Samuel H. Bean is a radar engineer at the National Air and Space Intelligence Center with experience in machine learning, image processing, and computer vision. He has a B.S. in Computer Science from the University of Michigan where he conducted research comparing machine learning algorithm variants and completed graduate level courses. Previously, he taught undergraduate courses and led student research projects utilizing machine learning algorithms.

1. Samuel H. Bean
2384 Mallard Ln Bevercreek, OH 45431
 (248) 933-4108 |  Samuel.H.Bean@gmail.com |  Sam-Bean-711a96a4
Education
University of Michigan - Ann Arbor Ann Arbor, MI
B.S.E. COMPUTER SCIENCE September 2010 - June 2016
• Successfully completed graduate level courses in user Machine Learning, Artificial Intelligence, Probabilistic Graph-
ical Models, Information Retrieval and Web Search, and Web Development
• Member of two research teams that investigated pertinent and current problems in machine learning such as utiliz-
ing supervoxel segmentation for video labeling and detecting deceptive speech based on raw audio signals
Experience
National Air and Space Intelligence Center Wright-Patterson AFB, OH
RADAR ENGINEER August 2016 - current
• Interfaced with contracting software development firms to best utilize government capital
• Developed machine learning, image processing, and computer vision solutions for analysis of threat radar in Matlab
and Python
• Leads team of new employees to best understand new work responsibilities and tasking
• Collaborates with government working groups forging modernization efforts for many intelligence needs
University of Michigan - Ann Arbor Ann Arbor, MI
INSTRUCTIONAL AIDE January 2013 - May 2015
• Taught undergraduate courses of more than 20 students focusing on FPGA programming, microprocessor organiza-
tion, and assembly language
• Instructed students during discussion sections, office hours, and labs
Research
University of Michigan - Ann Arbor Ann Arbor, MI
COMPARING RESTRICTED BOLTZMANN MACHINE VARIANTS IN THE GENERATIVE SETTING January 2015 - April 2015
• Proposed research concept of comparing algorithm variants in data generation setting
• Implemented state of the art machine learning algorithm from scratch in Python
• Wrote graduate level research paper on results and findings making observations on mathematical design never
before discovered in relevant literature
University of Michigan - Ann Arbor Ann Arbor, MI
SENTENCE COMPLETION USING A SPARSE DENOISING AUTOENCODER October 2013 - December 2013
• Proposed project and led a team of 4 to develop an original use for an algorithm
• Delegated tasks to team members and collaborated to resolve conflicts in order to complete the project in a timely,
effective manner
Skills
• Languages:
C++, C, Swift, Pyton, Matlab Javascript, Java, HTML, CSS
• Applications:
Eclipse, Matlab, Canopy, Weka, XCode, Android Studio
• Platforms:
Windows XP - 10, OSX 10, Linux
• Hardware:
Programmed and debugged Altera FPGA boards with Verilog HDL