The SIAM student chapter is hosting a Mathematics in Industry Seminar on February 11, 2021 from 2 to 3 PM. Immediately afterwards, we hosting an Internship Panel with the MGSA.

Mathematics in Industry Seminar
Speaker: Dr. Hailey Owen
Title: Journey into Industry
Abstract:
How I got to SAS:
I began a year round internship at SAS, in my current department, in April of 2017, my 3rd year in the PhD program. I looked for internships, after completing my qualifying exams, that complimented my history of teaching and love for analytics.
What do I do?
I work in EVAAS at SAS. We use K-12 standardized tests to assess and predict student performance. We use multiple models, usually gain based models and regression based models, to create measures that assist educators and policymakers in making decisions regarding schooling practices.

Internship Panel:
Description :
The NCSU chapters of SIAM and MGSA are very excited to announce our upcoming event, the “Math Internship Presentation and Panel” which will take place Thursday, February 11th at 3:00pm. The goal of this event is to answer questions about math internships coming from those currently in the application process for math internships, as well as those looking to apply in the future. Our panelists have a diverse background in varying different types of internships, including positions at Sandia National Labs and the US Air Force Research Lab. The target audience for this event is both undergraduate and graduate math majors/graduate students interested in applying to a math internship, either now or at any time in the future. You do not need to be a member of SIAM or MGSA to participate.

The SIAM Student Chapter will be hosting an information session for careers at Sandia National Laboratories.

Make a Difference: Mathematical Sciences R&D Careers at Sandia National Laboratories

Abstract: Brian Adams and colleagues will conduct a mathematics and statistics-specific information session including a brief overview of SNL’s mission, R&D areas, and opportunities in mathematics, statistics, and computational science. Staff and project profiles will demonstrate the ways you can contribute to high-impact problems in the national interest through fundamental math and computational science R&D, software/hardware development, and working in interdisciplinary teams on engineering applications. We are hiring at all degree levels.

Date: Thursday, October 22, 2020, 2:00p EST
Email Justen Geddes (jgeddes@ncsu.edu) or Tim Reid (twreid@ncsu.edu) for the Zoom Link

For Thursday’s SNL Info Session, we strongly encourage attendees to register on Yello, as it helps SNL track your interest and set up follow-on discussions. If you are interested in a one-on-one career discussion with Brian or another R&D staff member, you must register in Yello, upload a CV, and express interest by emailing briadam@sandia.gov.

There are two ways to register:

1. Click on this URL: https://sandialabs.recsolu.com/external/events/dCVISUfnDcGGfXd9WxC1_A
2. Using your Smartphone, text: SandiaMath2020 to 505.605.6276

Additional Information
R&D Staff from Sandia National Laboratories will be recruiting top NCSU students at all degree levels in the coming weeks. With sites in NM and CA, SNL is a Department of Energy laboratory conducting wide-ranging science and engineering R&D supporting national security. Please join us for a virtual info session and/or career fair! Students who register and submit a CV will be considered for one-on-one meetings to discuss their interests and possible careers at SNL.

SNL are hiring for the following types of positions:

• Regular Staff (MS or PhD degrees); GPA may be considered, but no minimum
• Post-Doctoral Researcher (PhD degrees) ); GPA may be considered, but no minimum
• Summer or Year Round Intern (undergraduate or graduate); cumulative GPAs for all degrees must be above 3.0 on a 4.0 scale

Speakers from SNL will also be at the NC MS/PhD Virtual Career Fair on Thu, Oct 29.

Speaker Biographies

Brian M. Adams is a principal member of technical staff in the optimization and uncertainty quantification department at Sandia National Laboratories, where he has worked since 2005. He holds a PhD in Computational and Applied Mathematics from North Carolina State University. Brian develops, implements, and applies algorithms for optimization and uncertainty quantification on computational models. He leads the Dakota software project, managing software development and deployment to ensure impact on the span of Sandia science and engineering problems.

Teresa Portone is a senior member of the technical staff in the optimization and uncertainty quantification department at Sandia National Laboratories. Prior to assuming her position at Sandia in January 2020, she received her PhD in Computational Science, Engineering, and Mathematics from The University of Texas at Austin. Teresa develops and deploys methods to improve the efficiency and fidelity of uncertainty analyses for practical problems in the national interest.

Jordan E. Massad (MS and PhD in Applied Mathematics, NCSU, 2003) is a Principal Member of Technical Staff in the Component Science and Mechanics Department of the Engineering Sciences Center at Sandia National Laboratories, Albuquerque, New Mexico where his research has focused on the thermomechanical analysis of nonlinear systems. His industrial experience also includes scientific computing research at The Boeing Company’s Mathematics and Computing Technology division.

We will be having Dr. Missy Gaddy talk as a part of our Mathematics in Industry Seminar this Thursday (10/1) at 2-3pm. Missy is a recent NCSU graduate and is now working for Evalueserve.

Abstract: Evalueserve is a global consulting company providing research and analytics solutions for Fortune 500 clients. Evalueserve has numerous focus areas including financial services, data analytics, and market intelligence, with an international presence in China, India, Chile, Europe, and the US. I am a mathematician in the Data Analytics practice currently working on a large-scale network optimization project with one of the top LTL freight carriers in the US. I work closely with our client to understand their business needs, and I formulate mathematical optimization models to provide recommendations for their network of distribution centers and linehaul routes throughout the US. During this talk, I will also speak about my experience from the job search and interview process.

For the Zoom link email Justen Geddes

The Society of Industrial and Applied Mathematics (SIAM), at the national/international level, is dedicated to the advancement of the application of mathematics and computational science, promoting research, and providing media for the exchange of information and ideas among fields. As a member of SIAM you will be able to benefit from, and contribute to, those goals.

What does that mean for you? First, NCSU students are eligible for a free membership in SIAM. Follow these directions to join SIAM:

Go to the SIAM member registration website (https://www.siam.org/membership/join-siam/individual-members)
Click on “Student”
Choose “Free Graduate Student” membership, then follow the directions on the website

We (the NCSU chapter of SIAM) will be hosting multiple events this semester. Due to current circumstances, all events will be online. So far we plan on having 3 industry related talks and a couple of graduate student talks/tutorials. These talks will be sent to the SIAM list once they are scheduled. Unfortunately, being online means there won’t be free food (very sad), but talks will still give excellent insight into different mathematical fields and what it’s like to work in industry, national labs and/or other sectors.

If you are interested in giving a talk/tutorial, or have a recommendation for someone to speak, please email Justen Geddes (jgeddes@ncsu.edu)!

On December 5, 2019 there was a Mathematics in Industry Seminar

The speakers were Drs. Stephen Shauger, Sean Lynch, and Aaron Wiechmann from the Laboratory for Analytic Sciences

Abstract:

How do we know that we can trust our machine learning models? How do we detect when an adversary is exploiting our machine learning solutions? How does the explainability of machine learning models help the customers of such models versus helping the adversaries of such models? In this talk we will examine recent issues in Machine Learning Explainability and in Adversarial Machine Learning.