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 the field of biologically-inspired AI. His dissertation topic was executive function and cognitive control (working memory, decision making, etc.) using neural-inspired systems rather than rule-based ones—essentially trying to get computers to act in ways that are a little more like our brains and also get artificial neural networks to act a little more like traditional computers. 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. He has also done research in fields including data mining, biometrics, circuit design, cognitive psychology, finance, social networks, and marketing.

Samantha Tracht joined Booz Allen Hamilton in March 2015 as a Lead Data Scientist. Since then she has been working on helping government clients migrate legacy and current data into a data lake based in Hadoop and most recently she has been creating models and analyzing data to determine online authentication fraud. Prior to joining Booz Allen she worked with the Watson Implementations Group at IBM creating prognostic models and performing analytics for healthcare clients and cancer research centers. Samantha is particularly interested in healthcare and national security applications of machine learning. She received her masters degree in mathematics from the University of Tennessee in 2013, where she focused on infectious disease modeling. This is her first Kaggle competition.

Peter VanMaasdam has been performing research, development, and implementation of Computer Vision and Machine Learning technologies for the past 18 years, and has been with Booz Allen Hamilton since July 2015. Prior to Booz Allen, he was employed with Northrop Grumman Electronic Systems and Raytheon (Hughes) Missile Systems, where he worked on a variety of advanced programs in both internal research and client funded efforts, including such technologies as Full Motion Video and Wide Area Motion Imaging exploitation, 3D Laser Radar exploitation, Infrared (Midwave and Longwave) and Synthetic Aperture Radar Automated Target Recognition, and several missile defense tracking and discrimination programs. These efforts included initial algorithm design, implementation in embedded systems, and field testing. He has a Bachelor’s Degree in Aerospace Engineering from Purdue University, a Master’s Degree in Electrical Engineering from the University of Southern California, and a Post Master’s Certificate from Johns Hopkins University. View his Kaggle profile.

Jon Barker joined NVIDIA in May 2015 as a Solution Architect. Since then he has been helping customers to design, implement and optimize a variety of GPU accelerated deep learning applications and has also provided internal and external deep learning training. Jon is particularly focused on the application of deep learning to problems in defense and national security. Jon graduated from the University of Southampton in the UK in 2007 with a PhD in Mathematics. Prior to joining NVIDIA, Jon worked for the UK Ministry of Defence and spent four years on secondment to the US Department of Defense where he was a research scientist focused on data analytics and machine learning for multi-sensor data streams. In order to learn new data science skills Jon has been a long time competitor on Kaggle. View his Kaggle profile.

Maxim Milakov joined NVIDIA in January 2012. Since then he has been working on accelerating scientific and industrial applications on GPUs in various fields including machine learning, developing codes and algorithms to maximize performance while also collaborating with internal teams to help co-design future hardware and tools. Maxim graduated from Moscow State University’s Computer Science department in 1998. Before joining NVIDIA, Maxim worked for CBS Interactive as a software architect in the R&D department. With a passion for deep learning in general and convolutional neural networks in particular, Maxim practices his skills at Kaggle, placing well in a number of competitions. View his Kaggle profile.