NISS National Labs Career Fair 2024

Speakers:

Moderator: 

Nancy McMillan, Division Manager Advanced Analytics, Battelle

Agenda

Each presenter will have 15 minutes to address the following general topics:

1. What are the job opportunities for statisticians/data scientists/analysts in your National Lab?
2. Describe the range of skills statisticians/data scientists/analysts need to succeed in your National Lab.
3. How do you prepare yourself for obtaining a position at a National Lab?
4. What is the career path for statisticians/data scientists/analysts in your lab?
5. Is your agency currently hiring statisticians/data scientists/analysts?
6. What advice would you give to students based on your experience? 

About the Speakers

Emily Casleton is currently the deputy group leader of the statistical sciences group, but was recruited to LANL as a summer student at the 2012 Conference on Data Analysis (CoDA). She joined the Lab as a post doc in 2014 after earning her PhD in Statistics from Iowa State University. Since converting to staff in 2015, Emily has routinely collaborated with seismologists, nuclear engineers, physicists, geologists, chemists, and computer scientists on a wide variety of cool data-driven projects. Most recently, she has been the PI of a data analytics project under the NA-22 venture MINOS; co-organizer of the invited CCS-6 seminar series; and co-chair of CoDA, the conference that brought her here a decade ago. She holds a BS in Mathematics, Political Science from Washington & Jefferson College, 2003; a MS in Statistics from West Virginia University, 2006; and a PhD in Statistics from Iowa State University, 2014. 

Karl Pazdernik is a senior data scientist at Pacific Northwest National Laboratory. He is also a research assistant professor at North Carolina State University (NCSU) and the chair of the American Statistical Association Section on Statistics in Defense and National Security. His research has focused on the dynamic modeling of multi-modal data with a particular interest in text analytics, spatial statistics, pattern recognition, anomaly detection, Bayesian statistics, and computer vision. Recent projects include natural language processing of multilingual unstructured financial data, anomaly detection in combined open-source data streams, automated biosurveillance and disease forecasting, and deep learning for defect detection and element mass quantification in nuclear materials. He received a Ph.D. in Statistics from Iowa State University and was a postdoctoral scholar at NCSU under the Consortium for Nonproliferation Enabling Capabilities.

Lyndsay Shand is a Principal Member of the Technical Staff, Statistical Sciences, Sandia National Laboratories. Lyndsay has expertise in spatial and spatio-temporal statistics, point process models, Bayesian hierarchical models, and statistical methods for remotely-sensed data, atmospheric, and climate applications. For the past eight years, she has developed space-time statistical methods for observational environmental and climate data. As principal investigator of Sandia’s first Marine Cloud Brightening LDRD, her interdisciplinary team developed data-driven methods to understand the local impacts of ship emissions. Lyndsay is also a key contributor to Sandia’s Climate Security Strategy and holds an adjunct faculty position in the Department of Statistics at University of Illinois Urbana-Champaign.

Stefan Wild is the Director of Berkeley Lab’s Applied Mathematics and Computational Research (AMCR) Division. Wild is internationally known for his work in DFO and has a long research record of problem-solving in optimization involving expensive computer simulations, large data sets, and physical experiments. He is the co-developer of data profiles, a popular tool for analyzing the performance of derivative-free optimization solvers when there are constraints on the computational budget. He has also developed computational noise estimation techniques and model-based methods for global, non-smooth, and stochastic DFO. Wild received his Ph.D. in operations research from Cornell University in 2009. Before he came to Berkeley Lab, Wild was a senior computational mathematician and deputy division director of the Mathematics and Computer Science Division at Argonne National Laboratory. He has served on editorial boards of journals such as INFORMS Journal on Computing, Mathematical Programming Computation, Operations Research, SIAM Journal on Scientific Computing, and SIAM Review. Wild is also an adjunct faculty member in Industrial Engineering and Management Sciences and a senior fellow in NAISE at Northwestern University.

About the Moderator

Nancy McMillan leads the Advanced Analytics Division at Battelle, with responsibility for a portfolio of Government and commercial contract research programs in data applications, statistics, and machine learning. Her team provides machine learning and artificial intelligence expertise to cross-disciplinary research projects across Battelle. Dr. McMillan is a Data Scientist with over 20 years experience working in a research environment. Her technical expertise is focused on providing quantitative analysis that captures uncertainty to support science-based decision-making, particularly for problems that require analysis of big data. Project Management Professional since 2011.