David Ebert

Professor of CS and ECE Gallogly Chair #3
Associate VP of Research and Partnerships Director
Data Institute for Societal Challenges

Email: ebert@ou.edu
Phone: (405) 325-4275
Office: 5 Partners Pl – 201 Stephenson Pkwy, Ste. 4600

Curriulum Vitae

Education
Ph.D., Computer Science
The Ohio State University
M.S., Computer Science
The Ohio State University
B.S., Computer Science
The Ohio State University

Research Focus

Visual analytics, human-computer teaming, trustable AI, fundamental data science advances with targeted impact in energy and climate resiliency and sustainability, defense and security, health and digital humanities.

Experience and Awards

Dr. Ebert was recently awarded the National Science Foundation's ART: Intensifying Translation of Research in Oklahoma (InTRO) grant.
Silicon Valley Professor of Electrical and Computer Engineering
Director, U.S. DHS Center of Excellence in Visual Analytics
Director, Center for Education and Research in Information Assurance and Security
Director, Purdue University Visual Analytics Center
Associate Professor, School of Electrical and Computer Engineering,
Purdue University
IEEE Fellow
Entrepreneur Leadership Academy Fellow
University Faculty Scholar
IEEE Computer Society Leadership
IEEE Computer Society vgTC Visualization Academy
IEEE Computer Society vgTC Technical Achievement Award
United States Coast Guard Certificate of Merit, VACCINE Social Media Analytics and Reporting Toolkit Project Team
U. S. Coast Guard Meritorious Team Commendation, U.S. Coast Guard, Port Resilience for Operational Tactical Enforcement to Combat Terrorism (PROTECT) Team
Impact Award, DHS S&T
Award of Excellence, DHS S&T

Biography

Dr. Dean F. Hougen is an associate professor in the School of Computer Science at the University of Oklahoma as well as the interim director of the School. Dr. Hougen has a PhD in Computer Science and Engineering from the University of Minnesota, with a graduate minor in Cognitive Science, and a BS in Computer Science from Iowa State University with minors in Philosophy and Mathematics. His primary research involves artificial intelligence (AI), particularly robotics and machine learning (ML), focusing on artificial neural networks and deep learning; interpretable, informed, informative, and interactive machine learning; the evolution of learning; distributed, heterogeneous, multi- agent robotic systems and situated learning in real robotic systems; reinforcement learning, connectionist learning, and evolutionary computation. He also has strong interests in computer science education and ethics. He has also worked in the areas of expert systems, decision support systems, geographic information systems, data compression, and user interfaces. Dr. Hougen has jointly secured grant and contract awards in excess of $20M since coming to OU in 2001 and has more than 100 refereed publications in the areas of artificial intelligence, machine learning, robotics, ethics, and computer science education during his career. He has more than thirty years of experience developing AI/ML systems and fielded software and hardware systems including OU’s first official iPhone application OU2GO in Summer 2009. He has advised dozens of doctoral and masters students, as well as countless undergraduates. In 2022, Dr. Hougen was awarded the Lloyd and Joyce Austin Presidential Professorship in honor of his excellence in scholarship and teaching.

Selected Patents

US Patent No. 8,924,332 – “Public safety camera identification and monitoring system and method,” issued on July 2, 2019.
US Patent No. 8,924,332 – "Forecasting Hotspots Using Predictive Visual Analytics," issued on December 30, 2014.
US Patent No. 8,882,664 – "Animal Symptom Visual Analytics," issued on November 11, 2014.
US Patent No. 8,849,728 – "Visual Analytics Law Enforcement Tools," issued on September 30, 2014.

Selected Publications

Snyder, L. S., Lin, Y.-S., Karimzadeh, M., Goldwasser, D., & Ebert, D. S., “Interactive Learning for Identifying Relevant Tweets to Support Real-time Situational Awareness,” IEEE Transactions on Visualization and Computer Graphics, to appear 2020.
Zhao, J., Karimzadeh, M., Snyder, L. S., Surakitbanharn, C., Qian, Z. C., & Ebert, D. S., “MetricsVis: A Visual Analytics System for Evaluating Employee Performance in Public Safety Agencies,” IEEE Transactions on Visualization and Computer Graphics, to appear 2020.
Khayat, M., Karimzadeh, M., Ebert, D. S., Ghafoor, A. “The Validity, Generalizability and Feasibility of Summative Evaluation Methods in Visual Analytics,” IEEE Transactions on Visualization and Computer Graphics, to appear 2020.
Khayat, M., Karimzadeh, M., Zhao, J., Ebert, D. S. “VASSL: A Visual Analytics Toolkit for Social Spambot Labeling,” IEEE Transactions on Visualization and Computer Graphics, to appear 2020.
Lee, C., Jin, S. Kim, D., Maciejewski, R., Ebert, D., Ko, S., “An Visual Analytics System for Exploring, Monitoring, and Forecasting Road Traffic Congestion,” IEEE Transactions on Visualization and Computer Graphics, 2019.
Chen, M., Ebert, D., “An Ontological Framework for Supporting the Design and Evaluation of Visual Analytics Systems,” Computer Graphics Forum (Proc. IEEE EuroVis 2019), 2019.