs Using Geometric Approaches
Yen-Chi Chen, University of Washington
10:55 AM

Using Genomic Features to Make Smart Clinical Decisions: The Power of Machine Learning with RNA-Seq
Jing Huang, Veracyte Inc; Su yeon Kim , Veracyte Inc; Yangyang Hao, Veracyte Inc; Jing Lu, Veracyte Inc; Joshua Babiarz, Veracyte Inc; Sean Walsh, Veracyte Inc; Giulia Kennedy, Veracyte Inc
11:00 AM

Personalized Solution Recommendation for Google Cloud Marketplace
Tianhong He, Google; Sangho Yoon, Google
11:05 AM

Estimation and Optimization of Composite Outcomes
Daniel J Luckett, University of North Carolina at Chapel Hill; Eric Laber, North Carlina State University; Michael Kosorok, University of North Carolina at Chapel Hill
11:15 AM

Learning-Based Inflation Expectations in an Unobserved Components Model
Srikanth Ramamurthy, Loyola University Maryland
11:15 AM

Spectral Methods for Kernel Learning
Charlotte Haley, Argonne National Lab; Christopher J Geoga, Argonne National Laboratory; Mihai Anitescu, Argonne National Laboratory
11:20 AM

Statistical Modeling for Pooling and Analyzing Multi-Site Data Sets Using Maximum Mean Discrepancy
Hao Zhou, University of Wisconsin Madison
11:20 AM

Ensemble Learning for Estimating Individualized Treatment Effects in Student Success Studies
Richard Levine, San Diego State University; Joshua Beemer, San Diego State University; Juanjuan Fan, San Diego State University
11:20 AM

Spatial Statistics Vs Machine Learning: Evaluating Air Pollution Exposure Prediction Models
Gregory Watson, UCLA; Donatello Telesca, UCLA
11:20 AM

Statistical Inference for Online Learning and Stochastic Approximation via Hierarchical Incremental Gradient Descent
Weijie Su, University of Pennsylvania; Yuancheng Zhu, University of Pennsylvania
11:35 AM

A Machine Learning (ML) Approach to Prognostic and Predictive Covariate Identification for Subgroup Analysis and Hypotheses Generation
David A James, Novartis
11:35 AM

An Application of Machine Learning for 3D IC Defect Detection
Meihui Guo, National Sun Yat-Sen University; Yu-Jung Huang, I-Shou University
11:40 AM

A Modified Approach to Component-Wise Gradient Boosting for High-Dimensional Regression Models
Brandon Butcher, University of Iowa; Brian J. Smith, University of Iowa
11:45 AM

Sequential Bayesian Analysis of Multivariate Count Data
Tevfik Aktekin , University of New Hampshire; Nick Polson, University of Chicago ; Refik Soyer , George Washington University
11:50 AM

Concept Maps, Feedback, and Statistics Learning: Exploring the Effects of Expert Map Feedback and Peer Feedback on Concept Map Structure
Terry Hickey, St. Martin's University
11:50 AM

Inference of Transcription Factor Binding Sites in New Cell Types from Open Chromatin and Gene Expression Data
Michael M. Hoffman, Princess Margaret Cancer Centre/University of Toronto; Mehran Karimzadeh, University of Toronto
2:05 PM

Evidence-based Policy for People with Disabilities: An Analysis of Disabilities in the DPRK within the Global Context of Disability Studies
Giang Huong Nguyen, University of Iowa; Allison Conners, University of Toronto; Sophie Lee, ISR Foundation Center for Interdisciplinary Research; Nema Dean, University of Glasgow; Paul Chun, ISR Foundation Center for Interdisciplinary Research
2:05 PM

Predicting Panel Drop-Outs with Machine Learning
Christoph Kern, University of Mannheim
2:20 PM

Causal Inference Using EMRs with Missing Data: a Machine Learning Approach with an Application on the Evaluation of Implantable Cardioverter Defibrillators
Changyu Shen, Beth Israel Deaconess Medical Center, Harvard Medical School; Xiaochun Li, Indiana University; Zuoyi Zhang, Regenstrief Institute; Alfred E Buxton, Beth Israel Deaconess Medical Center
2:25 PM

Dynamic, Personalized Instruments via Responsive Matrix Sampling with High-Dimensional Covariates
Sean Taylor, Facebook; Curtiss Cobb, Facebook; Chelsea Zhang, UC Berkeley
2:35 PM

A Comparison of Automatic Algorithms for Occupation Coding
Malte Schierholz, Institute for Employment Research
2:50 PM

Unsupervised Learning for Deciphering Mutational Signatures in Human Cancer
Ludmil B Alexandrov, University of California, San Diego; Velimir V Vesselinov, Los Alamos National Lab; Boian S Alexandrov, Los Alamos National Lab
2:55 PM

The Use of Machine Learning Methods to Improve the US National Resources Inventory Survey
Zhengyuan Zhu, Iowa State University
3:05 PM

Compressing Scientific Data: Reducing Storage While Preserving Information
Dorit Hammerling, National Center for Atmospheric Research; Joseph Guinness, NC State University; Allison Baker, National Center for Atmospheric Research
3:25 PM

Wednesday, 08/01/2018

Preparing Statistician to Successfully Data Scientist in Big Data Era
Ming Li, Amazon

Data Science in Marketing Research
Chen Teel, Electronic Arts

Improving Object Detection with Image Preprocessing
Timothy J. Park, Purdue University

Analyzing Bias in Object Detection Data Sets
Meera Haridasa, Purdue University; Cailey Farrell, Purdue University

Fast Bayesian Sparse Learning via Thresholding Priors
Andrew Whiteman, University of Michigan; Jian Kang, University of Michigan

Single Cell Data Mining of Live Cell Epigenetic Modifications
Chris Bryan

A Multivariate Probit Model for Learning Trajectories with Application to Classroom Assessment
Yinghan Chen, University of Nevada, Reno; Steven Culpepper, University of Illinois at Urbana-Champaign

A Stagewise Prognostic Control Predictive Approach (SPCPA) for Subgroup Identification and Its Application in a Phase II Study
Wanying Li, Gilead Sciences; Wangshu Zhang, Gilead Sciences; Lovely Goyal, Gilead Sciences; Yuanyuan Xiao, Gilead Sciences

Exposure-Response Analysis with Random Forest
Zifang Guo, Merck; Thomas Jemielita, Merck & Co.; John Kang, Merck

Interpretable Analysis of Team Performance in Soccer Using Tracking Data: a Hybrid of Supervised and Unsupervised Methods.
Paul David Power, STATS
8:35 AM

Identifying Misclassifications in Consumer Expenditure Data
Clayton Knappenberger, U.S. Bureau of Labor Statistics
8:35 AM

Reproducing Kernels for Pairwise Learning
Xin Guo, The Hong Kong Polytechnic University; Ting Hu, Wuhan University; Qiang Wu, Middle Tennessee State University; Ding-Xuan Zhou, City University of Hong Kong
8:35 AM

Estimation of Economic Models with Non-Euclidean Data
Suyong Song, University of Iowa; Stephen Baek, University of Iowa
8:35 AM

Q-Learning with Missing Data
Lin Dong, North Carolina State University; Eric Laber, North Carlina State University
8:55 AM

Large-Scale Interactives for Large-Enrolment Courses
Anna Fergusson, The University of Auckland
8:55 AM

Mortality Prediction with Multiple Unordered Treatments for Aortic Valve Replacement
Samrachana Adhikari, Harvard Medical School; Sherri Rose, Harvard Medical School; Sharon-Lise Normand, Harvard University; Jordan Bloom, Harvard Medical School; David Shahian, Harvard Medical School; Jake Spertus, Harvard Medical School
8:55 AM

The CFR Miner: Natural Language Processing of the Code of Federal Regulations Using R Studio and Shiny
Richard Schwinn, U.S. Small Business Administration
9:15 AM

REMAP-CAP: a Precision Medicine Embedded Platform Trial for Community Acquired Pneumonia
Scott Berry, Berry Consultants
9:15 AM

Can We Train Machine Learning Methods to Outperform the High-Dimensional Propensity Score Algorithm?
Mohammad Ehsanul Karim, University of British Columbia; Robert W Platt, McGill University
9:20 AM

Deep Learning in Medical Imaging: Evaluation and Study Design
Robyn Ball, Stanford University; David Larson, Stanford University; Pranav Rajpurkar, Stanford University; Matthew Chen, Nines AI; Jeremy Irvin, Stanford University; Jaden Yang, Stanford University; Matthew P Lungren, Stanford University
9:20 AM

Constructing Stabilized Dynamic Treatment Regimes
Guanhua Chen, University of Wisconsin-Madison; Ruoqing Zhu, University of Illinois Urbana-Champaign; Yingqi Zhao, Fred Hutchinson Cancer Research Center; Yingye Zheng, Fred Hutchinson Cancer Research Center
9:35 AM

Heterogeneous Treatment Effect Estimation through Deep Learning
Ran Chen, Wharton; Hanzhong Liu, Center for Statistical Science, Tsinghua University
9:35 AM

A Simulation Study on the Performance of Deep Learning Methods for Multi-Category Classification
Dawei Liu, Biogen; Ih Chang, Biogen
9:50 AM

Exposure-Response Analysis with Random Forest
Zifang Guo, Merck; Thomas Jemielita, Merck & Co.; John Kang, Merck
10:00 AM

Regression Trees and Ensemble Methods for Multivariate Outcomes
Evan Reynolds, University of Michigan; Mousumi Banerjee, University of Michigan
10:35 AM

Learning the Number of Components and Data Clusters in Bayesian Finite Mixture Models
Bettina Grün, Johannes Kepler Universität; Gertraud Malsiner-Walli, Wirtschaftsuniversität Wien; Sylvia Frühwirth-Schnatter, Wirtschaftsuniversität Wien
10:35 AM

Manifold Learning for Network Inference
Mingyue Gao, The Johns Hopkins University; Carey E Priebe, Johns Hopkins University; Minh Tang, Johns Hopkins University
10:35 AM

Training Data Scientists - Experiential Learning Through Corporate/University Partnerships
Herman Ray
10:35 AM

Fusion Learning with High-Dimensionality
Xin Gao, York University; Raymond J. Carroll, Texas A & M University
10:50 AM

Real-World Learning Analytics: Modeling Student Academic Practices and Performance
Chantal D. Larose, Eastern Connecticut State University; Kim Y. Ward, Eastern Connecticut State University
10:50 AM

Distributed Data Science with Sparklyr
Javier Luraschi, RStudio; Kevin Kuo, RStudio
11:05 AM

Sparse Model Identification and Learning for Ultra-High-Dimensional Additive Partially Linear Models
Xinyi Li; Lily Wang, Iowa State University; Dan Nettleton, Iowa State University
11:05 AM

Deep Learning for Data Imputation and Calibration Weighting
Yijun Wei, NISS; Luca Sartore, National Institute of Statistical Sciences; Jake Abernethy, National Agricultural Statistics Service, United States Department of Agriculture; Darcy Miller, National Agricultural Statistics Service; Kelly Toppin, National Agricultural Statistics Service; Clifford Spiegelman, Texas A&M University; Michael Hyman, USDA-NASS
11:15 AM

Time-Constrained Predictive Modeling on Large and Continuously Updating Financial Data Sets
Bernard Lee, HedgeSPA Limited; Nicos Christofides, Imperial College London
11:20 AM

Distributed Machine Learning with H2O
Navdeep Gill, H2O.ai
11:35 AM

Reflections on 10 Years of Teaching Online
Iain Pardoe, Thompson Rivers University
11:50 AM

Data Science in a Hurry
Iyue Sung
11:50 AM

Integrative Statistical Learning with Real World Healthcare Data: Towards a Data Driven Suicide Prevention Framework
Kun Chen, University of Connecticut
11:55 AM

Do as I Say, Not as I Do: Learning from My Mistakes as a Statistical Collaborator
Richard De Veaux, Williams College
2:05 PM

Deep Learning in Quantitative Imaging Analysis
Hongtu Zhu, University of Texas M.D. Anderson
2:05 PM

On the Art and Science of Machine Learning Explanations
Patrick Hall, H20.ai
2:05 PM

Dependency Diagnostic: Visually Understanding Pairwise Variable Relationships
Kevin Lin, Carnegie Mellon University; Han Liu, Northwestern University
2:20 PM

An Algorithm for Removing Sensitive Information
James Johndrow, Stanford University; Kristian Lum, Human Rights Data Analysis Group
2:25 PM

Cooperative Learning of Deep Energy-Based Model and Latent Variable Model via MCMC Teaching
Ying Nian Wu, UCLA
2:30 PM

An Old Dog Self-Teaching New Tricks
Mithat Gonen, Memorial Sloan Kettering Cancer Center
2:45 PM

Local, Model-Agnostic Explanations of Machine Learning Predictions
Sameer Singh, University of California, Irvine
2:45 PM

Evaluating the Census Planning Database, MSG, and Paradata as Predictors of Household Propensity to Respond
Xiaoshu Zhu, Westat; Robert Baskin, Westat; David Morganstein, Westat
2:50 PM

Think Deeper with Deep Learning
Saratendu Sethi, SAS Institute Inc.
2:55 PM

Can We Compute an Optimal Sparse Decision Tree?
Cynthia Rudin, Duke University; Elaine Angelino, Berkeley; Nicholas Larus-Stone, Cambridge; Margo Seltzer, Harvard; Daniel Alabi, Harvard
3:05 PM

Weighted Stochastic Gradient Descent Algorithm
Xueying Tang, Columbia University; Zhi Wang, Columbia University; Jingchen Liu, Columbia University
3:05 PM

Optimal Bayesian Design for Models with Intractable Likelihoods via Machine Learning Methods
Christopher C Drovandi, Queensland University of Technology; Markus Hainy, QUT
3:05 PM

Weight Normalized Deep Neural Networks
Xiao Wang , Purdue University; Yixi Xu, Purdue University
3:20 PM

A Classification Framework for Forecast Model Selection
Thiyanga Talagala, Monash University; Rob J Hyndman, Monash University; George Athanasopoulos, Monash University
3:20 PM

Beyond Feature Attribution: Quantitative Concept-Based Interpretability with TCAV
Been Kim, Google Brain
3:25 PM

Dimension Reduction of High-Dimensional Data Sets Based on Stepwise SVM
Elizabeth Chou, National Chengchi University; Tzu-Wei Ko, National Chengchi University
3:35 PM

Thursday, 08/02/2018

Statistical Consulting in the Age of Cognitive Computing, Deep Learning, and AI: Obsolete or Needed Now More Than Ever?
Nikola Andric, Deloitte Consulting LLP
8:35 AM

Sequential Prediction, Martingale Tail Bounds and Automatic Machine Learning
Karthik Sridharan, Cornell University
8:35 AM

Inferential Challenges in Machine Learning and Precision Medicine
Michael Kosorok, University of North Carolina at Chapel Hill
8:35 AM

Leveraging Adiabatic Quantum Computation for Election Forecasting
Maxwell Henderson, QxBranch
8:35 AM

Fair Inference Through Semiparametric-Efficient Estimation Over Constraint-Specific Paths
Nima Hejazi, Group in Biostatistics, UC Berkeley
8:35 AM

A Comparison of Record Linkage Techniques
Lowell Mason, U.S. Bureau of Labor Statistics
8:35 AM

Automatic Wildfire Smoke Plume Identification from Satellite Imagery with Machine Learning
Alexandra Larsen, North Carolina State University; Ana Rappold, U.S. Environmental Protection Agency; Yi Qin, The Commonwealth Scientific and Industrial Research Organisation; Martin Cope, The Commonwealth Scientific and Industrial Research Organisation; Geoffrey Morgan, The University of Sydney; Ivan Hannigan, The University of Sydney; Brian J. Reich, North Carolina State University
8:35 AM

Gaussian Process Selections in Semiparametric Regression for Multi-Pathway Analysis
Jiali Lin, Virginia Tech; Inyoung Kim, Virginia Tech
8:50 AM

The Use of Machine Learning in the Pharmaceutical Industry: The Promise and the Peril
Todd Sanger, Eli Lilly and Company
8:55 AM

Uncertainty Quantification of Treatment Regime in Precision Medicine by Confidence Distributions
Minge Xie, Rutgers University; Yilei Zhan, Rutgers University; Sijian Wang, Rutgers University
9:00 AM

Learning Nonconvex Hierarchical Interactions
Lingzhou Xue, Penn State University and National Institute of Statistical Sciences
9:00 AM

Statistical Learning of Successful Smiles
Nathaniel Helwig, University of Minnesota
9:05 AM

The Use of Machine Learning and Statistics in the Technology Sector
Joseph Kelly, Google
9:15 AM

Optimal Treatment Recommendation via Subgroup Identification in Randomized Control Trials
Yang (Grace) Zhao, Gilead Sciences; Haoda Fu, Eli Lilly and Company
10:05 AM

Real Data Applications of Learning Curves in Cardiac Devices and Procedures
Usha Govindarajulu, SUNY Downstate School of Public Health; David Goldfarb, Montfiore Medical Center; Frederic Resnic, Lahey Clinic
10:35 AM

A Statistical Framework for Cross-Tissue Transcriptome-Wide Association Analysis
Yiming Hu, Yale University
10:55 AM

Data Science + Social Science: Using Data Science to Track Arrest-Related Deaths in the US
Duren Banks, RTI International; Peter Baumgartner, RTI International; Michael G. Planty, RTI International
11:00 AM

Targeted Learning for Causal Inference
Mark van der Laan, UC Berkeley
11:00 AM

Advances in Measuring User Learning
Niall Cardin, Google Inc.; Henning Hohnhold, Waymo
11:15 AM

A Model for Prioritizing Interventions for People at Risk of Incarceration
Erika Salomon, University of Chicago
11:25 AM

Semi-Supervised Learning for Joint Association and Classification Analysis of Multimodal Data
Yunfeng Zhang, Texas A&M University; Irina Gaynanova, Texas A&M Univeristy
11:35 AM

Classification of Healthcare Data: When Scarcity of Labeled Data Is the Norm Semi-Supervised Learning Methods Can Come to the Rescue
Didem Egemen, The George Washington University; Paulo Macedo, Integrity Management Services Inc.; Sewit Araia, Integrity Management Services Inc.
11:35 AM

Targeted Learning for Variable Importance in Precision Medicine
Yue You, Division of Biostatistics, University of California, Berkeley; Alan Hubbard, Division of Biostatistics, University of California, Berkeley; Rachael Callcut, Zuckerberg San Francisco General Hospital, University of California; Lucy Kornblith, Zuckerberg San Francisco General Hospital, UCSF; Sabrinah Christie, Zuckerberg San Francisco General Hospital, UCSF
11:50 AM

JSM 2018 Online Program

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