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Fall 2020

Last modified on Wed Dec 9, 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: Quantifying Spatio-temporal Convective Structure in Tropical Cyclones