The printable schedule can be found here.

Monday, June 4
7:30-8:30am Registration & Continental Breakfast – VEC 401 multiple purpose room
8:30-8:55am Welcome
9:00-10:30am Parallel Sessions
VEC 404/405 High-dimensional Tensor Data Analysis
(organized and chaired by Xin “Henry” Zhang, Florida State U)
Xuan Bi (Yale) Multilayer Tensor Factorization with Applications to Recommender Systems
Will Wei Sun (University of Miami) Dynamic Tensor Clustering
Qing Mai (Florida State U) Covariate-adjusted tensor classification in high-dimensions
VEC 902/903 High-dimensional inference: assumption-lean or assumption-laden?
(organized by Ryan Tibshirani (CMU) and chaired by Jelena Bradic(UCSD))
Todd Kuffner (Washington U) Inferential goals, targets, and principles in high-dimensional regression
Lucas Janson (Harvard) Should We Model X in High-Dimensional Inference?
Andreas Buja (U Penn) Towards a Better Understanding of Ą “High-Dimensional” Linear Least Squares Regression
VEC 1202/1203 Modern nonparametric statistics
(organized by Richard Samworth (Cambridge) and chaired by Qi, Zhengling (UNC))
Samory Kpotufe (Princeton) Regimes of label-noise determine the benefits of Active Learning
Peter Orbanz (Columbia) Sampling design and stochastic gradient descent for relational data
Tong Li (Columbia) Statistical Properties of Maximum Mean Discrepancy with Gaussian Kernels
VEC 1302/1303 New methods for directed acyclic Gaussian graph and adaptive data analysis
(organized by Yichao Wu (UIC) and chaired by Mo, Weibin(UNC))
Lev Reyzin (UIC) Sublinear-Time Adaptive Data Analysis
Xiaotong Shen (U Minnesota) Reconstruction of a directed acyclic Gaussian graph for observational and interventional data
Yunzhang Zhu (OSU) Convex clustering over an undirected graph
VEC 1402 Statistical Inference in Clustering Problems
(organized and chaired by Jacob Bien (Cornell))
Max G’Sell (CMU) Inference for variable clustering under correlation-like similarities
Gourab Mukherjee (USC) Large scale cluster analysis via L1 fusion penalization
Yen-Chi Chen (UW) Density Tree and Density Ranking in Singular Measures
VEC 1403 Statistical methods of integrating -omics data
(organized by Ying Wei (columbia) and chaired by Xiaoyu Song (Mount Sinai))
Gen Li (Columbia) A Statistical Framework for Leveraging Information across Multiple Traits in Genetic Studies
Pei Wang (Icahn School of Medicine at Mount Sinai) A new method to study the change of miRNA¨CmRNA interactions due to environmental exposures
Peter Song(Umich) smFARM: sparse multivariate Factor Analysis Regression Model in integrative genomics analysis
10:30-11am Coffee Break – VEC Lobby
11:00am-12:30pm Parallel Sessions
VEC 404 Recent Advances in Statistical Learning
(organized by Ming Yuan (Columbia) and chaired by Dong Xia (columbia))
Jason Lee (USC) Geometry of Optimization Landscapes and Implicit Regularization of Optimization Algorithms.
Aurelie Lozano (IBM) M-estimation with the Trimmed L1 penalty
Sofia Olhede (UCL) Methods of network comparison
VEC 405 Supervised and unsupervised learning of complex data
(organized and chaired by Junhui Wang (Citi U of HK))
Yongzhao Shao (NYU) Systems of partially linear models with gradient boosting
Yoonkyung Lee (OSU) Supervised Dimensionality Reduction for Exponential Family Data
Yuan Zhang (OSU) Transform-based unsupervised point registration and unseeded low-rank graph matching
VEC 902 Advances in estimation and prediction for understanding complex disorders
(organized by Heping Zhang (Yale) and chaired by Narisetty, Naveen (UIUC))
Min-ge Xie (Rutgers) Uncertainty Quantification of Treatment Regime in Precision Medicine by Confidence Distributions
Yanyuan Ma (Penn State) Semiparametric Estimation in the Secondary Analysis of Case-Control Studies
Ying Wei (Columbia) Quantile Decision Trees and Forest with its application for predicting the risk (Post-Traumatic Stress Disorder) PTSD after experienced an acute coronary syndrome
VEC 903 Survival analysis with high-dimensional data
(organized by Ingrid Van Keilegom (KU Leuven) and chaired by Ricardo Cao (Universidade da Coruña))
Lan Wang (U Minnesota) Robust optimal treatment regime estimation with survival outcome
Jelena Bradic (University of California, San Diego) Fine-Gray Competing Risks Model with High-Dimensional Covariates: Estimation and Inference?
Yue Zhao (KU Leuven) Envelopes for censored quantile regression
Hammer LL109 A/B Recent advances of high-dimensional statistical learning
(organized and chaired by Xiaotong Shen (U of Minnesota))
Helen Zhang (Arizona State University) Multiclass Probability Estimation with Support Vector Machines
Hui Jiang (UMich) Minimizing Sum of Truncated Convex Functions and Its Applications
Peng Wang (University of Cincinnati) Uncertainty and Inference for High-Dimensional Models Using the Solution Paths
12:30-1:45pm Lunch Break
1:45-3:15pm Parallel Sessions
VEC 404 Modern Multivariate Statistics: Tensors and Networks
(organized and chaired by Jacob Bien (Cornell))
Dong Xia (columbia) Computationally Efficient Tensor Completion with Statistical Optimality
Peter Hoff (Duke) Structured shrinkage of tensor parameters
Dave Choi (CMU) Global Spectral Clustering for Dynamic Networks
VEC 405 Flexible Statistical Learning and Inference
(organized by Yufeng Liu (UNC) and chaired by Siliang Gong (UNC))
Min Jin Ha (MD Anderson) Multi-layered Graphical Models
Fei Xue (UIUC) Variable Selection for Highly Correlated Predictors
Xingye Qiao (SUNY Binghamton) Support Vector Machine with Confidence
VEC 902/903 Philosophy of Science and the New Paradigm of Data-Driven Science
(organized and chaired by Todd Kuffner (Washington U))
Deborah Mayo (Virginia Tech) Your Data-Driven Claims Must Still be Probed Severely
Ian McKeague (Columbia) On the replicability of scientific studies
Xiao-Li Meng (Harvard) Conducting Highly Principled Data Science: A Statistician’s Job and Joy
VEC 1202/1203 Advances in Bayesian methods for high-dimensional data
(organized by Howard Bondell (U. of Melbourne) and chaired by Xuan Bi (Yale))
Anindya Bhadra (Purdue) The Graphical Horseshoe Estimator for Inverse Covariance Matrices
Anirban Bhattacharya (Texas A & M) Scalable MCMC for Bayes shrinkage priors
Marianthi Markatou (U. at Buffalo) Clustering on the Sphere: State-of-the-art and a Poisson Kernel-Based Model
VEC 1302/1303 High-dimensional machine learning methods
(organized and chaired by Annie Qu (UIUC)
Yuguo Chen (UIUC) Latent Space Approaches to Community Detection in Dynamic Networks
Taps Maiti (MSU) Classification for High-Dimensional Functional Data
Naveen Narisetty (UIUC) A unified approach for censored quantile regression
VEC 1402 /1403 Recent development of Statistical Neuroimaging Analysis
(organized by Lexin Li (UC Berkeley) and chaired by Jason Lee (USC))
Ali Shojaie (U of Washington) Analyzing Non-Stationary High-Dimensional Time Series: Structural Break Detection and Parameter Estimation
Eric Lock (U of Minnesota) Tensor-on-tensor regression
Bei Jiang (University of Alberta) A Joint Modeling Approach for Baseline Matrix-valued Imaging Data and Treatment Outcome
3:15-3:45pm Coffee Break – VEC Lobby
3:45-4:45pm Keynote Speech
VEC 201 Auditorium Michael I. Jordan (University of California, Berkeley)- On Gradient-Based Optimization: Accelerated, Stochastic and Nonconvex
Chaired by Annie Qu (UIUC)
5:00 – 6:30pm Students/Post-doc Mixer
VEC 404/405
Tuesday, June 5th
7:30-8:30am Registration & Continental Breakfast
VEC Lobby
8:30-10am Parallel Sessions
VEC 405 Nonparameteric and Robust Statistical Methods for Imaging
(organized by Hernando Ombao (KAUST) and chaired by Wei Pan (U of Minnesota)
Mehdi Maadooliat (Marquette University) Nonparametric Collective Spectral Density Estimation and Clustering with Application to Brian Activities
Zhaoxia Yu (UC Irvine) A Flexible Non-parametric Framework for Imaging Genetics
Damla Senturk (UCLA) Hybrid Principal Components Analysis For Region-Referenced Longitudinal Functional EEG Data
VEC 1202 /1203 Big Data of different forms and different challenges
(organized by Regina Liu (Rutgers) and chaired by Xuan Bi (Yale))
Annie Qu (UIUC) Individualized Multilayer Learning with An Application in Breast Cancer Imaging
Catherine Chunling Liu (Polytech U of HK) Efficient estimation and fast algorithms for genetic microarray data with survival outcomes
Ricardo Cao (Universidade da Coruña) Nonparametric mean estimation for big-but-biased data
VEC 1302 OODA: Manifold Data Integration
(organized by Marron, James Stephen (UNC) and chaired by Anna Smith (Columbia))
Piercesare Secchi (Politecnico di Milano) Random Domain Decomposition for Kriging Riemannian Data
Ruiyi Zhang(Florida State) Nonparametric K-Sample Test on Riemannian Manifolds with Applications to Analyzing Mitochondrial Shapes
Chao Huang (UNC) High-Dimensional Manifold Data Clustering on Symmetric Spaces
VEC 1303 Advances in high-dimensional statistics
(organized and chaired by Genevera Allen (Rice))
Yufeng Liu (UNC) Adaptive local estimation for high dimensional linear models
Jacob Bien (Cornell) Are Clusterings of Multiple Data Views Independent?
Sijian Wang (Rutgers) Regularized Robust Buckley-James method for AFT Model with General Loss Function
VEC 1402 Causal Inference and Machine Learning
(organized by Ryan Tibshirani (CMU) and chaired by Vincent Joseph Dorie (Columbia)       )
Edward Kennedy (CMU) Nonparametric causal effects based on incremental propensity score interventions
Stefan Wager (Stanford) Quasi-Oracle Estimation of Heterogeneous Causal Effects
Yu-Xiang Wang (Amazon/UCSB) Off-policy Learning in Theory and in the Wild
VEC 1403 Decision making, operations research and statistical learning
(organized and chaired by Cynthia Rudin (Duke))
Theja Tulabandhula (UIC) Online Learning of Buyer Behavior under Realistic Pricing Restrictions
Adam Elmachtoub (Columbia) Smart “Predict, then Optimize”
Brian Segal (Flatiron Health) P-splines with an l1 penalty for repeated measures
10:00 – 10:30am Coffee Break – VEC Lobby
10:30am-11:30pm Keynote Speech
VEC 401 multiple purpose room David Madigan (Columbia University)- Honest learning for the healthcare system: large-scale evidence from real-world data
chaired by Tian Zheng (Columbia)
1:15-2:45pm Parallel Sessions
VEC 1202/1203 Novel inference approaches for complex data setting
(organized by Regina Liu (Rutgers) and chaired by Junhui Wang (City U. of Hong Kong))
Emre Barut (George Washington University) Stein Discrepancy Methods for Robust Estimation and Regression
Harry Crane (Rutgers) Toward a sampling theory for statistical network analysis
Aurore Delaigle (U of Melbourne) Estimating a covariance function from fragments of functional data
VEC 1302 New development for analyzing biomedical complex data
(organized by Zhezhen Jin (Columbia) and chaired by Peng Wang (University of Cincinnati))
Xiaonan Xue (Albert Einstein College of Medicine) New methods for estimating follow-up rates in cohort studies
Mengling Liu (New York University) Mediation analysis with time-to-event mediator
Tao Wang (Albert Einstein College of Medicine) Adjustment for covariates in genome-wide association study
VEC 1303 New Statistical Machine Learning Tools
(organized and chaired by Liu, Yufeng (UNC))
Genevera Allen (Rice) Inference, Computation, and Visualization for Convex Clustering and Biclustering
Guan Yu (SUNY Buffalo) High-dimensional Cost-constrained Regression via Non-convex Optimization
Heping Zhang (Yale) Modeling Hybrid Traits for Comorbidity and Genetic Studies of Alcohol and Nicotine Co-Dependence
VEC 1402 Functional Data Analysis in Action
(organized and chaired by Kehui Chen (U of Pitt))
Jane-Ling Wang (UC Davis) Brain Functional Connectivity — The FDA Approach
Daniel Gervini (U of Wisconsin at Milwaukee) Functional Data Methods for Replicated Point Processes
Hans Mueller (UC Davis) Frechet Regression for Time-Varying Covariance Matrices: Assessing Regional Co-Evolution in the Developing Brain
VEC 1403 Statistical Learning and Genomics
(organized by Ji Zhu (Umich) and chaired by Bing Li (Penn State))
Umut Ozbek (Mount Sinai) Proteomics and Genomics Integration for Translational Cancer Research
Xiaoyu Song (MSSM) What can we gain from proteogenomics prediction: The downstream analysis of NCI-CPTAC Proteogenomics DREAM Challenge
Wei Pan (U of Minnesota) An empirical comparison of deep neural networks and other supervised learning methods
2:45-3:15pm Coffee Break – VEC Lobby
3:15-4:45pm Parallel Sessions
VEC 1202 Recent advances in high-dimensional data
(organized by Cunhui Zhang (Rutgers) and chaired by Sijian Wang (Rutgers))
Pierre Bellec (Rutgers) The noise barrier and the large signal bias of the Lasso and other convex estimators
Yuan Liao (Rutgers) Factor-Driven Two-Regime Regression
Jiashun Jin (CMU) Network Analysis by SCORE
VEC 1203 Interpretable modeling and understanding variables
(organized and chaired by Cynthia Rudin (Duke))
Aaron Fisher (Harvard) Model Class Reliance: Variable Importance when all Models are Wrong, but *Many* are Useful.
Tong Wang (U Iowa) Feature-Efficient Multi-value Rule Sets for Interpretable Classification
Cynthia Rudin (Duke) Recent Work on Interpretable Machine Learning Models
VEC 1302 Statistical Inference for High-Dimensional Data
(organized and chaired by Jeff Simonoff (NYU))
Xi Chen (NYU) Quantile Regression for big data with small memory
Joshua Loftus (NYU) Inference after cross-validation
Yihong Wu (Yale) Optimal estimation of Gaussian mixtures via denoised method of moments
VEC 1303 New Development on Neuroimage Data Analysis
(organized by Zhu, Hongtu (MD Anderson) and chaired by Xuan Bi (Yale))
Tingting Zhang (UVA) A Low-Rank Multivariate General Linear Model forMulti-Subject fMRI Data and a Non-Convex Optimization Algorithm for Brain Response
Zhengwu Zhang (Rochester) Nonparametric Bayes Models of Fiber Curves Connecting Brain Regions
Dehan Kong (U Toronto) Supervised Principal Component Regression for Functional Data with High Dimensional Predictors
VEC 1402 Spectral Clustering, Graphical Models, and Hierarchical Interactions
(organized by Lingzhou Xue (Penn State) and chaired by Kuang-Yao Lee (Temple))
Hongyu Zhao (Yale) Spectral clustering based on learning similarity matrix
Bing Li (Penn State) Copula Gaussian Graphical Models for Functional Data
Lingzhou Xue (Penn State) Learning Nonconvex Hierarchical Interactions
VEC 1403 Data Science in IT Industries
(organized by David Banks (Duke) and chaired by Genevera Allen (Rice))
Julie Novak (Netflix) Using Data Science to Improve Streaming Quality at Netflix
Tim Au (Google) Random Forests, Decision Trees, and Categorical Predictors: The “Absent Levels”” Problem”
David Banks (Duke University and SAMSI) The Challenge of Educating Data Scientists for Industry
5:00-6:30pm Poster Session
VEC 401 multiple purpose room
6:30-10pm BANQUET – Faculty House Presidential Ballroom (Pres123R)
Speaker: Cathy O’Neil
Host: Cynthia Rudin (Duke)
Wednesday, June 6th
7:30-8:30am Registration & Continental Breakfast
VEC Lobby
8:30-10am Parallel Sessions
VEC 404 /405 Machine Learning and Precision Medicine
(organized and chaired by Haoda Fu (Eli Lilly))
Donglin Zeng (UNC) Support vector machines for learning optimal individualized treatment rules with multiple treatments
Haoda Fu (Eli Lilly) Individualized Treatment Recommendation (ITR) for Survival Outcomes
Yuanjia Wang (Columbia) Estimation and Evaluation of Linear Individualized Treatment Rules to Guarantee Performance
VEC 902/903 Advances in Nonparametric Statistics and their Applications
(organized by Narisetty, Naveen (UIUC) and chaired by Fei Xue (UIUC))
Roger Koenker (UIUC and UCL) Fly-By-Night Life Insurance and the NPMLE for Weibull Frailty Models
Christopher Kinson (UIUC ) Learning from Dr. Martin Luther King Jr: Text analysis and statistical approaches for civil rights
Stanislav Volgushev (U Toronto) Inference on the dependence structure of time series extremes
VEC 1202/1203 Recent advances in spectral methods for complex data
(organized by Yuekai Sun (UMich) and chaired by Edgar Dobriban (Upenn))
Geoff Schiebinger (MIT) Analyzing Developmental Processes with Optimal Transport
Edgar Dobriban (Wharton) How to select the number of components in PCA and factor analysis? Understanding and improving permutation methods
Austin Benson (Cornell) Higher-order spectral graph clustering with motifs
VEC 1302 New machine learning methods
(organized and chaired by Annie Qu (UIUC))
Quoc Tran-Dinh (UNC) Generalized self-concordant optimization and its applications in statistical learning
Yixin Fang (New Jersey Institute of Technology) On Scalable Inference with Stochastic Gradient Descent
Junhui Wang (City U. of Hong Kong) Scalable Kernel-based Variable Selection with Sparsistency
VEC 1402 New directions in functional data analysis.
(organized by Tailen Hsing ( UMich) and chaired by Vincent Joseph Dorie (Columbia) )
Kehui Chen (U of Pitt) Nonparametric covariance estimation for mixed longitudinal studies
Matthew Reimherr (Penn State) Functional Data Analysis with Highly Irregular Designs with Applications to Head Circumference Growth
Hao Ni (UCL) Supervised Learning on the Path Space and its Applications
10:00 – 10:30am Coffee Break – VEC Lobby
10:30am-11:30pm Keynote Speech
VEC 401 multiple purpose room Liza Levina (University of Michigan) – Matrix completion in network analysis
Chair: Ying Wei (Columbia)
1:15-2:45pm Parallel Sessions
VEC 404/405 Modern Approaches for Inference and Estimation
(organized and chaired by Genevera Allen (Rice))
Yang Ning (Cornell) High-Dimensional Propensity Score Estimation via Covariate Balancing
Gautam Dasarthy (Rice University) Interactive algorithms for graphical model selection
Will Fithian (UCB) AdaPT: An interactive procedure for multiple testing with side information
VEC 902 Functional and high dimensional data
(organized and chaired by Aurore Delaigle (U of Melbourne))
Emad Abdurasul (James Madison University) Small Sample Confidence Intervals for the ACL (Abduskhurov, Cheng, and Lin) Estimators Under the Proportional Hazards Model
Sophie Dabo-Niang (Université Lille 3) Binary functional linear models in a stratified sampling setting
Patrice Bertail (Université Paris Nanterre) Functional CLT and sharp bounds for some (conditional Poisson) survey sampling plans with applications to big (tall) data
VEC 1202 /1203 Machine learning, classification and designs
(organized and chaired by Annie Qu (UIUC))
Ying Hung (Rutgers) Efficient Gaussian Process Modeling using Experimental Design-based Subagging
Irina Gaynanova (Texas A&M) Structural Learning and Integrative Decomposition of Multi-View Data
Adam Rothman (U. of Minnesota) Shrinking characteristics of precision matrix estimators
VEC 1302 /1303 Statistics in neuroscience and microbiome research at the Flatiron Institute
(organized and chaired by Christian L. Müller (Flatiron Institute, Simons Foundation))
Cengiz Pehlevan (Simons Foundation) Neural representation learning as kernel alignment
Aditya Mishra (Flatiron Institute) Robust regression with compositional covariates
Eftychios Pnevmatikakis (Simons Foundation) Online deconvolution and demixing of calcium imaging data in real time
VEC 1402 /1403 Recent Advances in Statistical Network, Functional and High-dimensional Data Analysis
(organized by Ji Zhu (Umich) and chaired by Yujia Deng (UIUC))
George Michailidis (U of Florida) Factor Augmented Vector Autoregressive Models under High
Edoardo Airoldi (Harvard) Model-assisted design of experiments on networks and social media platforms
Gareth James (USC) Correcting Selection Bias via Functional Empirical Bayes
2:45-3:15pm Coffee Break – VEC Lobby
3:15-4:45pm Parallel Sessions
VEC 404/405 New insights into classical statistical methods
(organized and chaired by Qing Mai (Florida State U))
Yiyuan She (Florida State U) Rank-constrained inherent clustering paradigm for supervised and unsupervised learning
Yun Yang (Florida State U) Fast and Optimal Bayesian Inference via Variational Approximations
Xin Zhang (Florida State U) An Iterative Penalized Least Squares Approach to Sparse Canonical Correlation Analysis
VEC 902 New developments for large complex data
(organized and chaired by Annie Qu (UIUC))
Jiwei Zhao (SUNY, Buffalo) Point and Interval Estimations for Individualized MCID
Doug Simpson (UIUC) Robust Probabilistic Classification for Irregularly Sampled Functional Data
Francesca Petralia (Mount Sinai) A new method for constructing gene co-expression networks based on samples with tumor purity heterogeneity
VEC 1302 /1303 Statistical inference and complex data structures
(organized by Eric Laber (NCSU) and chaired by Yubai Yuan (UIUC))
Kristin Linn (UPenn) Inter-modal Coupling: A Class of Measurements for Studying Local Covariance Patterns Among Multiple Imaging Modalities
Jeff Goldsmith (Columbia University) Modeling Heterogeneity in Motor Learning using Heteroskedastic Functional Principal Components
Yichi Zhang (Harvard) Prior Adaptive Semi-supervised Learning with Application to Electronic Health Records Phenotyping
VEC 1402 /1403 Causal inference and statistical learning
(organized and chaired by Cynthia Rudin (Duke))
Chris Wiggins (Columbia & NY Times) Teaching History and Ethics of Data, with Python
Ben Letham (Facebook data science) Bayesian optimization and A/B tests
Alex Volfovsky (Duke) Causal inference from complex observational data