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 | |