Fall 2020


Group meetings are for students and faculty at CMU and UPitt.
The STAMPS webinars are open to everyone.

These meetings are Fridays 3-4 PM (note the new time for this semester!). Following university directives, the meetings take place over Zoom.

Public Webinars will still be at the old time 1:30-2:30 PM.

September 4, 3PM EST - Welcome Meeting #


September 11, 1:30PM EST - Public Webinar : Parker Holzer (Department of Statistics & Data Science, Yale University)


September 18, 3PM EST - Hamish Gordon (CMU, Engineering Research Accelerator and the Center for Atmospheric Particle Studies) #

Reducing uncertainty in climate models using atmospheric observations

Note: Advanced Data Analysis (ADA) related talk


September 25, 3PM EST - Coty Jen (CMU, Chemical Engineering Department) #

Identifying pollution sources from complex atmospheric observations

Note: Advanced Data Analysis (ADA) related talk


October 2, 3PM EST - Kimberly Wood (Mississippi State University) #

Quantifying vertical wind shear patterns to better understand how shear impacts hurricanes

Note: Advanced Data Analysis (ADA) related talk


October 9, 1:30PM EST - Public Webinar : Amy Braverman (Jet Propulsion Laboratory, California Institute of Technology)

October 9, 3PM EST - Tommaso Dorigo (INFN-Padova) #

Calorimetric muon energy reconstruction for particle colliders

Note: Advanced Data Analysis (ADA) related talk


October 15, 5PM EST - Alison Gray (University of Washington) #

Oceanographic Observations from the Argo Array: Challenges and Opportunities for Statisticians

Note: Advanced Data Analysis (ADA) related talk


October 23, 1:30PM EST - Public Webinar : Collin Politsch (Machine Learning Department, Carnegie Mellon University)


November 13, 1:30PM EST - Public Webinar : Murali Haran (Department of Statistics, Pennsylvania State University)


December 4, 1:30PM EST - Public Webinar : Jenni Evans (Department of Meteorology & Atmospheric Science, Pennsylvania State University)


December 18, 3PM EST - Trey McNeely (CMU, Statistics & Data Science) #

Thesis Proposal