Education

Ph.D. in Computer Science, Brigham Young University, 1998

M.S. in Computer Science, Brigham Young University, 1995

B.S. in Computer Science, Brigham Young University, 1992

Experience

Brigham Young University, Associate Professor of Computer Science, 9/07 to present

Brigham Young University, Assistant Professor of Computer Science, 7/01 to 8/07

Penn State University, Graduate Faculty of Computer Science and Engineering, 3/00 to 7/01

Penn State University, Applied Research Laboratory, Research Associate, 9/99 to 7/01

fonix Corporation, Research Scientist, 7/98 to 9/99

Book Chapters

Alexandr Ezhov and Dan Ventura, "Quantum Neural Networks", in Future Directions for Intelligent Systems and Information Science (Ed. N. Kasabov), Physica-Verlag, 2000.

Journal Articles

Kaivan Kamali, Dan Ventura, Amulya Garga and Soundar Kumara, "Geometric Task Decomposition in a Multi-agent Environment", Applied Artificial Intelligence, 20(5): 437-456, 2006.

Jonathan Dinerstein, Dan Ventura and Parris Egbert, "Fast and Robust Incremental Action Prediction for Interactive Agents", Computational Intelligence, 21(1): 90-110, 2005.

John Howell, John Yeazell and Dan Ventura, "Optically Simulating a Quantum Associative Memory", Physical Review A, vol. 62, article 42303, 2000.

Alexandr Ezhov, A. Nifanova, and Dan Ventura, "Distributed Queries for Quantum Associative Memory", Information Sciences, vol. 128 nos. 3-4, pp. 271-293, 2000.

Dan Ventura and Tony Martinez, "Quantum Associative Memory", Information Sciences, vol. 124 nos. 1-4, pp. 273-296, 2000.

Dan Ventura and Tony Martinez, "Initializing the Amplitude Distribution of a Quantum State", Foundations of Physics Letters, vol. 12 no. 6, pp. 547-559, 1999.

Dan Ventura, "Quantum Computational Intelligence: Answers and Questions", IEEE Intelligent Systems, vol. 14 no. 4, pp. 14-16, 1999.

Conference Papers

Rob Van Dam and Dan Ventura, "ADtrees for Sequential Data and N-gram Counting", Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, to appear, 2007.

Sabra Dinerstein, Jonathan Dinerstein and Dan Ventura, "Robust Multi-Modal Biometric Fusion via SVM Ensemble", Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, to appear, 2007.

Jared Lundell and Dan Ventura, "A Data-dependent Distance Measure for Transductive Instance-based Learning", Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, to appear, 2007.

Neil Toronto, Bryan Morse, Dan Ventura, Kevin Seppi, "The Hough Transform's Implicit Bayesian Foundation", Proceedings of the IEEE International Conference on Image Processing, to appear, 2007.

Jake Merrell, Dan Ventura and Bryan Morse, "Clustering Music via the Temporal Similarity of Timbre", IJCAI Workshop on Artificial Intelligence and Music, pp. 153-164, 2007.

Nancy Fulda and Dan Ventura, "Predicting and Preventing Coordination Problems in Cooperative Q-learning Systems", Proceedings of the International Joint Conference on Artificial Intelligence, pp. 780-785, 2007.

Jonathan Dinerstein, Dan Ventura and Parris Egbert, "Learning Policies for Embodied Virtual Agents Through Demonstration", Proceedings of the International Joint Conference on Artificial Intelligence, 1257-1262, 2007.

Eric Goodman and Dan Ventura, "Spatiotemporal Pattern Recognition via Liquid State Machines", Proceedings of the International Joint Conference on Neural Networks, pp. 7579-7584, July 2006.

Neil Toronto and Dan Ventura, "Learning Quantum Operators from Quantum State Pairs", Proceedings of the IEEE Congress on Evolutionary Computation, pp. 9157-9162, July 2006.

David Norton and Dan Ventura, "Preparing More Effective Liquid State Machines Using Hebbian Learning", Proceedings of the International Joint Conference on Neural Networks, pp. 8359-8364, July 2006.

Nancy Fulda and Dan Ventura, "Learning a Rendezvous Task with Dynamic Joint Action Perception", Proceedings of the International Joint Conference on Neural Networks, pp. 627-632, July 2006.

Neil Toronto, Dan Ventura and Bryan Morse, "Edge Inference for Image Interpolation", Proceedings of the International Joint Conference on Neural Networks, pp. 1782-1787, July 2005.

Adam Drake and Dan Ventura, "Practical Fourier-based Learning", Proceedings of the International Conference on Machine Learning, pp. 185-192, August 2005.

Neil Toronto, Dan Ventura and Bryan Morse, "Edge Inference for Image Interpolation", Proceedings of the International Joint Conference on Neural Networks, pp. 1782-1787, July 2005.

Eric Goodman and Dan Ventura, "Effectively Using Recurrently Connected Spiking Neural Networks", Proceedings of the International Joint Conference on Neural Networks, pp. 1542-1547, July 2005.

Adam Drake and Dan Ventura, "Comparing High-Order Binary Features", Proceedings of the Joint Conference on Information Sciences, pp. 428-431, July 2005.

Eric Goodman and Dan Ventura, "Time Invariance and Liquid State Machines", Proceedings of the Joint Conference on Information Sciences, pp. 420-423, July 2005.

Nancy Fulda and Dan Ventura, "Incremental Policy Learning: An Equilibrium Selection Algorithm for Reinforcement Learning Agents with Common Interests", Proceedings of the International Joint Conference on Neural Networks, pp. 1121-1126, July 2004.

Mark Richards and Dan Ventura, "Choosing a Starting Configuration for Particle Swarm Optimization", Proceedings of the International Joint Conference on Neural Networks, pp. 2309-2312, July 2004.

Stephen Whiting and Dan Ventura, "Learning Multiple Correct Classifications from Incomplete Data using Weakened Implicit Negatives", Proceedings of the International Joint Conference on Neural Networks, pp. 2953-2958, July 2005.

Bob Ricks and Dan Ventura, "Training a Quantum Neural Network", Neural Information Processing Systems, pp. 1019-1026, December 2003.

Nancy Fulda and Dan Ventura, "Target Sets: A Tool for Understanding and Predicting the Behavior of Interacting Q-learners", Proceedings of the International Joint Conference on Information Sciences, pp. 1549-1552, September 2003.

Mark Richards and Dan Ventura, "Dynamic Sociometry in Particle Swarm Optimization", Proceedings of the International Joint Conference on Information Sciences, pp. 1557-1560, September 2003.

Nancy Fulda and Dan Ventura, "Dynamic Joint Action Perception for Q-Learning Agents", Proceedings of the International Conference on Machine Learning and Applications, pp. 73-78, June 2003.

Nancy Fulda and Dan Ventura, "Concurrently Learning Neural Nets: Encouraging Optimal Behavior in Cooperative Reinforcement Learning Systems", Proceedings of the IEEE International Workshop on Soft Computing Techniques in Instrumentation, Measurement, and Related Applications, pp. 2-5, May 2003.

Dan Ventura, "Probabilistic Connections in Relaxation Networks", Proceedings of the International Joint Conference on Neural Networks, pp.934-938, May 2002.

Dan Ventura, "Pattern Classification Using a Quantum System", Proceedings of the Joint Conference on Information Sciences, pp.537-640, March 2002.

Dan Ventura, "A Quantum Analog to Basis Function Networks", Proceedings of the International Conference on Computing Anticipatory Systems, pp. 286-295, August 2001.

Dan Ventura, "On the Utility of Entanglement in Quantum Neural Computing", Proceedings of the International Joint Conference on Neural Networks, pp. 1565-1570, July 2001.

Dan Ventura, "Learning Quantum Operators", Proceedings of the Joint Conference on Information Sciences, pp. 750-752, March 2000.

Dan Ventura, "Implementing Competitive Learning in a Quantum System", Proceedings of the International Joint Conference on Neural Networks, paper 513 (CD-ROM), July 1999.

Dan Ventura, D. Randall Wilson, Tony Martinez and Brian Moncur, "A Neural Model of Centered Tri-gram Speech Recognition", Proceedings of the International Joint Conference on Neural Networks, paper 2188 (CD-ROM), July 1999.

D. Randall Wilson, Dan Ventura, Tony Martinez and Brian Moncur, "The Robustness of Relaxation Rates in Constraint Satisfaction Networks", Proceedings of the International Joint Conference on Neural Networks, paper 162 (CD-ROM), July 1999.

Dan Ventura and Tony Martinez, "A Quantum Associative Memory Based on Grover's Algorithm", Proceedings of the International Conference on Artificial Neural Networks and Genetic Algorithms, pp. 22-27, April 1999.

Dan Ventura, "Artificial Associative Memory using Quantum Processes", Proceedings of the Joint Conference on Information Sciences, vol. 2, pp. 218-221, October 1998.

Dan Ventura and Tony Martinez, "Quantum Associative Memory with Exponential Capacity", Proceedings of the International Joint Conference on Neural Networks, pp. 509-513, May 1998.

Dan Ventura and Tony Martinez, "Optimal Control Using a Neural/Evolutionary Hybrid System", Proceedings of the International Joint Conference on Neural Networks, pp. 1036-1041, May 1998.

Dan Ventura and Tony Martinez, "Using Evolutionary Computation to Facilitate Development of Neurocontrol", Proceedings of the International Workshop on Neural Networks and Neurocontrol, August 1997.

Dan Ventura and Tony Martinez, "An Artificial Neuron with Quantum Mechanical Properties", Proceedings of the International Conference on Artificial Neural Networks and Genetic Algorithms, pp. 482-485, April 1997.

Dan Ventura and Tony Martinez, "A General Evolutionary/Neural Hybrid Approach to Learning Optimization Problems", Proceedings of the World Congress on Neural Networks, pp. 1091-5, September 1996.

Dan Ventura and Tony Martinez, "Concerning a General Framework for the Development of Intelligent Systems", Proceedings of the International Conference on Artificial Intelligence, Expert Systems and Neural Networks, pp. 44-47, August 1996.

Dan Ventura and Tony Martinez, "Robust Optimization Using Training Set Evolution", Proceedings of the International Conference on Neural Networks, pp. 524-8, June 1996.

Dan Ventura, Tim Andersen and Tony Martinez, "Using Evolutionary Computation to Generate Training Set Data for Neural Networks", Proceedings of the International Conference on Artificial Neural Networks and Genetic Algorithms, pp. 468-471, April 1995.

Dan Ventura and Tony Martinez, "An Empirical Comparison of Discretization Methods", Proceedings of the International Symposium on Computer and Information Sciences, pp. 443-450, November 1995.

Dan Ventura and Tony Martinez, "Using Multiple Statistical Prototypes to Classify Continuously Valued Data", Proceedings of the International Symposium on Neuroinformatics and Neurocomputers, pp. 238-45, September 1995.

Dan Ventura and Tony Martinez, "BRACE: A Paradigm For the Discretization of Continuously Valued Data", Proceedings of the Florida Artificial Intelligence Research Symposium, pp. 117-121, May 1994.

Other

Steve Bair, Uday Chakraborty, Shu-Heng Chen, Heng-Da Cheng, David K.Y. Chiu, Sanjoy Das, Grit Denker, Richard Duro, Manuel Grana Romay, Donald Hung, Etienne Kerre, Hong Va Leong, Chang-Tien Lu, Jie Lu, Liam Maguire, Chong Wah Ngo, M. Sarfraz, Chris Tseng, Shusaku Tsumoto, Dan Ventura, Paul P. Wang, Xin Yao, C.N. Zhang, Kaizhong Zhang (Editors), Proceedings of the Eighth Joint Conference on Information Sciences, July 2005.

Ken Chen, Shu-Heng Chen, Heng-Da Cheng, David K.Y. Chin, Sanjoy Das, Richard Duro, Zhen Jiang, Nik Kasabov, Etienne Kerre, Hong Va Leong, Qing Li, Mi Lu, Manuel Grana Romay, Dan Ventura, Paul P. Wang, Jie Wu (Editors), Proceedings of the Seventh Joint Conference on Information Sciences, September 2003.

Dan Ventura and Tony Martinez, "Kvantovaya Accotsiativnaya Pamyat'", Neirokomp'yutery: razrabotka i primenenie, N9-10, pp. 34-53, 2002 (Russian translation of "Quantum Associative Memory" in the journal Neurocomputers: development and applications).

H. John Caulfield, Shu-Heng Chen, Heng-Da Cheng, Richard Duro, Vasant Hanovar, Etienne E. Kerre, Mi Lu, Manuel Grana Romay, Timothy K. Shih, Dan Ventura, Paul Wang, Yuanyuan Yang (Editors), Proceedings of the Sixth Joint Conference on Information Sciences, March 2002.

Dan Ventura, comments in "Discussion on Neurocomputers After Ten Years" (Eds. Frolov and Ezhov), Neural Network World, vol. 1 no. 2, pp. 103-174, 1999.

Dan Ventura, Quantum and Evolutionary Approaches to Computational Learning, Ph.D. Dissertation, Computer Science Department, Brigham Young University, 1998.

Dan Ventura, On Discretization as a Preprocessing Step for Supervised Learning Models, Master's Thesis, Computer Science Department, Brigham Young University, 1995.

Seminars, Colloquia and Presentations

• Reservoir Shaping via Reinforcement Learning to Improve Liquid State Machines, NIPS Workshop on Echo State Networks and Liquid State Machines, Whistler, British Columbia, December 2006 (with David Norton)
• Effecting Transfer via Learning Curve Analysis, Inductive Transfer: 10 Years Later Workshop, Neural Information Processing Systems, Whistler, British Columbia, December 2005 (with Christophe Giraud-Carrier)
• The Challenge of Computing using Quantum Computation, Department of Physics and Astronomy, Brigham Young University, colloquium, January 2004
• Learning as Quantum Search, Quantum Neural Networks Workshop, Neural Information Processing Systems, Whistler, British Columbia, December 2002
• Quantum Neural Computation, Mini-Symposium on Quantum Computing, College Station, Texas, May 2001
• Experimental Validation of the Common Control Language, The Third Very Shallow Water/Surf Zone Mine Counter Measures Program Meeting, Panama City, Florida, February 2001
• Why quantum computation is good for computational learning and computational learning is good for quantum computation, Department of Computer Science, Brigham Young University, February 2001
• Quantum Computation and Computational Learning, Department of Physics and Astronomy, Brigham Young University, February 2001
• Linear Optics for Quantum Neural Computing, Quantum Neural Computing Workshop, Neural Information Processing Systems, Breckenridge, Colorado, December 2000
• Application of Quantum Computation to Computational Learning, DARPA Quantum Information Science and Technology Workshop, Greenbelt, Maryland, October 2000
• Quantum Computation: Experiments in Linear Optics, Applied Research Laboratory, Penn State University, September 2000.
• Field Programmable Scripts for Multiple UUV Missions, Second Very Shallow Water/Surf Zone Mine Counter Measures Program Meeting, Panama City, Florida, December 1999
• Quantum Computation: Current and Future Perspectives, Fall meeting of the Applied Research Laboratory Advisory Board, Penn State University, November 1999
• An Introduction to Quantum Computation, Applied Research Laboratory, Penn State University, October 1999
• A Neural/Evolutionary Hybrid Learning System, Information Science and Technology Division, Applied Research Laboratory, Penn State University, July 1999
• Quantum Computation: An Overview, Analogical Modeling of Language Group, Brigham Young University, July 1999
• Quantum Computation: An Overview, Department of Computer Science, University of Massachusetts at Lowell, May 1999
• Fourier Algorithms and Quantum Learning, Theoretical and Mathematical Physics Research Group, Brigham Young University, March 1998
• A Genetic Algorithm - Neural Network Hybrid, Theoretical and Mathematical Physics Research Group, Brigham Young University, November 1998
• Genetic Algorithms, Theoretical and Mathematical Physics Research Group, Brigham Young University, September 1996

Grants

• $14,000, Student Visions of Learning in Autonomous Extra-terrestrial Systems, Mentoring Environment Grant, Brigham Young University, 2005.
• $5,760, proposal development funding for Application of Quantum Computation to Computational Intelligence for anticipated DARPA/QuIST solicitation, ARL, Penn State University, 2000-2001
• $40,000, Quantum Associative Memory Using Linear Optics, ARL Center of Excellence, Penn State University, 2000

Students

• Kaivan Kamali, Masters of Computer Science and Engineering, May 2001 (with Soundar Kumara)
• Rock Hymas, Honors Thesis, April 2003
• Mark Richards, Masters of Computer Science, April 2004
• Nancy Owens, Masters of Computer Science, August 2004
• Stephen Whiting, Masters of Computer Science, August 2004
• Adam Drake, PhD student
• Eric Goodman, Masters of Computer Science, April 2005
• Neil Toronto, PhD student
• Jared Lundell, Bachelor's of Science in Computer Science, April 2006
• Charles DuHadway, Bachelor's of Science in Computer Science, April 2006
• David Norton, MS student
• Charlie Neo, MS student
• Max Wilson, MS student
• Jeremy West, Bachelor's of Science in Computer Science, April 2007
• Heather Chan, Undergraduate student
• Rob Van Dam, MS student
• Kyle Dickerson, MS student
• Sabra Dinerstein, PhD student

Professional Activities

• Chair, Creative Intelligent Systems, 2008 AAAI Spring Symposium Series (with Mary Lou Maher and Simon Colton)
• Program Committee, IEEE Conference on Evolutionary Computation, 2007
• Program Committee, International Joint Conference on Neural Networks, 2007
• Program Committee, IEEE Three-Rivers Workshop on Soft Computing in Industrial Applications 2006 (SMCia/07)
• Area Editor, New Mathematics and Natural Computation, 2005-2006
• Program Committee, International Symposium on Neural Networks, 2006
• Program Committee, IEEE Mountain Workshop on Adaptive and Learning Systems 2006 (SMCals/06)
• Program Committee, IEEE Conference on Evolutionary Computation, 2006
• Program Committee, International Joint Conference on Neural Networks, 2006
• Program Committee, IEEE Mid-Summer Workshop on Soft Computing in Industrial Applications, 2005 (SMCia/05)
• Program Co-Chair, International Conference on Computational Intelligence in conjunction with the Joint Conference on Information Sciences, 2005
• Program Committee, International Joint Conference on Neural Networks, 2005
• Program Committee, International Conference on Neural Information Processing, 2004
• Program Co-Chair, International Conference on Computational Intelligence in conjunction with the Joint Conference on Information Sciences, 2003
• Participant, Faculty Development Series, Faculty Center, Brigham Young University, Fall 2001-Spring 2003
• Program Chair, International Conference on Computational Intelligence in conjunction with the Joint Conference on Information Sciences, 2002
• Certificate, The Penn State Course in College Teaching, Center for Excellence in Learning and Teaching, Penn State University, Fall 2000
• Panelist, National Science Foundation, 2000
• Member, Association for Intelligent Machinery
• Guest Editor, Information Sciences Special Issue on Quantum and Neuro-quantum Information Processing, October 2000
• Common Control Language Working Group Lead, Second Very Shallow Water/Surf Zone Mine Counter Measures Program Meeting, Panama City, Florida, December 1999
• Organizer, Special sessions on Quantum Computation and Neuro-Quantum Information Processing, International Conference on Computational Intelligence and Neuroscience, February 2000
• Organizing Committee, International Workshop on Quantum Neural Networks, Second Annual All-Russian Conference on Neuroinformatics, January 2000
• Member, International Neural Network Society
• Invited Speaker, International Conference on Computational Intelligence and Neuroscience special session on Neuro-Quantum Information Processing, October 1998
• Program Committee, International Conference on Computational Intelligence and Neuroscience, October 1998
• Program Committee, International Conference on Computational Intelligence and Neuroscience, March 2000

Committee Assignments

• Chair, Colloquium Committee, Computer Science Department, Brigham Young University, August 2005-present
• Member, Faculty Expectations Committee, Computer Science Department, Brigham Young University, July 2004-January 2005
• Member, Faculty Search Committee, Computer Science Department, Brigham Young University, August 2003-present
• Chair, PhD Recruiting Committee, Computer Science Department, Brigham Young University, August 2002-July 2005
• Member, Graduate Committee, Computer Science Department, Brigham Young University, July 2001-July 2003

References

Professor Tony Martinez Department of Computer Science Brigham Young University Email: martinez@cs.byu.edu Phone: (801) 378-6464	Dr. John Howell Department of Physics and Astronomy University of Rochester Email: howell@pas.rochester.edu Phone: (585) 275-4361
Professor Subhash Kak Department of Electrical and Computer Engineering Louisiana State University Email: kak@ee.lsu.edu Phone: (225) 578-5552	Professor Jean-Francois Van Huele Department of Physics and Astronomy Brigham Young University Email: vanhuele@dirac.byu.edu Phone: (801) 378-4481
Dr. Randy Wilson Family & Church History Department The Church of Jesus Christ of Latter-Day Saints Email: wilsonr@ldschurch.org Phone: (801) 240-9020