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## Event Calendar

### Quick Look: Upcoming Events

- Mon Oct 22, 2018 : ACMS Colloquium: Péter L. Erdős, A. Rényi Institute of Mathematics, Budapest, Hungary
- Tue Oct 23, 2018 : ACMS Statistics Seminar: Julie Bessac
- : ACMS Informational Pizza Party
- Thu Oct 25, 2018 : Andy Hiles, Aetna
- Mon Oct 29, 2018 : ACMS Colloquium: Sudipto Banerjee, Department of Biostatistics, UCLA
- Mon Nov 5, 2018 : ACMS Colloquium: Yuhong Hang, Department of Statistics, University of Minnesota
- Thu Nov 8, 2018 : ACMS Applied Math Seminar: Jeff Schenker
- Mon Nov 12, 2018 : ACMS Colloquium: Justin Ellis, Data Scientist, Infinia ML
- Tue Nov 13, 2018 : ACMS Statistics Seminar: Mengyang Gu
- Fri Nov 30, 2018 : ACMS Colloquium: Catalin Trenchea, Department of Mathematics, University of Pittsburgh

## Mon Oct 22, 2018

## ACMS Colloquium: Péter L. Erdős, A. Rényi Institute of Mathematics, Budapest, Hungary

###
4:15 PM - 5:15 PM

127 Hayes-Healy Center

Péter L. Erdős, A. Rényi Institute of Mathematics, Budapest, Hungary, will give a colloquium titled, "Sampling bipartite degree sequence realizations - the Markov chain approach" at 4:15 PM in 127 Hayes-Healy Center.

### Posted In: ACMS Colloquia

## Tue Oct 23, 2018

## ACMS Statistics Seminar: Julie Bessac

###
3:30 PM - 4:30 PM

154 Hurley Hall

Julie Bessac

Argonne National Lab

**3:30 PM**

**154 Hurley Hall**

**1. Stochastic Simulation of Predictive Space-Time Scenarios of Wind Speed Using Observations and Physical Model Outputs**

** and**

**2. Stochastic Parameterization of Subgrid-Scale Velocity Enhancement of Sea Surface Fluxes**

We propose a statistical space-time model for predicting atmospheric wind speed based on deterministic numerical weather predictions (NWP) and historical measurements. We consider a Gaussian multivariate space-time framework that combines multiple sources of past physical model outputs and measurements along with NWP model predictions in order to produce a probabilistic wind speed forecast within the prediction window. The process is expressed hierarchically in order to facilitate the specification of cross-variances between the two datasets. We illustrate this strategy in wind speed forecast during several months in 2012 for a region near the Great Lakes in the United States.…

### Posted In: Statistics Seminar

## ACMS Informational Pizza Party

###
5:00 PM - 6:00 PM

Hurley Hall Globe

Are you interested in learning more about the Applied and Computational Mathematics and Statistics (ACMS) or Statistics major? Would you like to meet our faculty and discuss the many opportunities for students with the ACMS/STAT major? Then join us at the ACMS Informational Pizza Party!

Pizza and refreshments will be served.

…### Posted In: Student Events

## Thu Oct 25, 2018

## Andy Hiles, Aetna

###
5:30 PM - 6:30 PM

127 Hayes-Healy

#### Andy Hiles, F.S.A., M.A.A.A.

Vice Presidentt, Plan Sponsor Insights

Aetna

October 25, 2018

5:30 pm

127 Hayes-Healy

Andy Hiles is Aetna’s Vice President of Plan Sponsor Insights, a team of medical economists and clinicians responsible for analytics, reporting and clinical consulting for Aetna’s group commercial customers. Andy also leads Aetna’s approach to addressing social determinants of health in the group commercial marketplace. Prior to his current role Andy led the strategy, underwriting, actuarial and insured private exchange teams for the National Accounts segment.…

### Posted In: Delahanty Foundation Speaker Series (fka Ask the Actuaries)

## Mon Oct 29, 2018

## ACMS Colloquium: Sudipto Banerjee, Department of Biostatistics, UCLA

###
4:15 PM - 5:15 PM

127 Hayes-Healy Center

Sudipto Banerjee, Dept. of Biostatistics, UCLA, will give a colloquium titled, "TBD" at 4:15 PM in 127 Hayes-Healy Center.

### Posted In: ACMS Colloquia

## Mon Nov 5, 2018

## ACMS Colloquium: Yuhong Hang, Department of Statistics, University of Minnesota

###
4:15 PM - 5:15 PM

127 Hayes-Healy Center

Yuhong Hang, Dept. of Statistics, University of Minnesota, will give a colloquium titled, "TBD" at 4:15 PM in 127 Hayes-Healy Center.

### Posted In: ACMS Colloquia

## Thu Nov 8, 2018

## ACMS Applied Math Seminar: Jeff Schenker

###
3:30 PM - 4:30 PM

154 Hurley Hall

**Jeff Schenker**

Michigan State University

**3:30 PM**

**154 Hurley Hall**

**"Random Walk Models adn Applied Chemical Ecology or How Big Is A Lattice Point**"

**Hitting probabilities play a key role in a theory of insect trapping developed in recent years using a combination of random walk models and field experiments. The goal of this theory, which is still under development, is to provide a sound scientific framework for farmers and pest managers to make decisions about when to apply chemical pesticides based on numbers of pests captured in monitoring traps. In this talk I will briefly describe the scientific background briefly and then turn to the mathematical story of the associated random walk models.**…

### Posted In: Applied Math Seminar

## Mon Nov 12, 2018

## ACMS Colloquium: Justin Ellis, Data Scientist, Infinia ML

###
4:15 PM - 5:15 PM

127 Hayes-Healy Center

Justin Ellis, Data Scientist, Infinia ML, will give a colloquium titled, "TBD" at 4:15 PM in 127 Hayes-Healy Center.

### Posted In: ACMS Colloquia

## Tue Nov 13, 2018

## ACMS Statistics Seminar: Mengyang Gu

###
3:30 PM - 4:30 PM

154 Hurley Hall

Mengyang Gu

John Hopkins University

**3:30 PM**

**154 Hurley Hall**

**A Theoretical Framework Of The Scaled Gaussian Stochastic Process In Prediction and Calibration**

The Gaussian stochastic process (GaSP) is a useful technique for predicting nonlinear functional outcomes. The estimated mean function in a GaSP, however, can be far from the reality in terms of the L2 distance. This problem was widely observed in calibrating imperfect mathematical models using experimental data, when the discrepancy function is modeled as a GaSP. In this work, we study the theoretical properties of the scaled Gaussian stochastic process (S-GaSP), a new stochastic process to address the identifiability problem of the mean function in the GaSP model. We establish the explicit connection between the GaSP and S-GaSP through the orthogonal series representation. We show the predictive mean estimator in the S-GaSP calibration model converges to the reality at the same rate as the GaSP with the suitable choice of the regularization parameter and scaling parameter. We also show the calibrated mathematical model in the S-GaSP calibration converges to the one that minimizes the L2 loss between the reality and mathematical model with the same regularization and scaling parameters, whereas the GaSP model does not have this property. From the regularization perspective, the loss function from the S-GaSP calibration penalizes the native norm and L2 norm of the discrepancy function simultaneously, whereas the one from the GaSP calibration only penalizes the native norm of the discrepancy function. The predictive error from the S-GaSP matches well with the theoretical bound. The simulated data and real data of calibrating the geophysical model of the Kilauea Volcano will be presented concerning the performance of the studied approaches. Both the GaSP and S-GaSP calibration models are implemented in the “RobustCalibration” R Package on CRAN.…

### Posted In: Statistics Seminar

## Fri Nov 30, 2018

## ACMS Colloquium: Catalin Trenchea, Department of Mathematics, University of Pittsburgh

###
4:15 PM - 5:15 PM

127 Hayes-Healy Center

Catalin Trenchea, Department of Mathematics, University of Pittsburgh, will give a colloquium titled, "TBD" at 4:15 PM in 127 Hayes-Healy Center.