Kenneth Sullivan has over 10 years of experience in research and development of computer vision, image analysis, and machine learning software. He has built frameworks for large-scale image/video search, video activity recognition, object classification, geolocation, and malware detection. He is currently an Imaging Consultant at Epic Sciences designing approaches for automatic cancer cell detection using deep learning.

1. Kenneth Sullivan
12650 Carmel Country Road #107, San Diego, CA, 92130
sullivak@gmail.com
Summary
An experienced researcher and developer, I write software to solve problems in computer vision, image and video
analysis, and machine learning. I have more than 10 years of experience in research and development both as an
individual contributor and as a technical lead. During this time I have built frameworks to perform large scale
image/video content-based search, video activity recognition, object classification, automated geolocation, saliency
based compression, and malware detection. In addition to technical development, I have demonstrated writing and
presentation skills to win competitive contracts and communicate ideas with colleagues and customers.
Work Experience
Imaging Consultant Epic Sciences, San Diego, CA Feb 2016-Present
• Designing and implementing new approaches for automatic detection of cancer cells using deep neural
networks.
Research Staff Member Mayachitra, Inc., Santa Barbara, CA Sep 2005-Jan 2016
• Delivered fully functional software systems including an end-to-end image and video content-based search
system, a video activity analysis pipeline, and a saliency-enhanced video compression tool.
• Won several image and video analysis projects from competitive calls-for-proposals (BAA grant, SBIR, and
subcontract) and led these projects as principal investigator (PI).
• Consistently won optional contract extensions and phases based on technical performance for projects led.
• Developed algorithms in computer vision, image processing, and high-dimensional indexing, including
methods for content-based search, object classification, activity recognition, and indexing of large databases
of multimedia descriptors.
Grad Student Researcher University of California, Santa Barbara 2001-2005
• Researched and published novel approaches to digital image steganalysis (detection of hidden data in images)
extending the state-of-the-art.
Teaching Assistant University of California, Santa Barbara 2001, 2005
• Conducted laboratory and discussion classes in upper-division classes: Digital Signal Processing, Analog
Communications, and Network Synthesis. Supervised and assisted students with labs and homework.
HW/SW Engineer Tiernan Communications Inc., San Diego, CA 1998-2000
• Designed hardware (PCB, FPGA) and software (C, C++ firmware) for commercial broadcast equipment.
• Developed hardware and software for new MPEG-2 IRD (integrated receiver and decoder), both cheaper to
build and with more features then previous models.
Education
Doctor of Philosophy in Electrical Engineering 2005 • University of California, Santa Barbara
Thesis: “Image Steganalysis: Hunting and Escaping”. Dissertation topic is steganalysis, the detection of data
hidden in images, a field marrying elements of image analysis, statistics, and communications. Advisors: Profs.
Manjunath, Madhow, and Chandrasekaran.
Master of Science in Electrical Engineering 2002 • University of California, Santa Barbara
Focus on image and signal processing, and communications (emphasis on source and channel coding).
Bachelor of Science in Electrical Engineering, Minor in Economics 1998 • University of California, San
Diego
Skills
• Languages (from very comfortable to played with): C++, Python, C, MATLAB, Objective-C
• Libraries/frameworks: OpenCV, NumPy/SciPy, PostgreSQL, Caffe, HDF5, Boost, Hadoop, Accumulo,
IPP
Projects
Active Learning Based Scalable Representations for Event Understanding in Video, ONR Project
• 9/2015 to 1/2016, developed deep neural network models for classification and segmentation of overhead
images/video.
VIRAT (Video Image Retrieval and Analysis Tool), DARPA Project
• Led project team (2-6 people) as Mayachitra Principal Investigator (PI), 10/2008 to 10/2013. Project goal
was implementation and integration of fast large-scale search of high-dimensional descriptor feature vectors
generated from various groups.
• Designed and implemented large-scale video activity indexing database in C++ from ground up.
• Won Phase II award based on performance (speed and accuracy), competing against two other teams.
• Ported single-server system to distributed processing on Hadoop distributed system exploiting MapReduce
algorithm, for scalability and integration with customer systems.
• For further details, see indexing section here http://www.kitware.com/virat avss 2015.html

2. MalSee: Using vision, hearing, and other features to detect malware, ONR SBIR
• Principal Investigator, 7/2015 to 1/2016. Project goal is to use novel, unique approaches to malware
classification.
• Built initial prototype for vision-based malware detection able to correctly classify malware families of new
variants with 98% accuracy.
MIECS: Motion Imagery Exploitation with Compressive Sensing, ONR STTR
• Principal Investigator, 7/2013 to 7/2015. Project goal is reduction of required video bandwidth for Navy
drones, in some cases where processing is too limited for traditional compression.
• Halved required bandwidth by developing novel methods for saliency based compression.
Mixed Initiative Multiple Cue Image Localization (Finder), IARPA Project
• Principal Investigator at Mayachitra, 4/2012 to 12/2014. Project goal is determining location of unknown
query image/video. Mayachitra team focus is matching to public datasets of images with known locations.
• Built geolocalizing image/video search system comparing query to large database of public images (Flickr,
Panoramio, etc.) covering large geographic regions (roughly 100 km2
). Output is heatmap of likely locations
and top matches.
AVAAMO (Automated Video Activity Analysis for Military Operations), DARPA STTR
• Principal Investigator, 1/2009 to 6/2013. Project goal was development of video activity analysis methods,
especially for unmanned aerial vehicles (UAV).
• Successfully won competitive Phase 2 award based on Phase 1 performance.
• Developed novel video activity descriptors with unique features compared to state-of-the-art. In the second
phase, added these to existing end-to-end video search system to retrieve untrained activities from large video
databases.
Learning a Visual Thesaurus and Applications, ONR Project
• Principal Investigator, 10/2006 to 12/2009. Project goal was scene understanding of aerial imagery. This
includes automatic detection and classification, that is, the ability to both locate and categorize regions of
interest within very large geographical regions.
• In completing project goals, built end-to-end software system for content-based image search of aerial images,
including descriptor extraction, search algorithms, and GUI (Cocoa).
• Developed novel method to automatically use spatial context to improve results of classification.
Other projects
• Automatic scene understanding and generation of textual descriptions, ONR SBIR 3/2009 to
3/2011, developed methods for detection and classification of objects in aerial imagery.
• Geospatial Representation and Analysis (GEO*), DARPA Project 9/2005 to 10/2009, built
prototype search engine for aerial imagery.
• Multimedia data hiding, ONR Project 6/2001 to 9/2005, while at UCSB, developed steganalysis
methods extending the state-of-the-art, found theoretical limits to safe (undetectable) hiding, and proposed
new hiding methods resistant to steganalysis.
Academic Community Involvement
• Program Co-Chair for Information Hiding 2008, Member of Program Committee for Information Hiding
conference
• Reviewed papers for IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI), IEEE Trans. Signal
Processing, IEEE Trans. Information Forensics and Security, ACM Multimedia, ACM Workshop on
Multimedia and Security, IEEE Int’l Conference on Image Processing, SPIE Electronic Imaging, and others.