Kirk is the Principal Data Scientist within the Strategic Innovation Group at Booz Allen Hamilton. He previously spent 12 years as Professor at George Mason University in the Computational and Data Sciences program. Before that, he worked 18 years on various NASA contracts, as research scientist and manager on large data systems. He has a PhD in Astrophysics from Caltech. He has applied his expertise in science and data systems as a consultant and advisor to numerous agencies and firms, focusing on the use of data for discovery, decision support, and innovation across many different domains and industries. He is an active contributor of data science and analytics information on social media, where he has been named consistently among the top worldwide influencers in big data and data science. Read his blog at http://rocketdatascience.org/

Shannon works in the healthcare and health policy domains, primarily focusing on causal analytics and information systems. Her projects have focused on patient decision-making, physician quality, consumer information processing, and the economics of regulation. She is a multi-method researcher with expertise in running human subjects experiments, econometrics, and machine learning. She holds a B.S. in Mathematics, Masters of Information Management, and is currently working toward her Ph.D. in Consumer Research and Information Systems.

Aaron focuses on deep learning, machine learning, and algorithm development. He has worked on solutions in a variety of domains, including pharmaceutical business analytics, image analysis for computed tomography, Next Generation Sequencing based forensic bioinformatics, cybersecurity strategy, and cloud based applications of deep learning. Aaron has led projects across multiple sectors including pharmaceutical, nonprofit, and government. He has authored numerous publications in physics and astrophysics journals and holds a patent in the area of forensic bioinformatics. He holds a Ph.D. in Physics from Ohio State University where he developed an analysis to search for dark matter in the Milky Way with the Fermi Space Telescope.

Marck designs data-driven solutions to help clients make better business decisions, recognize opportunities, experiment, gain insights, and solve difficult problems using large datasets and a combination of tools. His experience pulls from multiple disciplines including Internet, telecommunications, and high tech. Marck is an experienced R programmer and advocate, and a contributing author to The Bad Data Handbook and Analyzing the Analyzers. He teaches graduate level classes at George Washington University and George Mason University. Marck founded Data Community DC, an organization that promotes Data Science and Analytics practitioners in the Washington DC Metro area. He holds a B.S. in Mechanical Engineering from Boston University and an MBA from Vanderbilt University.

Vu is always thinking about the future. His work in predictive analytics helps to forecast unknown events. He likes to take on projects that have applications across multiple domains including energy, health care, and national security. His other professional and research interests include machine learning, deep learning, natural language processing and understanding, and computer vision. Vu enjoys general aviation, outdoor pursuits and foodie adventures. He holds a B.S. in Bioengineering and a Masters in Engineering Physics from the University of California, San Diego.