Richard Heimann is a technical fellow and Chief Data Scientist at L-3 National Security Solutions (NSS) (NYSE:LLL), and is also an EMC-certified data scientist with concentrations in spatial statistics, data mining, and Big Data. Richard also leads the data science team at the L-3 Data Tactics Business Unit. L-3 NSS and L-3 Data Tactics are both premier Big Data and analytics service providers based in Washington DC and serve customers globally.
Richard is an adjunct professor at the University of Maryland, Baltimore County, where he teaches Spatial Analysis and Statistical Reasoning. Additionally, he is an instructor at George Mason University, teaching Human Terrain Analysis; he is also a selection committee member for the 2014-2015 AAAS Big Data and Analytics Fellowship Program and member of the WashingtonExec Big Data Council.
Richard has recently published a book titled Social Media Mining with R, Packt Publishing. He recently supported DARPA, DHS, the US Army, and the Pentagon with analytical support.
Sarah Kelley is a junior Python developer and aspiring data scientist. She currently works at a start-up in Bethesda, Maryland, where she spends most of her time on data ingestion and wrangling. Sarah holds a Master's degree in Education from Seattle University. She is a self-taught programmer who became interested in the field through her desire to inspire her students to pursue careers in Mathematics, Science, and technology.
Liang Shi received his PhD in Computer Science and a Master's degree in Statistics from the University of Georgia in 2008 and 2006, respectively. His PhD study is on Machine Learning and AI, mainly solving surrogate model-assisted optimization problems. After graduation, he joined the Data Mining Research team at McAfee; his job was to detect network threats through machine-learning approaches based on Big Data and cloud computing platforms. He later joined Microsoft as a software engineer, and continued his security research and development leveraged by machine-learning algorithms, basically for online advertisement fraud detection on very large, real-time data scales. In 2012, he rejoined McAfee (Intel) as a senior researcher, conducting network threat research, again with the help of machine-learning and cloud computing techniques. Early this year, he joined Pivotal as a senior data scientist; his work is mainly on data scientist projects with clients of popular companies, mainly for IT and security data analytics. He is very familiar with statistical and machine-learning modeling and theories, and he is proficient with many programming languages and analytical tools. He has several journal- and conference-proceeding publications, and he also published a book chapter.
Will Voorhees is a software developer with experience in all sorts of interesting things from mobile app development and natural language processing to infrastructure security. After teaching English in Austria and bootstrapping an education technology start-up, he moved to the West Coast, joined a big tech company, and is now happily working on infrastructure security software used by thousands of developers.
In his free time, Will enjoys reviewing technical books, watching movies, and convincing his dog that she's a good girl, yes she is.