Physical and Engineering Sciences Section News for June 2019

Brad Evans, SPES JSM Program Chair

The Physical and Engineering Sciences Section (SPES) will sponsor three invited and two topic-contributed sessions at JSM in Denver. SPES is also co-sponsoring multiple invited, topic-contributed, poster, and speed sessions, as well as continuing education courses.

Invited Sessions

Combinatorial Testing: Using Covering Arrays to Maximize the Impact of Testing
Organized by Michael Crotty with speakers Raghu Kacker, Dennis Lin, Ryan Lekivetz, and Caleb King

Experimental Design Applications in the Pharmaceutical Industry
Organized by Stan Altan with speakers Brad Evans, Jose Ramirez, Jyh-Ming Shoung, Yia Hu, and Stan Altan

Statistics Impacting Challenges Within Academia, Industry, and Government
Organized by Claire McKay Bowen with speakers Evercita Cuevas Eugenio, Suzanne Marie Neidhart, Lois Keller Smith, and Amanda Koepke

Topic-Contributed Sessions

Data Fusion: An Exploration of Practical Aspects
Organized by Emily Casleton, Los Alamos National Laboratory

Less Can Be More: Smart Sampling in Data and Engineering Sciences
Organized by Xinwei Deng, Virginia Tech, and C. Devon Lin, Queen’s University

Co-Sponsored Invited and Topic-Contributed Sessions

Beyond the VAR: Advances in Spatial and Spatio-Temporal Modeling for Climate and Environmental Data,
with Section on Statistics and the Environment and National Research Center for Statistics for the Environment

Uncertainty Quantification in Various Applications,
with ASA Advisory Committee on Climate Change Policy and Section on Statistics and the Environment

Climate Networks and Extremes,
with Section on Risk Analysis and Section on Statistics and the Environment

Filtering Methods for Spatio-Temporal Big Data Applications,
with Section on Statistics and the Environment and Section on Statistical Computing

Quantitative Inference for the Global Carbon Cycle,
with Section on Statistics and the Environment and WNAR

Decision-Making in Tech Giants Through A/B Testing, Prediction, and Optimization,
with Quality and Productivity Section and Section on Statistical Learning and Data Science

Co-Sponsored Continuing Education Courses

Big Data, Data Science, and Deep Learning for Statisticians,
with ASA and Quality and Productivity Section
Instructors: Ming Li, Amazon, and Hui Lin, Netlify

Design and Analysis of Experiments That Incorporate Simulator Platforms,
with ASA
Instructors: Thomas Santner, The Ohio State University, and Brian J. Williams, Los Alamos National Laboratory