June 2nd - Dr. Alireza Aghasi, Oregon State University (EECS)

May 19th - Dr. Michael Sohn, University of Rochester

Optimal Approaches to the Compositionality and High-Sparsity of Microbiome Data

Over the past decade, the US National Institutes of Health has invested more than a billion dollars to human microbiome research, underscoring the crucial role the microbiome plays in our health and well-being. Despite these substantial efforts, many of the groundbreaking discoveries have faced challenges in being applied effectively in clinical practice. One major hurdle is the difficulty in reproducing the results related to specific microbes (or taxa) that are linked to various diseases. One key factor contributing to this irreproducibility is the improper treatment of the compositional and sparse nature of microbiome data during statistical analysis. In this presentation, I will first outline the unique characteristics of microbiome data and illustrate the consequences of ignoring these factors. I will then introduce an optimal normalization method to address the compositional effects, built on the minimal assumption required to extract meaningful absolute information from relative data. Additionally, I will discuss why treating all zeros as missing values tends to lead to better outcomes than the common practice of replacing zeros with a small constant (e.g., 0.5), and then I will present a multiple imputation method specifically designed for high-dimensional, sparse compositional data. The optimality and validity of the proposed methods, along with their beneficial effects on downstream analyses, are demonstrated through extensive simulation studies. I will also illustrate their application to real microbiome datasets.

May 12th - Dr. Jun Chen, Mayo Clinic

Microbiome Association Analysis - Methods, Tools and Study Design

The human microbiome has become an integral component of modern genomics research. The central theme of human microbiome research is to decipher the complex interplay between environmental factors, microbial communities, and host biology. To unravel these interactions, robust and powerful statistical tools are essential. In this talk, I will introduce two new computationally efficient approaches, D-MANOVA and LinDA, for microbiome association analyses. D-MANOVA enables community-level testing to establish an overall association, while LinDA pinpoints the specific taxa driving the overall association. Building upon these tools, I will present a simulation-based power analysis framework to guide the design of microbiome studies.

May 5th - Dr. Deepak Kadetotad, Starkey Hearing

Research vs. Reality - Challenges in Real-Time ML based Fixed-Point processing in Hearing Aids

With the proliferation of machine learning into the hearing aid industry, many research papers try to achieve the state-of-the-art results with measured metrics like Signal to Noise Improvement (SNRi), Short-Time Objective Intelligibility (STOI), etc. but end up missing the forest for the trees by neglecting crucial constraints of Hearing Aids (HAs) that tend to leave the research unusable by industry. The talk aims to highlight certain key constraints of HAs that can be generalized to other low power embedded devices. The goal is to inspire a parsimonious integration of these constraints into the design of networks, system architectures and algorithms, thereby facilitating real-world impacts of your research.

May 1st - Dr. Xiaohui Chang, Oregon State University (College of Business)

Dynamic Methods for Calibrating Numerical Model Outputs

Numerical air quality models are pivotal for forecasting and assessing air pollution, but their outputs may be systematically biased. In this study, we propose several hierarchical dynamic models to calibrate large-scale numerical model outputs using supplementary data sources. At deeper levels of our models, we employ stochastic integro-differential equations to characterize the dynamic evolution of spatial random effects. To accelerate computation, we adopt techniques such as the ensemble Kalman smoother and variational Bayes. For statistical inference, we apply a conditional simulation technique to quantify the uncertainty of parameter estimates and calibration results. We demonstrate our approach by calibrating real-world PM2.5​ outputs from the Community Multiscale Air Quality (CMAQ) system for China’s Beijing-Tianjin-Hebei region. Results show that our models produce more accurate calibrations than competing methods across different performance metrics while achieving higher computational efficiency.

April 21st - Dr. Ting Zhang, University of Georgia

High-quantile regression for tail-dependent time series

Quantile regression is a popular and powerful method for studying the effect of regressors on quantiles of a response distribution. However, existing results on quantile regression were mainly developed for cases in which the quantile level is fixed, and the data are often assumed to be independent. Motivated by recent applications, we consider the situation where (i) the quantile level is not fixed and can grow with the sample size to capture the tail phenomena, and (ii) the data are no longer independent, but collected as a time series that can exhibit serial dependence in both tail and non-tail regions. To study the asymptotic theory for high-quantile regression estimators in the time series setting, we introduce a tail adversarial stability condition, which had not previously been described, and show that it leads to an interpretable and convenient framework for obtaining limit theorems for time series that exhibit serial dependence in the tail region, but are not necessarily strongly mixing. Numerical experiments are conducted to illustrate the effect of tail dependence on high-quantile regression estimators, for which simply ignoring the tail dependence may yield misleading p-values.

April 14^th - Dr. Lynn LaMotte, Louisiana State University

ANOVA at 100: Its Influence, Quandaries, and a Resolution

R. A. Fisher formalized Analysis of Variance – ANOVA – in Statistical Methods for Research Workers, first published in 1925. Within a decade, it became widely taught, accepted, and applied in agriculture, economics, engineering, social sciences, and other scientific disciplines. ANOVA spawned terms like additive, interaction, fixed, and random factor effects. It was the primary motivation to broaden linear models from full-rank multiple regression models to more general overparameterized non-full-rank models, which required dealing with the notion of estimability. Its influence on the broad subject of linear models, on the teaching of statistical methods, and on the practice of statistics has been immense. In the first lecture I intend to relate some of the developments of ANOVA over time, along with a few of the personalities responsible. This will include formulation of ANOVA in balanced models and the complications that result from the least bit of unbalanced-ness. The second lecture will deal mainly with the question, how (or whether) to construct a test statistic for lower-level factor effects (e.g., main effects) in models that permit higher-level (e.g., interaction) effects. Such tests are done routinely and mostly without controversy in balanced models. Why has it seemed difficult and controversial in unbalanced models?

March 3^rd - Dr. Yang Chen, University of Michigan

Statistical Machine Learning Enabled Scientific Discovery – Case Studies in Space Weather Forecasting

In recent years, machine learning approaches have become increasingly popular in space weather forecasting with the increasing data collection and sharing capabilities of satellite data. Studies have shown that machine learning approaches can offer significantly higher accuracy for forecasting extreme space weather events such as solar flares and geomagnetic storms. However, the “proven” success of data-driven approaches raises various concerns from scientists: the interpretability, generalizability, stability, ignorance of known physics principles, and reproducibility of the fitted machine learning model are often the center of debates. Statistical principles provide valuable insights into the roots of the successes and failures of data-driven prediction models, thus of utmost importance. However, most classical statistical models suffer from the simplicity of model specifications and stringent regularity conditions, which do not apply in practice. We have shown through multiple studies that by embedding machine learning components into rigorous statistical models, we can leverage the strengths of (i) highly interpretable statistical models with calibrated uncertainty estimates, (ii) the flexibility of deep learning models, and (iii) modern computational infrastructures. In this talk, I will discuss a few case studies where we propose novel statistical machine-learning approaches in space weather forecasting.

January 13^th - Dr. Michael Dumelle, US Environmental Protection Agency

Introducing spatial statistics using the spmodel R package

Abstract: In scientific disciplines like ecology and environmental science, spatial data (i.e., data that are distributed in space) are common. For spatial data, statistical models incorporating spatial dependence (i.e., spatial models) tend to be more realistic than statistical models ignoring spatial dependence (i.e., nonspatial models). Recent software advances in R's spatial data ecosystem have made spatial models much more accessible to practitioners. Here we focus on the spmodel R package (https://usepa.github.io/spmodel/), which fits, summarizes, and makes predictions for a variety of spatial models. We discuss three reasons why spmodel is an effective tool for introducing (and teaching) spatial statistics: First, spmodel uses a syntactic structure similar to that of familiar base R functions like lm() and glm(); Second, spmodel provides a wide breadth of options that give users a high amount of control over the model being fit; And third, spmodel is compatible with other modern R packages like sf, broom, and emmeans.

February 24^th - Dr. James Molyneux, Swyfft,

Oregon State University (Statistics adjunct professor; Food Science & Technology courtesy graduate faculty)

There and Back Again: Tales of a Former Academic

Abstract: A former academic leaves the Academy and heads off into the wild west of industry. Here, we’ll share tales of how the transition from tenure-track researcher to data scientist was accomplished, discuss the trade offs between the two career paths, and learn that there’s still opportunities to solve stimulating problems outside of the Ivory Tower. We’ll offer tips (or at least bad advice) for folks interested in data science roles and biased opinions about what data science curricula might look like within the confines of a statistics department. Plus, as many interesting stories as can be told while also not breaking any non-disclosure agreements.

February 20^th - Department Event

Info will be emailed to department members

February 19^th - Department Event

Info will be emailed to department members

February 12^th - Department Event

Info will be emailed to department members

February 10^th - Department Event

Info will be emailed to department members

February 6^th - Department Event

Info will be emailed to department members

February 3^rd - Department Event

Info will be emailed to department members

October 7^th - Jessica Li, University of California Los Angeles

Dissecting Double Dipping in Statistical Tests After Clustering

Abstract: Motivated by the widespread use of clustering followed by statistical testing in single-cell and spatial omics data analysis, this talk will address the issue of double dipping. We aim to explore whether double dipping is a significant concern and investigate how various data-splitting and data-simulation strategies can mitigate its impact on inflated false discovery rates (FDR). We will also discuss different perspectives on whether the inference should be conditional on the clustering step or not. In particular, we will highlight the influence of feature correlations on FDR inflation. Through simulation and real-data examples, we will demonstrate how our simulation-based strategy for correcting double dipping can lead to more reliable and insightful discoveries.

October 14^th - Hui Zou, University of Minnesota

Box-Cox Regression Revisited

Abstract: Box-Cox regression (1964, JRSSB) is a must-taught topic in any applied regression course. However, the Box-Cox model has received mixed views in the statistical community. While some enthusiastically embraced the idea, others had raised serious concerns. For example, Bickel and Doksum (1981, JASA) analyzed the MLE for the Box-Cox model and showed that the unknown power transformation causes high instability. Despite these criticisms, we believe the core concept of Box-Cox model is valid and powerful for developing new statistical tools. In this work we consider using a nonparametric transformation to replace the parametric Box-Cox transformation in the model, yielding practically more useful models. Moreover, we focus on the high-dimensional application of the new Box-Cox model, aiming at improving the standard practice of using penalized least squares. We develop a composite likelihood inference framework, which avoids the need of estimating the transformation function, for estimating the regression coefficients and for testing linear hypotheses. We will use a supermarket data example to illustrate the new method and theory.

October 21^st - Yimin Xiao, Michigan State University

Multivariate Gaussian Random Fields and their Statistical Analysis

Abstract: In recent years, a number of classes of new multivariate random fields have been constructed by using the approaches of covariance matrices, variogram matrices, the convolution method, spectral representations, or systems of stochastic partial differential equations (SPDEs), and have been applied for modeling multivariate spatial data. However, the theoretical development of parameter estimation, prediction, and extreme values for multivariate random fields is still under-developed and the range of their applications is growing constantly. In this talk, we provide an overview on several classes of multivariate Gaussian random fields including multivariate Mat´ern Gaussian fields, operator fractional Brownian motion, and matrix-valued Gaussian random fields, and some recent results on estimation and prediction of bivariate Gaussian random fields. These results illustrate explicitly the effects of the dependence structures among the coordinate processes on statistical analysis of multivariate Gaussian random fields.

October 28^th - Dongseok Choi, Oregon Health & Science University

Deep Learning Applications for Two Medical Imaging Data

Abstract: Convolution neural network (CNN) is a deep learning specialist for imaging data. Big data is generally required for training of CNN. However, in typical medical studies, sample sizes are at most 100s. Would CNN work for such small sample sizes? This talk presents the results of applying CNN to two small to medium medical studies using R and related packages. In the first study, CNN was trained to classify X-ray images for diffuse idiopathic skeletal hyperostosis (DISH). The second study used hybrid deep learning to predict glaucoma with optical coherence tomography (OCT) images and clinical data.

November 4^th - Jun Zhu, University of California Los Angeles

Spatial Cluster Detection and Change-set Analysis

Abstract: For the purpose of grouping spatial units on a lattice with similar characteristics within a group but with distinction among groups, we consider spatial cluster detection and change-set analysis. While the existing methods for spatial cluster detection are largely based on hypothesis testing or Bayesian models, we consider an alternative frequentist approach using regularization. In addition, we develop a change-set method for two-dimensional spatial data that permit more abrupt changes in space and irregular change sets. A quasi-likelihood approach is taken for statistical inference that accounts for covariates and spatial correlation. Finite-sample properties are investigated in a simulation study and the methods are applied to analyze county-based poverty rates in the Upper Midwest.

November 18^th - Steve Portnoy, Portland State University

Canonical Regression Quantiles: A Regression Quantile Approach to Canonical Correlations

Abstract: Canonical Correlations are often used to relate two sets of variables modelled as multivariate vectors X and Y. The regression approach seeks to find linear combinations of X's that predict linear combinations of Y's in some optimal sense. The classical approach uses the covariance matrix of and so relies heavily on normal assumptions and implicit least squares methods. Thus, canonical correlations lack robustness, are unable to address heterogeneity, and are unable to disaggregate responses by quantile effects. An alternative canonical regression quantile (CanRQ) approach seeks to find the linear combination of explanatory variables that best predicts the best linear combination of response variables using a quantile loss function. To apply this approach more generally, subsequent linear combinations are chosen to explain what earlier CanRQ components failed to explain. While numerous technical issues need to be addressed, the major methodological issue concerns directionality: a quantile analysis requires that the notion of a large or small response be well-defined. To address this issue, the sign of at least one response coefficient will be assumed to be non-negative. CanRQ results can be quite different from those of classical canonical correlation, and can offer the kind of improvements offered by regression quantiles in linear models. In theory, inference can be based on the n-choose-m bootstrap. Some new simulations and examples are quite promising.

November 25^th - Erik Van Dusen, University of California Berkeley

Abstract: Jupyter Notebooks have revolutionized how we teach data science, providing an interactive and accessible platform for learning computational tools and analytical reasoning. In this talk, Eric Van Dusen will explore how Jupyter Notebooks have been integral to the development of UC Berkeley’s Data Science Undergraduate Studies programs. Key initiatives include the interdisciplinary Data 8 course, which anchors the Data Science major and minor, the Data Science Modules Program that integrates data literacy into diverse disciplines, and applications in teaching Data Science and Economics. The talk will showcase strategies for engaging students across different levels, fostering equity in technical education, and preparing them to apply data science to real-world problems. Practical examples and lessons learned will highlight how Jupyter Notebooks empower educators and students alike.

October 3 – Jessica (Jingyi) Li, Department of Statistics, UCLA

Abstract: The rapid development of genomics technologies has propelled fast advances in genomics data science. While new computational algorithms have been continuously developed to address cutting-edge biomedical questions, a critical but largely overlooked aspect is the statistical rigor. In this talk, I will introduce our recent work that aims to enhance statistical rigor by addressing three issues:

1. Large-scale feature screening (i.e., enrichment and differential analysis of high-throughput data) relying on ill-posed p-values;
2. Double-dipping (i.e., statistical inference on biasedly altered data);
3. Gaps between black-box generative models and statistical inference.

October 10 – Luca Mazzucato, Department of Mathematics and Biology, University of Oregon

Abstract: Animal behavior exhibits a striking amount of variability in the temporal domain along at least three independent axes: hierarchical, contextual, and stochastic. First, a vast hierarchy of timescales links movements into behavioral sequences and long-term activities, from milliseconds to minutes. Second, action timing can be modulated by changes in context, of either internal (neuromodulatory, state-dependent) or external origin. Third, self-initiated actions exhibit large residual variability across repetitions, with signatures of stochastic origin. What computational principles underlie such complex temporal features? We will present the foundation of a theory of temporal variability in behavior and neural activity, based on metastable attractors observed in sensory and motor cortical areas. We will highlight the essential role played by intrinsic noise and heterogeneities in controlling the features of temporal variability.

October 17 – Hao Chen, Department of Statistics, UC Davis

Abstract: After observing snapshots of a network, can we tell if there has been a change in dynamics? After collecting spiking activities of thousands of neurons in the brain, how shall we extract meaningful information from the recording? We introduce a change-point analysis framework utilizing graphs representing the similarity among observations. This approach is non-parametric and can be applied to data when an informative similarity measure can be defined. Analytic approximations to the significance of the test statistics are derived to make the method fast applicable to long sequences. The method is illustrated through the analysis of the Neuropixels data.

October 24 – Ali Shojaie, Department of Biostatistics, University of Washington, Seattle

Abstract: Recent evidence suggests that changes in biological networks, e.g., rewiring or disruption of key interactions, may be associated with development of complex diseases. These findings have motivated new research in computational and experimental biology that aim to obtain condition-specific estimates of biological networks, e.g. for normal and tumor samples, and identify differential patterns of connectivity in such networks, known as differential network analysis. In this talk, we primarily focus on testing whether two Gaussian graphical models are the same. We will first illustrate that existing inference procedures for this task may lead to misleading results. To address this shortcoming, we propose a two-step inference framework, for testing the null hypothesis that the edge sets in two networks are the same. The proposed framework is especially appropriate if the goal is to identify nodes or edges that show differential connectivity. Time permitting, we will also discuss how differential network analysis methods can be extended to non-Gaussian settings as well as settings where differences in network edges are functions of other covariates.

October 31 – Dominik Rothenhausler, Department of Statistics, Stanford University

November 7 – Karthika Mohan, EECS Department, Oregon State University

November 14 – Rahul Majumder, Operations Research and Statistics Group, Sloan School of Business, MIT

November 21 – Vijayan Nair, Wells Fargo

November 28 – Ben Brown, Department of Statistics, UC Berkeley, and Computational Biologist, Berkeley Labs

Jan 11th, 4 - 5 p.m. – Katherine McLaughlin, Assistant Professor, Oregon State University, Statistics Dept, Corvallis, OR

Jan 25th, 4 - 5 p.m. – Sharmodeep Bhattacharyya, Assistant Professor, Oregon State University, Statistics Dept, Corvallis, OR

Feb 1st, 4 - 5 p.m. – Jean Opsomer, Vice President & Senior Statistical Fellow, Westat

Feb 8th, 4 - 5 p.m. – Holly Janes, Professor of Vaccine and Infectious Diseases, Fred Hutchinson Cancer Research Center

Feb 15th, 4 - 5 p.m. – Kathi Ivrine, Statistician, U.S. Geological Survey

Feb 22nd, 4 - 5 p.m. – Teri Utlaut, Statistician, Intel Corporation

Mar 1st, 4 - 5 p.m. – John Williamson, Statistician, Center for Disease Control and Prevention

Sep 28th, 4–5 p.m. Orientation, Oregon State University, Statistics Dept

Oct 5th, 1–2 p.m. Tracy Ke, Assistant Professor, Harvard University, Statistics, Dept., Cambridge, MA

Oct 12th, 1–2 p.m. Pixu Shi, Assistant Professor, Duke University, Biostatistics and Bioinformatics, Durham, NC

Oct 19th, 4–5 p.m. Anru Zhang, Assistant Professor, University of Wisconsin at Madison, Statistics Dept., Madison, WI

Oct 28th, 1–2 p.m. Scott Bruce, Assistant Professor, George Mason University, Statistics Dept., Fairfax, VA

Nov 2nd, 4–5 p.m. Ali Shojaie, Professor, University of Washington, Statistics Dept Seattle, WA

Nov 9th, 4–5 p.m. Minge Xie, Professor, Rutgers University, Statistics Dept., New Brunswick, NJ

Nov 16th, 1–2 p.m. Hui Jiang, Associate Professor, University of Michigan, Biostatistics, Ann Arbor, MI

Nov 39th, 4–5 p.m. Maude David, Assistant Professor, Oregon State University, Department of Microbiology, Corvallis, OR

• January 7th – Abel Rodriguez, Ph.D., Professor of Statistics and Associate Dean at Univ. of California, Santa Cruz
• January 14th – Stephanie Hicks, Ph.D., Johns Hopkins University
• January 23rd – Special seminar: Dajiang Liu, Penn State University
• January 28th – Ramon Durazo, Loyola Chicago University
• January 29th – Special Seminar: Aluisio Pinheiro, Ph.D., University of Campinas
• February 4th – Eduardo Cotilla-Sanchez, Ph.D., Associate Professor at Oregon State University's School of Electrical Engineering and Computer Science
• February 11th – Frederick Campbell, Microsoft
• February 18th – Jane-Ling Wang, University of California, Davis
• February 25th – Luis Tenorio, Ph.D., Associate Professor in Applied Mathematics and Statistics at Colorado School of Mines
• March 4th – Luis Leon-Novelo, Texas Health Science Center at Houston
• April 8th – Xinping Cui, Ph.D., Professor and Chair, Department of Statistics, University of California, Riverside, California
• April 15th – Ben Shaby, Ph.D. Assistant Professor, Department of Statistics and the Institute for CyberScience at Penn State University, University Park, Pennsylvania
• April 22nd – Saonli Basu, Ph.D., Associate Professor, Division of Biostatistics at the University of Minnesota, Minneapolis, Minnesota
• April 24th – Lucas Beverlin, Ph.D., Statistician at Intel, Corp, Hillsboro, Oregon
• April 29th – Cindy Yu, Ph.D., Associate Professor, Department of Statistics, Center of Survey Statistics and Methodology at Iowa State University, Ames, Iowa
• May 6th – Alvaro Munoz, Ph.D., Professor, Department of Epidemiology and Methodology at John Hopkins Bloomberg School of Public Health, Baltimore, Maryland
• May 13th – Jackie Hughes-Oliver, Ph.D. Professor, Department of Statistics at North Carolina State University, Raleigh,North Carolina
• May 20th – Daniel Taylor-Rodriguez, Ph.D., Assistant Professor, Fariborz Maseeh Department of Mathematics and Statistics at Portland State University, Portland, Oregon
• September 30 – Orientation, Statistics Department
• October 7th – Amy Willis, Assistant Professor University of Washington, Biostatistics Department, Seattle, Wa
• October 14th – Jayanth Banavar, Professor and Knight Chair of Physics University of Oregon, Physics Department, Eugene, Or
• October 21st – Dongseok Choi, Professor OHSU-PSU School of Public Health, Portland, Or
• October 28th – Lin Xihong, Professor Harvard School of Public Health, Boston, Ma
• November 4th – Wendy Meiring, Professor UC Santa Barbara, Department of Statistics and Applied Probability, Santa Barbara, Ca
• November 18th – Mikyoung Jun, Professor Texas A&M University, Department of Statistics, College Station, TX
• November 25th – Aditya Guntuboyina, Associate Professor University of California, Berkeley, Department of Statistics, Berkeley, CA

• October 1st – Amelia McNamera, Ph.D., Assistant Professor for Department of Computer and Information Sciences at University of St. Thomas
• October 8th – Bryon Aragam, Ph.D., Research Scientist at Carnegie Mellon University
• October 15th – Kathy Li, Assistant Professor of Marketing at McCombs School of Business, University of Texas at Austin
• October 22nd – Kristen Gore, Ph.D., Hewlett Packard, Inc
• October 26th, 2018 (Friday) Debashis Paul, Ph.D., Professor for Department of Statistics at University of California, Davis
• October 29th – Naomi Altman, Ph.D., Professor for Department of Statistics at Penn State
• November 5th – Andrew Bray, Ph.D., Assistant Professor of Mathematics at Reed College

• January 23, 2017: Dr. Craig Johns, Distinguished Data Scientist, Oracle Title: "Causal Inference in Advertising"
• January 30, 2017: Dr. Andrew Zhou, Professor, Department of Biostatistics, University of Washington Director of Biostatistics Unit, Veterans Affairs Puget Sound Health Care System, Associate Director, National Alzheimer’s Coordinating Center (NACC) Title: "Double Robust Estimator of Average Causal Treatment Effect for Censored Medical Cost Data"
• February 6, 2017: Dr. Hongyuan Cao, Assistant Professor, Department of Statistics, University of Missouri Title: "Analysis of asynchronous longitudinal data with partially linear models."
• February 13, 2017: Wanli Zhang, Department of Statistics, Oregon State University Title: "Model-based Clustering with Measurement or Estimation Errors"
• February 20, 2017: Tzu-Chin Wu, Oregon State University Career Development Center Title: "Writing an Effective Resume and/or Cover Letter"
• March 6, 2017: Kristin Luck, Entrepreneur Title: "From Research Assistant to Entrepreneur: Mapping a Data Driven Career Path"
• March 13, 2017: Jianfei (Jeffrey) Zheng, Department of Statistics, Oregon State University Title: "Semi-parametric method for non-ignorable missing in longitudinal data using refreshment samples."

• February 8, 2016: Katherine R. McLaughlin, Dept. of Statistics, University of California, Los Angeles, "Modeling Preferential Recruitment for Respondent-Driven Sampling"
• February 18, 2016: Karl Gregory, Dept. of Statistics, Universität Mannheim, "Pointwise inference in the high-dimensional additive model"
• February 23, 2016: Dehan Kong, "High-dimensional Matrix Linear Regression Model"
• April 4, 2016: Lorenzo Ciannelli, Professor, College of Earth, Ocean, and Atmospheric Sciences, Oregon State University, "Risk and uncertainty quantification in marine science and policy" (Room 218, Covell Hall)
• April 11, 2016: Dan Dalthorp, PhD, USGS Forest and Rangeland Ecosystem Science Center, "Estimation of mortality of legally protected bird and bat species at wind power facilities - Statistical inference when stakes are high and evidence is scarce" (Room 218, Covell Hall)
• April 18, 2016: Jun Li, Department of Applied and Computational Mathematics and Statistics at University of Notre Dame, "Statistical analysis of single-cell RNA-Sequencing data: identifying and removing the cell-cycle effect" (Room 218, Covell Hall)
• April 25, 2016: Lu Wang, PhD candidate, Oregon State University Department of Statistics, "Nonparametric estimation of additive models with shape constraints" (Room 218, Covell Hall)
• May 2, 2016: Nikolay Bliznyuk, Assistant Professor of Statistics, University of Florida, "A Multi-Pathogen Hierarchical Bayesian Spatio-Temporal Model for Transmission of Hand, Foot, and Mouth Disease" (Room 218, Covell Hall)
• May 9, 2016: Sujit Ghosh, Professor of Statistics, NC State University and Deputy Director, SAMSI, "Semi-parametric Model Based Methods to Test for Biosimilarity" (Room 218, Covell Hall)
• May 16, 2016: Wei Sun, Fred Hutchinson Cancer Research Center, TBA (Room 218, Covell Hall)
• May 23, 2016: John Henry, VP Data Scientist at Maiden Re, TBA (Room 218, Covell Hall)
• September 26, 2016: Department Orientation
• October 3, 2016: Department of Statistics Faculty research introduction
• October 10, 2016: Brian Sikora, Senior Director, Kaiser Permanente Title: Opportunities in Healthcare Analytics
• October 17, 2016: Donald A. Pierce, Professor Emeritus Statistics, Oregon State University Title: Modern Likelihood - Frequentist Inference
• October 24, 2016: Andrew Ferlitsch, Sharp Labs of America Title: Blazing a Trail on Collecting War Crime Data in Real Time
• October 31, 2016: Claudio Fuentes, Department of Statistics, Oregon State University Title: Marginal Likelihood Estimation of Negative Binomial Parameters with Applications to RNA-Seq Data
• November 7, 2016: Tomi Mori, Professor, Oregon Health Science University Title: Evaluation of Safety in Biomarker Driven Multiple Agent Phase IB Trial
• November 21, 2016: Jinna Liu, Ecova Title: How Big Data is Impacting Utility Industry
• November 28, 2016: Xiaohui Chang, Oregon State University Title: Disease Risk Estimation by Combining Case-Control Data with Aggregated Information on the Population at Risk

• January 5, 2015: Carolyn Killefer, Career Development Center, Oregon State University (Room 102 Owen) "Writing and Effective Resume and Cover Letter"
• January 26, 2015: Jeremy Gaskins, University of Louisville, KY (Room 102 Owen) "Bayesian Methods for Non-Ignorable Dropout in Joint Models in Smoking Cessation Studies"
• February 2, 2015: David Degras, DePaul University, IL (Room 102 Owen) "Estimation and detection of brain activations in fMRI studies"
• February 9, 2015: Juan Restrepo, Oregon State University Department of Mathematics (Room 102 Owen) "Data Assimilation: Non-Gaussian Filtering Strategies for Time Series and Dynamics"
• **February 20, 2015 (SPECIAL DAY) Sanjay Chaudhuri, National University of Singapore (Room TBA) "Hamiltonian Monte Carlo for Bayesian Empirical Likelihood"
• February 23, 2015: Daniel Taylor, SAMSI (Room 102 Owen)
• **February 26, 2015 (SPECIAL DAY) Dr. Ernest Fokoue, Associate Professor of Statistics, Rochester Institute of Technology (Room 112 Kearney) "Random Adaptive Subspace Learning for High Dimension Low Sample Size Data"
• March 2, 2015: Adam Branscum, Oregon State University Biostatistics Department (Room 102 Owen) "Nonparametric Bayesian Applications in Biostatistics"
• **March 5, 2015 (SPECIAL DAY) Jay VerHoef, (Room 112 Kearney) "Spatial Statistical Models for Stream Networks"
• March 9, 2015: Frederic Schoenberg, (Room 102 Owen) "Voronoi residuals and other residual analyses applied to CSEP earthquake forecasts"
• **March 12, 2015 (SPECIAL DAY) Ranjan Maitra, (Room 112 Kearney)
• March 30, 2015: Michael Waterman, Computational Biology, USC
• April 6, 2015: Kathy Hall, Senior Statistician, Hewlett Packard, Corvallis (Room 101 Owen Hall), "Clients, Decisions and Statistics: Issues for consulting statisticians"
• April 13, 2015: Tom Dietterich, Distinguished Professor, School of Electrical Engineering and Computer Science, Oregon State University (Room 200, Gleeson Hall), "Advances in Anomaly Detection"
• April 20, 2015: Roberto Molinari, Research Center for Statistics, University of Geneva (Room 200, Gleeson Hall), "A Robust Wavelet-based Framework for the Estimation of Time Series Models"
• **April 24, 2015 (SPECIAL DAY and time = 3:00 pm): Ron Wasserstein, Executive Director, American Statistical Association (Room 102, Owen Hall),
• May 11, 2015: Ed Waymire, Mathematics,Oregon State University (Room 200, Gleeson Hall), "Revisiting the Hurst Exponent"
• May 18, 2015: Selena Niu, Assistant Professor of Mathematics, University of Arizona (Room 200, Gleeson Hall), "Reduced Ranked Linear Discriminant Analysis"
• **May 19, 2015 (SPECIAL DAY and time = 2:00 pm): Roger Zoh, Texas A & M (Room 106 Owen Hall), "Application of a Probabilistic Correlation Analysis to Two Counts Data Sets"
• June 1, 2015: Jacob Bien, Biological Statistics and Computational Biology, Cornell (Room 200, Gleeson Hall),
• Oct 5, 2015: Eric Fox, EPA, Corvallis, "Nonparametric Methods for Estimating Space-time Hawkes Point Process Models for Earthquake Occurrences"
• Oct 12, 2015: Sara Emerson, Oregon State University, "Biomarker/Diagnostic Test Performance Bounds from Comparison to Imperfect Reference Test"
• **Oct 15, 2015: (Special Day) Vanda de Carvalho, Pontifical Catholic University of Chile."Semiparametric Bayesian inference for the Youden index and its corresponding optimal cutoff value." Kidder Hall Rm# 278
• Oct 19, 2015: Adam Sykulski, University College, London, "Nonstationary Time Series Modeling and Estimation with Applications in Oceanography"
• Oct 26, 2015: Rui Feng, University of Pennsylvania, "Causal Inference Using Categorical Instrumental Variables"
• Nov 02, 2015: Thuan Nguyen, Oregon Health and Science University, "Classified Mixed Model Prediction"
• Nov 09, 2015: Adrian Raftery, University of Washington, "Probabilistic Population Projections with Migration Uncertainty"
• Nov 16, 2015: Don Percival, University of Washington, "Fitting Statistical Models to DART Buoy Data for Operational Tsunami Forecasting"
• Nov 23, 2015: (Horizon Room, Memorial Union Hall) Peter Bickel, University of California, Berkeley, "Statistics,the transfer science, Big Data, and an experience with ENCODE"
• **Nov 24, 2015: (Special Day) Peter Bickel, University of California, Berkeley, "Erdos-Renyi nodes in block models and determining how many blocks are needed for adequate description"

• January 6, 2014: First week of classes, no seminar
• January 13, 2014: Gu Mi, PhD Student, OSU Statistics: "Goodness-of-Fit Tests and Model Diagnostics for Negative Binomial Regression of RNA sequencing Data" (Room 106 Owen Hall)
• February 25, 2014: Kathryn M. Irvine, US Geological Survey, Nothern Rocky Mountain Science Center, "Graphical causal models: the next multimodel inference regime change needed in Ecology?" (Room 212 Kearney Hall)
• March 3, 2014: Hadley Wickham, R Studio (Room 106 Owen Hall)
• April 3, 2014: Yiyi Chen, Assistant Professor, Division of Biostatistics, Department of Public Health and Preventive Medicine, Oregon Health & Science University, "The use of testing confidence value for the transitional decisions of single-arm phase II oncology trials" (Room 102 Furman Hall)
• April 7, 2014: Sandrine Dudoit, Professor, Division of Biostatistics, School of School of Public Health, and Department of Statistics, University of California, Berkeley (CH2M HILL Alumni Center)
• April 14, 2014: Michael Wu, Assistant Member, Biostatistics and Biomathematics Program, Public Health Sciences Division, Fred Hutchinson Cancer Research Center. " Integrative Genomic Analysis and Detecting Epistasis using Flexible Statistical Methods" (Room 106 Owen Hall)
• April 21, 2014: Yongli Zhang, Assistant Professor, Department of Decision Sciences, University of Oregon (Room 106 Owen Hall)
• April 28, 2014: Heping Zhang, Susan Dwight Bliss Professor, Department of Biostatistics; Professor, Yale Child Study Center and Department of Statistics, Yale University (Room 106 Owen Hall)
• May 12, 2014: Shuangge Ma, Associate Professor, Department of Biostatistics, Yale University (Room 106 Owen Hall)
• May 19, 2014: Thomas Sharpton, Assistant Professor, Departments of Microbiology and Statistics, Oregon State University (Room 106 Owen Hall)
• September 29, 2014: Statistics Department Orientation (required for all Statistics students and faculty) (Room 102 Bexell Hall)
• October 6, 2014: Research summaries from Statistics Department Faculty (Room 102 Bexell Hall)
• **October 15, 2014: Wednesday (Special date) ASA Oregon Chapter Meeting **5:30 pm - 8:30 pm (Room 305 Kearney Hall) "Bayesian Model Selection for Multiple Genetic Models: Application to GWAS Data" Must RSVP to [email protected] if you are planning to attend.
• October 20, 2014: Jennifer Huckett, Associate, Cadmus Consulting, Energy Services Division (Room 102 Bexell Hall), "Strategies for Addressing Sampling and Analysis Challenges in Energy Efficiency Program Evaluation"
• **October 31, 2014: Friday (Special date) John Sall, Co-founder and Executive Vice President of SAS **2:00 pm - 3:00 pm (Room 4001 Ag Life Sciences Bldg) "From Big Data to Big Statistics"
• November 3, 2014: Dan Cooley, Colorado State University (Room 102 Bexell Hall) "Data mining for Extreme Behavior and Application to Ground Level Ozone"
• **November 4, 2014: Tuesday (Special date) David A. Dickey, William Neal Reynolds Professor of Statistics, North Carolina State University **12:00 noon - 1:00 pm (Room 103 Bexell Hall) "Overview of Data Mining"
• November 10, 2014: Kristen Gore, Statistician, Hewlett-Packard Company (Room 102 Bexell Hall), "Unbiased Penetrance Estimates with Unknown Ascertainment Strategies"
• November 17, 2014: Addison James, Oregon State University (Room 102 Bexell Hall), "Nonparametric Information Criterion for Model-Assisted Survey Estimators"
• November 24, 2014: Yanming Di, Professor, Oregon State University (Room 102 Bexell Hall), "Statistical Issues in Differential Expression Analysis of RNA-Sequencing Data"

• October 1, 2013: Marie Davidian, 2013 President of the American Statistical Association and William Neal Reynolds, Professor of Statistics North Carolina State University. "The Right Treatment for the Right Patient (at the Right Time): Personalized Medicine and Statistics"
• October 12, 2013: Tim Hesterburg, Google, Inc. (this seminar is at PSU). "Apps, Earthquakes, and Survival: some statistical stories from Google and beyond"
• October 14, 2013: Len Stefanski, Drexel Professor - Department of Statistics North Carolina State University. "Measurement Error and Variable Selection in Parametric and Nonparametric Models"
• October 21, 2013: Sastry Pantula, Dean – Oregon State University College of Science. "ABCs @ NSF" (Room 102 Owen Hall)
• October 28, 2013: Don Dillman, Regents Professor in the Department of Sociology at Washington State University. (Room 102 Owen Hall)
• November 4, 2013: Bryan Wright, Ariel Muldoon, Matt Nahorniak (former OSU M.S. students). (Room 203 Strand Hall).
• November 11, 2013: OSU Statistics Dept, brief summary of research areas. (Room 203 Strand Hall)
• November 18, 2013: Leigh Ann Harrod, Environmental Consultant and former Oregon State University PhD student) (Room 203 Strand Hall)
• November 25, 2013: Qiongxia Song, Department of Mathematics – University of Texas at Dallas. (Room 203 Strand Hall)