Full Agenda Day 1 – DATAWorks 2023

DATAWorks 2023 Agenda Day 1

Show Floorplans

April 25th Agenda

Audience Legend

1
Everyone.
These talks should be accessible to everyone regardless technical background.
2
Practitioners.
These talks might include case studies with some discussion of methods and coding, but largely accessible to a non-technical audience.
3
Technical Experts.
These talks will likely delve into technical details of methods and analytical computations and are primarily aimed at practitioners advancing the state of the art.

7:30 AM – 8:45 AM
		Check-in
9:00 AM – 12:00 PM: Parallel Sessions
	Room A Virtual Session	Short Course 1, Part 1 2 Introduction to Machine Learning Stephen Adams (Virginia Tech National Security Institute) Materials URL: Introduction to Machine Learning Stephen Adams (Virginia Tech National Security Institute) Show Bio Stephen Adams is an Associate Research Professor in the Virginia Tech National Security Institute. He received a M.S. in Statistics from the University of Virginia (UVA) in 2010 and a Ph.D. from UVA in Systems Engineering in December of 2015. His research focuses on applications of machine learning and artificial intelligence in real-world systems. He has experience developing and implementing numerous types of machine learning and artificial intelligence algorithms. His research interests include feature selection, machine learning with cost, transfer learning, reinforcement learning, and probabilistic modeling of systems. His research has been applied to several domains including activity recognition, prognostics and health management, psychology, cybersecurity, data trustworthiness, natural language processing, and predictive modeling of destination given user geo-information data. Materials URL: Machine learning (ML) teaches computer systems through data or experience and can generally be divided into three broad branches: supervised learning, unsupervised learning, and reinforcement learning. The objective of this course is to provide attendees with 1) an introduction to ML methods, 2) insights into best practices, and 3) a survey of limitations to existing ML methods that are leading to new areas of research. This introduction to machine learning course will cover a wide range of topics including regression, classification, clustering, feature selection, exploratory data analysis, reinforcement learning, transfer learning, and active learning. This course will be taught through a series of lectures followed by demonstrations on open-source data sets using Jupyter Notebooks and Python.
	Room B Virtual Session	Short Course 2, Part 1 2 Applied Bayesian Methods for Test Planning and Evaluation Victoria Sieck (STAT COE/AFIT) Cory Natoli (Huntington Ingalls Industries/STAT COE) Corey Thrush (Huntington Ingalls Industries/STAT COE) Materials URL: Applied Bayesian Methods for Test Planning and Evaluation Bayesian methods have been promoted as a promising way for test and evaluation analysts to leverage previous information across a continuum-of-testing approach to system evaluation. This short course will cover how to identify when Bayesian methods might be useful within a test and evaluation context, components required to accomplish a Bayesian analysis, and provide an understanding of how to interpret the results of that analysis. The course will apply these concepts to two hands-on examples (code and applications provided): one example focusing on system reliability and one focusing on system effectiveness. Furthermore, individuals will gain an understanding of the sequential nature of a Bayesian approach to test and evaluation, the limitations thereof, and gain a broad understanding of questions to ask to ensure a Bayesian analysis is appropriately accomplished. Additional Information: Recommended background: basic understanding of common distributions (e.g., Normal distribution, Binomial distribution) To follow along with examples, it is recommended you bring a computer that… o Can access the internet o Already has R Studio loaded (code will be provided; however, not having R Studio will not be a detriment to those who are interested in understanding the overall process but not executing an analysis themselves) Victoria Sieck (STAT COE/AFIT) Dr. Victoria R. C. Sieck is the Deputy Director of the Scientific Test & Analysis Center of Excellence (STAT COE), where she works with major acquisition programs within the Department of Defense (DoD) to apply rigor and efficiency to current and emerging test and evaluation methodologies through the application of the STAT process. Additionally, she is an Assistant Professor of Statistics at the Air Force Institute of Technology (AFIT), where her research interests include design of experiments, and developing innovate Bayesian approaches to DoD testing. As an Operations Research Analyst in the US Air Force (USAF), her experiences in the USAF testing community include being a weapons and tactics analyst and an operational test analyst. Dr. Sieck has a M.S. in Statistics from Texas A&M University, and a Ph.D. in Statistics from the University of New Mexico. Cory Natoli (Huntington Ingalls Industries/STAT COE) Dr. Cory Natoli works as an applied statistician at Huntington Ingalls Industries as a part of the Scientific Test and Analysis Techniques Center of Excellence (STAT COE). He received his MS in Applied Statistics from The Ohio State University and his Ph.D. in Statistics from The Air Force Institute of Technology. His emphasis lies in design of experiments, regression modeling, statistical analysis, and teaching. Corey Thrush (Huntington Ingalls Industries/STAT COE) Mr. Corey Thrush is a statistician at Huntington Ingalls Industries within the Scientific Test and Analysis Techniques Center of Excellence (STAT COE). He received a B.S. in Applied Statistics from Ohio Northern University and an M.A. in Statistics from Bowling Green State University. His interests are data exploration, statistical programming, and Bayesian Statistics. Materials URL:
	Room C	Short Course 3, Part 1 1 Present Your Science Melissa Marshall (Present Your Science) Materials URL: Present Your Science Melissa Marshall (Present Your Science) Show Bio Melissa Marshall is the leading expert on presenting complex ideas. Melissa Marshall is on a mission: to transform how scientists, engineers, and technical professionals present their work. That’s because she believes that even the best science is destined to remain undiscovered unless it’s presented in a clear and compelling way that sparks innovation and drives adoption. For a decade, she’s traveled around the world to work with Fortune 100 corporations, institutions and universities, teaching the proven strategies she’s mastered through her consulting work and during her decade as a faculty member at Penn State University. In 2019 through 2022, Microsoft has named her a Most Valuable Professional (MVP) for her work in transforming the way the scientific community uses PowerPoint to convey their research. Melissa has also authored a new online course on LinkedIn Learning. Melissa’s workshops are lively, practical and transformational. For a sneak peek, check out her TED Talk, “Talk Nerdy to Me.” It’s been watched by over 2.5 million people (and counting). Materials URL: This comprehensive 1-day course equips scientists, engineers, researchers, and technical professionals to present their science in an understandable, memorable, and persuasive way. Through a dynamic combination of lecture, discussion, exercises, and video analysis, each participant will walk away with the skills, knowledge, and practice necessary to transform the way their work is presented. Five course objectives are covered: Transform the scientific presentations skills of participants. Enable participants to utilize effective strategies for content, structure, slide design and delivery of scientific presentations. Teach participants to analyze and adapt to their audience. Help participants understand which scientific details to emphasize in their presentation and which details to filter out. Equip participants to understand and enact the assertion evidence slide design in their own talks to make their scientific presentation slides more understandable, memorable, and engaging. Assist participants in developing an engaging and confident delivery style. * Attendees should bring a laptop with them to the session. *
	Room E	Short Course 4, Part 1 2 Plotting and Programming in Python Chasz Griego and Elif Dede Yildirim (The Carpentries) Materials URL: Plotting and Programming in Python Chasz Griego and Elif Dede Yildirim (The Carpentries) Show Bio Chasz Griego is an Open Science Postdoctoral Associate at the Carnegie Mellon University (CMU) Libraries. He received a PhD in Chemical Engineering from the University of Pittsburgh studying computational models to accelerate catalyst material discovery. He leads and supports Open Science teaching and research initiatives, particularly in the areas of reproducibility in computational research. His research involves investigating how open tools help promote reproducibility with computational research. He supports students and researchers at CMU with Python programming for data science applications, literate programming with Jupyter Notebooks, and version control with Git/GitHub. Elif Dede Yildirim is a data scientist within the Office of Data and Analytics at All of US Program, NIH. She leads the data quality workstream and support demo and driver projects. She holds MS degrees in Statistics and Child Development, and my PhD in Child Development from Syracuse University. She completed her postdoctoral work at the University of Missouri-Columbia and held a faculty appointment at Auburn University, where she taught graduate-level method and stats courses and provided statistical consulting. She is currently pursuing her second undergraduate degree in Computer Science at Auburn, and plan to graduate in December 2023. Materials URL: Plotting and Programming in Python is an introductory Python lesson offered by Software Carpentry. This workshop covers data analysis and visualization in Python, focusing on working with core data structures (including tabular data), using conditionals and loops, writing custom functions, and creating customized plots. This workshop also introduces learners to JupyterLab and strategies for getting help. This workshop is appropriate for learners with no previous programming experience.
12:00 PM – 1:00 PM
		Lunch
1:00 PM – 4:00 PM: Parallel Sessions
	Room A Virtual Session	Short Course 1, Part 2 2 Introduction to Machine Learning Stephen Adams (Virginia Tech National Security Institute) Materials URL: Introduction to Machine Learning Stephen Adams (Virginia Tech National Security Institute) Show Bio Stephen Adams is an Associate Research Professor in the Virginia Tech National Security Institute. He received a M.S. in Statistics from the University of Virginia (UVA) in 2010 and a Ph.D. from UVA in Systems Engineering in December of 2015. His research focuses on applications of machine learning and artificial intelligence in real-world systems. He has experience developing and implementing numerous types of machine learning and artificial intelligence algorithms. His research interests include feature selection, machine learning with cost, transfer learning, reinforcement learning, and probabilistic modeling of systems. His research has been applied to several domains including activity recognition, prognostics and health management, psychology, cybersecurity, data trustworthiness, natural language processing, and predictive modeling of destination given user geo-information data. Materials URL: Machine learning (ML) teaches computer systems through data or experience and can generally be divided into three broad branches: supervised learning, unsupervised learning, and reinforcement learning. The objective of this course is to provide attendees with 1) an introduction to ML methods, 2) insights into best practices, and 3) a survey of limitations to existing ML methods that are leading to new areas of research. This introduction to machine learning course will cover a wide range of topics including regression, classification, clustering, feature selection, exploratory data analysis, reinforcement learning, transfer learning, and active learning. This course will be taught through a series of lectures followed by demonstrations on open-source data sets using Jupyter Notebooks and Python.
	Room B Virtual Session	Short Course 2, Part 2 2 Applied Bayesian Methods for Test Planning and Evaluation Victoria Sieck (STAT COE/AFIT) Cory Natoli (Huntington Ingalls Industries/STAT COE) Corey Thrush (Huntington Ingalls Industries/STAT COE) Materials URL: Applied Bayesian Methods for Test Planning and Evaluation Bayesian methods have been promoted as a promising way for test and evaluation analysts to leverage previous information across a continuum-of-testing approach to system evaluation. This short course will cover how to identify when Bayesian methods might be useful within a test and evaluation context, components required to accomplish a Bayesian analysis, and provide an understanding of how to interpret the results of that analysis. The course will apply these concepts to two hands-on examples (code and applications provided): one example focusing on system reliability and one focusing on system effectiveness. Furthermore, individuals will gain an understanding of the sequential nature of a Bayesian approach to test and evaluation, the limitations thereof, and gain a broad understanding of questions to ask to ensure a Bayesian analysis is appropriately accomplished. Additional Information: Recommended background: basic understanding of common distributions (e.g., Normal distribution, Binomial distribution) To follow along with examples, it is recommended you bring a computer that… o Can access the internet o Already has R Studio loaded (code will be provided; however, not having R Studio will not be a detriment to those who are interested in understanding the overall process but not executing an analysis themselves) Victoria Sieck (STAT COE/AFIT) Dr. Victoria R. C. Sieck is the Deputy Director of the Scientific Test & Analysis Center of Excellence (STAT COE), where she works with major acquisition programs within the Department of Defense (DoD) to apply rigor and efficiency to current and emerging test and evaluation methodologies through the application of the STAT process. Additionally, she is an Assistant Professor of Statistics at the Air Force Institute of Technology (AFIT), where her research interests include design of experiments, and developing innovate Bayesian approaches to DoD testing. As an Operations Research Analyst in the US Air Force (USAF), her experiences in the USAF testing community include being a weapons and tactics analyst and an operational test analyst. Dr. Sieck has a M.S. in Statistics from Texas A&M University, and a Ph.D. in Statistics from the University of New Mexico. Cory Natoli (Huntington Ingalls Industries/STAT COE) Dr. Cory Natoli works as an applied statistician at Huntington Ingalls Industries as a part of the Scientific Test and Analysis Techniques Center of Excellence (STAT COE). He received his MS in Applied Statistics from The Ohio State University and his Ph.D. in Statistics from The Air Force Institute of Technology. His emphasis lies in design of experiments, regression modeling, statistical analysis, and teaching. Corey Thrush (Huntington Ingalls Industries/STAT COE) Mr. Corey Thrush is a statistician at Huntington Ingalls Industries within the Scientific Test and Analysis Techniques Center of Excellence (STAT COE). He received a B.S. in Applied Statistics from Ohio Northern University and an M.A. in Statistics from Bowling Green State University. His interests are data exploration, statistical programming, and Bayesian Statistics. Materials URL:
	Room C	Short Course 3, Part 2 1 Present Your Science Melissa Marshall (Present Your Science) Materials URL: Present Your Science Melissa Marshall (Present Your Science) Show Bio Melissa Marshall is the leading expert on presenting complex ideas. Melissa Marshall is on a mission: to transform how scientists, engineers, and technical professionals present their work. That’s because she believes that even the best science is destined to remain undiscovered unless it’s presented in a clear and compelling way that sparks innovation and drives adoption. For a decade, she’s traveled around the world to work with Fortune 100 corporations, institutions and universities, teaching the proven strategies she’s mastered through her consulting work and during her decade as a faculty member at Penn State University. In 2019 through 2022, Microsoft has named her a Most Valuable Professional (MVP) for her work in transforming the way the scientific community uses PowerPoint to convey their research. Melissa has also authored a new online course on LinkedIn Learning. Melissa’s workshops are lively, practical and transformational. For a sneak peek, check out her TED Talk, “Talk Nerdy to Me.” It’s been watched by over 2.5 million people (and counting). Materials URL: This comprehensive 1-day course equips scientists, engineers, researchers, and technical professionals to present their science in an understandable, memorable, and persuasive way. Through a dynamic combination of lecture, discussion, exercises, and video analysis, each participant will walk away with the skills, knowledge, and practice necessary to transform the way their work is presented. Five course objectives are covered: Transform the scientific presentations skills of participants. Enable participants to utilize effective strategies for content, structure, slide design and delivery of scientific presentations. Teach participants to analyze and adapt to their audience. Help participants understand which scientific details to emphasize in their presentation and which details to filter out. Equip participants to understand and enact the assertion evidence slide design in their own talks to make their scientific presentation slides more understandable, memorable, and engaging. Assist participants in developing an engaging and confident delivery style. * Attendees should bring a laptop with them to the session. *
	Room E	Short Course 4, Part 2 2 Plotting and Programming in Python Chasz Griego and Elif Dede Yildirim (The Carpentries) Materials URL: Plotting and Programming in Python Chasz Griego and Elif Dede Yildirim (The Carpentries) Show Bio Chasz Griego is an Open Science Postdoctoral Associate at the Carnegie Mellon University (CMU) Libraries. He received a PhD in Chemical Engineering from the University of Pittsburgh studying computational models to accelerate catalyst material discovery. He leads and supports Open Science teaching and research initiatives, particularly in the areas of reproducibility in computational research. His research involves investigating how open tools help promote reproducibility with computational research. He supports students and researchers at CMU with Python programming for data science applications, literate programming with Jupyter Notebooks, and version control with Git/GitHub. Elif Dede Yildirim is a data scientist within the Office of Data and Analytics at All of US Program, NIH. She leads the data quality workstream and support demo and driver projects. She holds MS degrees in Statistics and Child Development, and my PhD in Child Development from Syracuse University. She completed her postdoctoral work at the University of Missouri-Columbia and held a faculty appointment at Auburn University, where she taught graduate-level method and stats courses and provided statistical consulting. She is currently pursuing her second undergraduate degree in Computer Science at Auburn, and plan to graduate in December 2023. Materials URL: Plotting and Programming in Python is an introductory Python lesson offered by Software Carpentry. This workshop covers data analysis and visualization in Python, focusing on working with core data structures (including tabular data), using conditionals and loops, writing custom functions, and creating customized plots. This workshop also introduces learners to JupyterLab and strategies for getting help. This workshop is appropriate for learners with no previous programming experience.