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Activity Details

220128 Sun, 8/2/2020, - Virtual
JSM Walk/Run - Day 1 — Other Cmte/Business
Section on Physical and Engineering Sciences
Organizer(s): Anindya Bhadra, Purdue University; Robert B Gramacy, Virginia Tech
Chair(s): Robert B Gramacy, Virginia Tech

Join us for the JSM Walk/Run challenge! It is an on demand challenge so it doesn’t matter if you are a morning walker or runner, an evening walker or runner, or any other time of the day. Beginning on Sunday, August 2 and concluding on Thursday, August 6 we will have daily challenges. You can earn 1 point for completing a walk/run, 1 point for completing the daily challenge, and 1 point posting on social media.

Track Your Results

Enter your name as a participant and track your daily results. There will be prizes for completing the daily challenge. Every walker or runner who earns 10 points or more will be eligible for the grand prize drawing!

Here are the daily challenges:

Sunday, August 2 - Walk/run 1 mile of a new route + predict how long it will take you to walk or run a mile for the last day. You will need to submit your prediction by 11:59 p.m. ET on Tuesday, August 4

Monday, August 3 - Walk/run 2020 steps or something to do with 2020 (JSM and decennial census for the US)

Tuesday, August 4 - Walk/run a route in the shape of your favorite greek letter. You may submit a map from your fitness device, indicate your route on google maps, or an image of a map showing your route.

Wednesday, August 5 - Walk/run 1 mile, pi kilometers (1.95 miles), pi miles (3.14), or tau miles (6.28 miles)

Thursday, August 6 - Walk/run 1 mile and see how close you are to your prediction.

10:05 AM Computer Model Emulation and Uncertainty Quantification Using a Deep Gaussian Process
Derek Bingham, Simon Fraser University; Ilya Mandel, School of Physics and Astronomy, Monash University; Daniel Williamson, University of Exeter; Faezeh Yazdi, Simon Fraser University
10:30 AM Active Learning for Deep Gaussian Process Surrogates
Annie Sauer, Virginia Tech
10:55 AM Beyond Matérn: On a Class of Interpretable Confluent Hypergeometric Covariance Functions
Pulong Ma, Duke University / SAMSI; Anindya Bhadra, Purdue University
11:20 AM DeepKriging: Spatially Dependent Deep Neural Networks for Spatial Prediction
Yuxiao Li, King Abdullah University of Science and Technology (KAUST); Ying Sun, King Abdullah University of Science and Technology (KAUST); Brian Reich, North Carolina State University