Dean Hougen

National Science Foundation, “Predictive Intelligence for Pandemic Prevention Phase I: Next Generation Surveillance Incorporating Public Health, One Health, and Data Science to Detect Emerging Pathogens of Pandemic Potential,” with David S. Ebert, Michael C. Wimberly, Jason R. Vogel, Thirumalai Venkatesan, Hank Jenkins-Smith, Chao Lan, Charles Nicholson, Talayeh Razzaghi, and Anni Yang, $1M, Jul 2022-Jan 2024.

National Science Foundation, “Computer Science Indigenous Community of Learners United to Develop, Excel, and Succeed (CS INCLUDES),” with Deborah A. Trytten, Heather J. Shotton, Natalie Youngbull, Deborah A. Moore-Russo, Randa L. Shehab, and Casey V. Haskins, $1.5M, Oct 2021-Sep 2026.

Air Force Sustainment Center, “Attention and Decision Hierarchies for Interpretable Auto Routing of Aircraft,” with John Antonio and Lacey Schley,” $476K, Sep 2021-Aug 2023.

Air Force Research Laboratory, “Smart Fusion for Command-and-Control Operations,” with John Antonio and Lacey Schley, $610K, Sep 2021-Dec 2022.

Economic Development Administration, “CARES Act Grant,” with Thomas Wavering, John Antonio, Lacey Schley, Joyce Burch, Brandt Smith, and Francisco Robles, $450K, Feb 2021-Feb 2024.

Universidad Nacional de San Agustín (UNSA), Peru, “Creation of the joint UNSA/OU Center for Monitoring and Control of Public Health for the Arequipa Region,” with David Ebert, Hank Jenkins-Smith, Charles Nicholson, and Carol Silva, $1.6M, Feb 2021-Dec 2023.

Boeing Defense and Space Group, “SkyNet: Artificial Intelligence and Machine Learning Improvements for Reconnaissance,” with Sridhar Radhakrishnan, Charles Nicholson, Christan Grant, and John Antonio, $88K, Aug 2020-Dec 2020.

Boeing Defense and Space Group, “Hydra: Implementation of Multi-Threaded Software for Reconnaissance,” with Sridhar Radhakrishnan, Christan Grant, Charles Nicholson, and John Antonio, $183K, Aug 2020-Dec 2020.

Oklahoma Department of Transportation, “Machine Learning for Transportation Short Course,” $50K, Nov 2019-Sep 2020.

Federal Aviation Administration, “Learner Data Management,” with Christan Grant, $139K, Jan 2017-Dec 2017.

Weathernews, Inc., “Coastal Assistant Prototype,” with Sridhar Radhakrishnan, Christan Grant, and Suleyman Karabuk, $101K, Jun 2016-Dec 2016.

Weathernews, Inc., “WxButler: An Artificial Intelligence Planning System for Maritime Logistics,” with Sridhar Radhakrishnan and Suleyman Karabuk, $60K, Jun 2015-Dec 2015.

National Science Foundation, “Mental Models and Creative Problem-Solving in Information Technology,” with Michael Mumford and Eric Day, $207K, Sep 2009-Aug 2011.

National Science Foundation, “Research Experiences for Undergraduates Site: Integrated Machine Learning Systems,” with Andrew H. Fagg, Amy McGovern, Rafael Fierro and Terran Lane, $311K, Feb 2008-Jan 2011.

National Science Foundation, “Development and Evaluation of a Work Practices Approach for Ethics Education in Science and Engineering,” with Michael Mumford, Frederick Carr, Mary Connelly, Teri Murphy, Morris Foster, Marguerite Keesee, and Robert Palmer, $210K, Oct 2005-Sep 2008.

National Science Foundation, "Research Experiences for Undergraduates Site: Embedded Machine Learning Systems,” with Andrew H. Fagg, Amy McGovern, Kelvin Droegemeier, and Terran Lane, $300K, Feb 2005-Jan 2008.

Army Research Office, “Adaptation and Learning at All Levels in Intelligent Robot Teams for Reconnaissance, Surveillance, and Battlefield Assessment,” with Sesh Commuri and Rafael Fierro, $812K, Jul 2003-Jun 2007.

• Alexander Stringer, Brian Sun, Lacey Schley, Dean F. Hougen, and John K. Antonio. “Causality-Aware Machine Learning for Path Correction.” Digital Avionics Systems Conference, 10 pages, September 2022. https://doi.org/10.1109/DASC55683.2022.9925875
• Sai Kiran Reddy Maryada, William Booker, Gopichandh Danala, Dean F. Hougen, and Bin Zheng. “Applying a Novel Two-Stage Deep-Learning Model to Improve Accuracy in Detecting Retinal Fundus Images.” SPIE Medical Imaging / Computer-Aided Diagnosis, February 2022. https://doi.org/10.1117/12.2611565
• Sai Teja Kanneganti, Jin-Song Pei, and Dean Frederick Hougen. “Developing Interpretable Machine Learning for Forward Kinematics of Robotic Arms.” IEEE Symposium Series on Computational Intelligence, 10 pages, December 2021. https://doi.org/10.1109/SSCI50451.2021.9660074
• Alexander Stringer, Brian Sun, Zackary Hoyt, Lacey Schley, Dean Hougen, and John K. Antonio. “SEDA: A Self-Explaining Decision Architecture Implemented Using Deep Learning for On-Board Command and Control.” Digital Avionics Systems Conference, 10 pages, October 2021. Best Paper in Session (Explainable AI). https://doi.org/10.1109/DASC52595.2021.9594351
• Jin-Song Pei, Dean F. Hougen, Sai Teja Kanneganti, Joseph P. Wright, Eric C. Mai, Andrew W. Smyth, Sami F. Masri, Armen Derkevorkian, François Gay-Balmaz, and Ludian Komini. “Interpretable Machine Learning for Function Approximation in Structural Health Monitoring.” Structural Health Monitoring Based on Data Science Techniques, Alexandre Cury, Diogo Ribeiro, Filippo Ubertini, and Michael Todd (eds.), Springer, 2021. http://dx.doi.org/10.1007/978-3-030-81716-9_18
• Audrey Reinert, Luke S. Snyder, Jieqiong Zhao, Andrew S. Fox, Dean F. Hougen, Charles Nicholson, and David S. Ebert. “Visual Analytics for Decision-Making During Pandemics.” Computing in Science and Engineering, 22(6): 48-59, 2020. https://dx.doi.org/10.1109/MCSE.2020.3023288
• Dean Frederick Hougen, Jin-Song Pei, and Sai Teja Kanneganti. “Toward Interpretable Machine Learning for Understanding Epidemic Data.” IEEE International Workshop on Fair and Interpretable Learning Algorithms, 6 pages, December 2020. https://doi.org/10.1109/BigData50022.2020.9377834
• Britt Richardson and Dean Frederick Hougen. “Districts by Demographics: Predicting U.S. House of Representative Elections using Machine Learning and Demographic Data.” IEEE International Conference on Machine Learning and Applications, 6 pages, December 2020. https://doi.org/10.1109/ICMLA51294.2020.00136
• Oluwatobi I. Ajagbe and Dean Frederick Hougen. “Evolution, Sympatric Speciation, and Risk Aversion.” IEEE Symposium Series on Computational Intelligence, 8 pages, December 2020. https://doi.org/10.1109/SSCI47803.2020.9308276
• Syed Naveed Hussain Shah and Dean Frederick Hougen. “Improved Stochastic Synapse Reinforcement Learning for Continuous Actions in Sharply Changing Environments.” IEEE/INNS International Joint Conference on Neural Networks, 8 pages, July 2020. https://doi.org/10.1109/IJCNN48605.2020.9207622
• Steven Aaron Roberts and Dean Frederick Hougen. “Information and Resource Sharing in Reinforcement Learning Agents Subject to Risk.” IEEE Symposium Series on Computational Intelligence, 8 pages, December 2019. https://doi.org/10.1109/SSCI44817.2019.9002914
• Syed Naveed Hussain Shah and Dean Frederick Hougen. “Rethinking Stochasticity in Neural Networks for Reinforcement Learning with Continuous Actions.” IEEE Symposium Series on Computational Intelligence, 8 pages, December 2019. https://doi.org/10.1109/SSCI44817.2019.9002826
• Dean Frederick Hougen and Syed Naveed Hussain Shah. “The Evolution of Reinforcement Learning.” IEEE Symposium Series on Computational Intelligence, 8 pages, December 2019. https://doi.org/10.1109/SSCI44817.2019.9003146
• Will Booker and Dean Frederick Hougen. “Meiotic Inheritance and Gene Dominance in Synthetic Sympatric Speciation.” IEEE Congress on Evolutionary Computation, 8 pages, July 2018. https://doi.org/10.1109/CEC.2018.8477761 Best Student Paper Award.
• Byran Hoke and Dean Frederick Hougen. “Nurturing Promotes the Evolution of Generalized Supervised Learning.” IEEE Congress on Evolutionary Computation, 8 pages, July 2018. https://doi.org/10.1109/CEC.2018.8477786
• Joohee Suh and Dean Frederick Hougen. “The Context-Aware Learning Model: neuro-experience-powered Logistic Regression Backpropagation (CALM-nepLRB).” IEEE/INNS International Joint Conference on Neural Networks, 8 pages, July 2018. https://doi.org/10.1109/IJCNN.2018.8489617
• Joohee Suh and Dean Frederick Hougen. “The Context-Aware Learning Model: experience-powered Logistic Regression Backpropagation (CALM-epLRB).” IEEE/INNS International Joint Conference on Neural Networks, 8 pages, July 2018. https://doi.org/10.1109/IJCNN.2018.8489380
• Joohee Suh and Dean F. Hougen. “The Context-Aware Learning Model: reward-based and experience-based Logistic Regression Backpropagation.” IEEE Symposium Series on Computational Intelligence, 8 pages, November 2017. https://doi.org/10.1109/SSCI.2017.8285401
• Syed Naveed Hussain Shah and Dean F. Hougen. “Nurturing Promotes the Evolution of Reinforcement Learning in Changing Environments.” IEEE Symposium Series on Computational Intelligence, 8 pages, November 2017. https://doi.org/10.1109/SSCI.2017.8285400
• Syed Naveed Hussain Shah and Dean F. Hougen. “Stochastic Synapse Reinforcement Learning (SSRL).” IEEE Symposium Series on Computational Intelligence, 8 pages, November 2017. https://doi.org/10.1109/SSCI.2017.8285425
• Joohee Suh and Dean F. Hougen. “Context-based Adaptive Robot Behavior Learning Model (CARB-LM).” IEEE Symposium Series on Computational Intelligence, 8 pages, December 2014. https://dx.doi.org/10.1109/CICA.2014.7013253
• David R. Peterson, Jamie D. Barrett, Kimberly S. Hester, Issac C. Robledo, Dean F. Hougen, Eric A. Day, and Michael D. Mumford. “Teaching People to Manage Constraints: Effects on Creative Problem-Solving.” Creativity Research Journal, 25(3): 335-347, 2013. https://dx.doi.org/10.1080/10400419.2013.813809
• Jamie D. Barrett, David R. Peterson, Kimberly S. Hester, Issac C. Robledo, Eric A. Day, Dean F. Hougen, and Michael D. Mumford. “Thinking About Applications: Effects on Mental Models and Creative Problem-Solving” Creativity Research Journal, 25(2): 199-212, 2013. https://dx.doi.org/10.1080/10400419.2013.783758
• Brent E. Eskridge and Dean F. Hougen. “Nurturing Promotes the Evolution of Learning in Uncertain Environments.” The Second Joint IEEE International Conference on Development and Learning / Epigenetic Robotics Conference, 6 pages, unnumbered, November 2012. https://dx.doi.org/10.1109/DevLrn.2012.6400847
• Armand Leonce, Bryan Hoke, and Dean F. Hougen. “Evolution of Robot-to-Robot Nurturing and Nurturability.” The Second Joint IEEE International Conference on Development and Learning / Epigenetic Robotics Conference, 7 pages, unnumbered, November 2012. https://dx.doi.org/10.1109/DevLrn.2012.6400852
• Mark Woehrer, Dean F. Hougen, Ingo Schlupp, and Brent E. Eskridge. “Robot-to-Robot Nurturing: A Call to the Research Community.” The Second Joint IEEE International Conference on Development and Learning / Epigenetic Robotics Conference, 2 pages, unnumbered, November 2012. https://dx.doi.org/10.1109/DevLrn.2012.6400859
• Mark Woehrer, Dean Hougen, and Ingo Schlupp. “Sexual Selection, Resource Distribution, and Population Size in Synthetic Sympatric Speciation.” International Conference on the Synthesis and Simulation of Living Systems, pages 137-144 July 2012. https://dx.doi.org/10.7551/978-0-262-31050-5-ch020 Best Paper Award.