Statistics researchers at JSM 2017

Faculty and students from the Department of Statistics are participating in the Joint Statistical Meetings (JSM) 2017 in Baltimore, July 29 – August 3. JSM is one of the largest statistical conferences in the world, hosting more than 6,000 statisticians from academia, industry and government and featuring more than 600 research sessions and poster presentations.

Topics at JSM 2017 range from statistical applications to methodology and theory to the expanding boundaries of statistics, such as analytics and data science.

College of Science Dean Sastry Pantula, who is also a Professor of Statistics, will be the luncheon speaker and present a talk, “Strengths, Opportunities and Challenges in the era of BIG Data: An Asian Statistician Perspective.” He will discuss:  The strengths Asian statisticians bring to the profession, opportunities that exist for Asian statisticians in the era of BIG data across all sectors, how universities and professional societies can help build future leaders in statistics, the needs and challenges Asian statisticians face and how Asian statisticians can strive for excellence, enhance diversity and foster harmony in the profession.

Pantula is delivering a talk during the Pre-Conference Workshop, which is part of a continuing education course, “Preparing Statisticians for Leadership: How to See the Big Picture and Have More Influence, Part 2.” The course, which is being held Sunday morning on July 30, addresses what leadership is and how statisticians can improve and demonstrate leadership to affect their organizations. It features leaders from all sectors of statistics speaking about their personal journeys and offering guidance on personal leadership development with a focus on the larger organizational/business view and influence.

Pantula is also participating in a panel discussion of current and former Deans and Provosts of Arts and Sciences who are statisticians. Panelists will share their perspectives and experiences about how to advance the mission of statistics departments in the current university environment. The panel is on Thursday, August 3.

All OSU Statistics alumni are welcomed at a special reception for them on Tuesday, August 1 from 5:30-7:00 p.m. at the Hilton Hotel on Pratt Street in Tubman A. The event offers the perfect occasion to reconnect with other alumni, OSU faculty, students and friends. We look forward to catching up with alumni to hear about their accomplishments and successes!

Below is a complete list of our faculty and student who are presenting talks and poster presentations at JSM 2017. Many of our faculty and alumni are also representing OSU on various committees, are presenting papers on which they are co-authors and participating in other ways at the conference, but are not listed below.

Faculty

Presentation

Lan Xue Semiparametric Estimation of Longitudinal Data with Nonignorable Attrition Using Refreshment Samples
Jeff Kollath Assessment of Impact of Using Learning Assistants in an Introductory Statistics Course 
Sharmodeep Bhattacharyya Spectral Clustering for Dynamic Block Models
Katherine McLaughlin Empirical Assessment of Programs to Promote Collaboration: a Network Model Approach
Sastry Pantula Advancing Statistics in Universities: Deans’ Viewpoints
Sastry Pantula Strengths, Opportunities and Challenges in the Era of BIG Data: An Asian Statistician Perspective 

Student

Talk/Poster Title

Heather Kitada Adjusting for Mode Effects in Longitudinal Studies Utilizing a Bayesian Prior on Within Subject Correlation
Faraz Niyaghi Variable Selection Using Intersection and Average of Random Forests
Jeremy Groom Informing Oregon Forestry Rule Change Decisions with a Bayesian Hierarchical Model
Miao Yang Local Signal Detection on Irregular Domains via Bivariate Splines
Laura Gamble The Interaction Between Question Order and Delivery Mode (All Mail vs. Mixed Mode, Web+Mail) 
Chunxiao Wang Matrix-Free Computation of Spatial-Temporal Gaussian Autoregressions and Related Stat-Space Models
Nima Dolatnia High Dimensional Bayesian Optimization with both Continuous and Categorical Explanatory Variables 

 

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