Lauren has more than 14 years of experience in the life sciences industry with a focus on creating and delivering innovative solutions for the acquisition, processing, computation, storage and visualization of complex scientific data. Lauren helps federal agencies as well as commercial pharmaceutical companies implement data-driven approaches for medical product development. Prior to joining Booz Allen, Lauren was a fellow at the FDA Center for Drug Evaluation and Research (CDER).

Charles specializes in algorithm analysis and machine learning. He helps clients discover insights from large data sets and shift from traditional infrastructures to cloud computing environments. He received his BS from Morehouse College and his MS and PhD from the University of Maryland. His work was covered in the July 2014 issue of Ebony magazine where Data Science was noted as the most growing job in the country.

Drew helps his clients solve problems related to large-scale analytics, distributed computing and machine learning. He has implemented a wide variety of text exploration, management and retrieval applications, combining natural language processing, classification and visualization techniques. He holds a master’s degree in Information Resource Management from Syracuse University’s iSchool and a BFA in Computer Graphics.

Kirk is the Principal Data Scientist 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 to numerous agencies and firms, focusing on the use of data for discovery, decision support, and innovation.

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.

Anna Fernandez has worked in data science since graduate school, working with large medical array RF data to develop novel ultrasound imaging techniques and translate research findings into medical device products. At Booz Allen, she leads informatics and IT teams to develop discovery and analytic platforms in precision medicine oncology and cardiology. She holds a B.S. in Electrical Engineering and an M.S. and Ph.D. in biomedical engineering, and is a faculty member at the University of Maryland University College Cybersecurity Program.

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. Marck is an experienced R programmer and advocate, and a contributing author to The Bad Data Handbook and Analyzing the Analyzers. He holds a B.S. in Mechanical Engineering from Boston University and an MBA from Vanderbilt University.

Vu works in predictive analytics to help forecast unknown events 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. He holds a B.S. in Bioengineering and a Master’s in Engineering Physics from the University of California, San Diego.

Jonathan works on a wide variety of solutions, from optimizing resource distribution and logistics to big data anomaly detection. He helped communicate the results of the Second Annual Data Science Bowl to the medical community, and assisted with the development of this year’s Data Science Bowl tutorial. Jonathan has a PhD in physics from the University of Virginia and worked for 8 years in experimental nuclear physics before joining Booz Allen.

Jared Sylvester is a consultant/machine learning researcher/data scientist at Booz Allen Hamilton. In 2014, he received his doctorate in Computer Science from the University of Maryland, focusing on biologically-inspired AI. While at the University of Maryland, Jared also worked at the Center for Complexity in Business, applying data-driven computational techniques to marketing, finance, and other business domains.

Samantha Tracht joined Booz Allen Hamilton in March 2015 as a Lead Data Scientist, helping government clients migrate legacy and current data into a data lake based in Hadoop. She creates models leveraging deep learning techniques for outcomes like mitigating online authentication fraud. Prior to joining Booz Allen she worked with the Watson Implementations Group at IBM. She received her master’s degree in mathematics from the University of Tennessee in 2013.