Workshop on Data-Driven Mathematical & Statistical Modeling for Graduate Students

July 12-16, 2021
(virtual, via Zoom)

Application Deadline was May 31, 2021


This five-day virtual workshop will introduce graduate students to topics in mathematical and statistical modeling that are needed to carry out advanced research in a data-driven context. Important aspects of model formulation and selection, parameter estimation, sensitivity analysis and uncertainty quantification, including synergies with machine learning, will be covered in a series of mini-tutorials. The tutorials will include projects that participants will work on in groups, presenting their results on the final day of the workshop. Researchers from outside of academia (national labs, government agencies, industry) will present a series of case studies demonstrating how advanced mathematical and statistical research is used to address problems in these sectors. The workshop will also include a set of career development activities and professional development panel discussions. Participants will leave the workshop with a greater understanding of the skills needed to address mathematical and statistical problems in a data rich context whether in academia, government, or industry.


Applicants must be currently enrolled full-time in a US-based mathematics, statistics (or related) graduate program. Participants are expected to have access to high-speed internet and a computer with R and/or Matlab. Participants must also make a commitment to participate in the full workshop.

Schedule: (All Times are EDT – New York):

Monday July 12th
Day 1
9:30 Opening & Introductions: SAMSI Directorate Members
9:45 Overview of Workshop: Mansoor Haider & Emily Griffith
10:00 Tutorial#1: Alen Alexanderian
Sensitivity analysis of optimal control problems with application to disease modeling
11:15 Tutorial#2: Ralph Smith
Bayesian Inference and Uncertainty Propagation for Physical and Biological Models
12:15 Break
1:30 A2I Strength Training Professional Development Workshop
Dr. Joe Aldinger (Director) & Morgan Dalman (Program Assistant), A2i, NCSU
3:30 Break
4:00 Tutorial#3: Srijan Sengupta
NLP-driven statistical modeling for patient safety
Tuesday July 13th
Day 2
10:00 Tutorial#4: John Nardini
Learning differential equation models from stochastic agent-based model simulations
11:00 Initial Team Meetings
Breakout Rooms
11:45 Case Study#1: Mosquitoes Everywhere! Modeling Vector Borne Diseases in a Changing Environment
Kimberly Kaufeld, Los Alamos National Laboratories
12:45 Break
1:30 Case Study#2: Where’s the Bottom? Remote Assessment for Flood Risk Reduction and Mobility in Shallow Aquatic Environments
Matt Farthing & Ty Hesser, US Army Corps of Engineers
2:40 Case Study #3: A Case Study of Applied Mathematics at Sandia National Laboratories: Design of Electromagnetic Reflectors with Integrated Shape Control
Jordan Massad, Sandia National Laboratories
4:00 Panel on Careers in National Labs & Government Agencies
Kimberly Kaufeld (Los Alamos), Matt Farthing (USACE), Ty Hesser (USACE), Jordan Massad (Sandia)
Wednesday July 14th
Day 3
9:30 Working Session on Projects in Teams
Breakout Rooms
12:00 Break
1:30 Panel on Careers in Industry
Bruce Campbell (Red Hat), Laura Potter (Syngenta), Shusheela Singh (Google), Hoang Tran (GSK)
2:30 Working Session on Projects in Teams
Breakout Rooms
3:30 Team Progress Updates
4:00 Panel on Careers in Academia featuring Junior Faculty
Emily Hector (NCSU), Emily Kang (University of Cincinnati), Andee Kaplan (Colorado State University), Alexandria Volkening (Northwestern), Arvind Saibaba (NCSU)
Thursday July 15th
Day 4
9:30 Working Session on Projects in Teams
Breakout Rooms
12:00 Break
1:30 Hot Topic Lecture – Accelerated, Derivable Symbolic Discovery through Synergy of Theory and Data
Lior Horesh, Senior Manager & Master Inventor, AI Science, IBM TJ Watson Research Center
2:30 Working Session on Projects in Teams
Breakout Rooms
4:00 Team Progress Updates
Friday July 16th 
Day 5
10:00 Final Working Session on Projects in Teams
Breakout Rooms
12:30 Break
1:30 Group 1 – Sensitivity Analysis – Alexanderian
2:00 Group 3 – NLP – Sengupta
2:30 Break
3:00 Group 2 – Bayesian Inference – Smith
3:30 Group 4 – Learning DiffEq – Nardini
4:00 Closing Remarks & Adjourn