Ying Zhao, Ph.D.

Research Professor

Information Sciences Department
Naval Postgraduate School

Root Hall 201F, 589 Dyer Road, Monterey, CA 93943

 

 

Education

1992: Ph.D. in Mathematics from Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139. 

Experience

2019-present: Research Professor, Information Sciences Department, Naval Postgraduate School.

2009-2018: Research Associate Professor, Information Sciences Department, Naval Postgraduate School.

Research on theories and methodologies of quantum intelligence, data, text and information mining, pattern recognition, lexical link analysis, social network analysis,  agent learning, search and visualization applied to DoD applications in system self-awareness, decision making, organizational behavior, collaboration, and command and control. 

Funded projects at NPS are listed below:

·       Supply and Maintenance Predictive Modeling and Simulation Analysis Tool   (2020)

·       Leverage Artificial Intelligence (AI) to Learn, Optimize, and Win (LAILOW) for a Complex Enterprise - Navy Logistics and Supply Chain (2020)

·       Design, Demonstrate and Proof of Concept of Using the Explainable Reinforcement Learning (xRL) in Soar for Combat Identification (CID) (2020)

·       Demonstrating a Tactical Server Concept Leveraging Big Data, Deep Analytics, Machine Learning (ML), and Artificial Intelligence (AI) Algorithms (2019)

·       Leverage Artificial Intelligence (AI) to Optimize Global Material Distribution (2019)

·       Modeling Large-Scale Warfighter Cognitive Reasoning and Decision- Making Using Machine Learning (ML), Artificial Intelligence (AI), and Game Theory (GT) (2019)

·       Deep Analytics for MarineNet with Personalized Learning - Using the Pilot Data (Continuation) (2019)

·       Data Mining, Machine Intelligence, and Al for Application in Over-The-Horizon Targeting (OTH-T) for Anti-Access Area Denied (A2AD) Environments (2019, 2020)

·       Big Data ML and AI for Combat ID and Combat Systems – Design, Demonstrate and Proof of Concept (2018)

·       Deep Analytics for Content Management System (CMS) (2018)

·       Reinforcement learning for Modeling Large-Scale Cognitive Reasoning (2017,2018)

·       Big Data Architecture and Analytics for Common Tactical Air Picture – Efficient Implementation (2017)

·       The Hatch LLA Data Depiction with MMOWGLI (2017)

·       A Big Data and Deep Learning Model for the CSAAC RDK Cloud (2017)

·       Big Data and Deep Learning for Defense Acquisition Visibility Environment (DAVE) – Developing NPS Student Thesis Research (2017)

·       Big Data and Deep Learning (BDDL) for Logistics in Support of the Fleet’s Distributed Lethality Concept (2016)

·       Applying the SOAR Architecture to Model Cognitive Functions in a Kill Chain (2016)

·       Big Data Architecture and Analytics for Common Tactical Air Picture – Practical Applications (2016)

·       Big Data Architecture and Analytics for Common Tactical Air Picture (2015)

·       Undiscovered Secrets: Leveraging Lexical Link Analysis (LLA) to Discover New Knowledge Using Open Social Media Data Sources (2015)

·       Authenticity Verification of Social Network Participants (2014)

·       Application of Lexical Link Analysis Web Service to Defense Acquisition Visibility Environment (DAVE) (2014,2015)

·       Lexical Link Analysis Application: Improving Web Service to Acquisition Visibility Portal (2013)

·       Improve DoD Energy Efficiency: Applying MMOWGLI Social Media Brainstorming with Lexical Link Analysis to the Acquisition Process (2013)

·       Applications of Lexical Link Analysis Web Service for Large-Scale Automation, Validation, Discovery, Visualization, and Real-Time Program Awareness (2012)

·       A Web Service Implementation for Large-Scale Automation, Visualization, and Real-Time Program-Awareness Via Lexical Link Analysis (2011)                  

·       MDA Acquisition: Toward Real-time Program-awareness via Lexical Analysis (2011)

·       MDA Acquisition: Toward Real-time Program-awareness via Lexical Analysis (2010)

·       Matching Navy Recruiting Needs Using Social Networking and Lexical Link Analysis (2010,2011)

·       A Web Service Implementation for Large-scale Automation, Visualization and Real-time Program-awareness via Lexical Link Analysis (LLA) (2009)

·       Reconstruct and Analyze Social Networks from Raw HUMINT Reports (2009).

2001-2009: Co-founder, Quantum Intelligence, Inc., 3375 Scott Blvd, Suite 100, Santa Clara, CA 95054.

Principal Investigator for DoD SBIR (2002-2008):

·       DARPA SBIR Phase I & II (10/2002-4/2006):  Development of Predictive Algorithms for In Silico Drug Toxicity and Efficacy Assessment

·       NSF SBIR Phase I (1/2003 – 7/2003): Quantum Intelligence System for Extended Enterprise

·       NAVY SBIR Phase I & II (08/2003 – 12/2006):  Knowledge Gathering Network for Battle Field Interoperability Management System.

·       NAVY ONR SBIR Phase I (5/2006 – 12/2006): Semantical Machine Understanding

·       Naval Health Research Center: (1/2007 – 7/2007): Patterns and Correlations in Theater Medical Registry Records

·       Trident Warrior 08 (6/2007 – 10/2008): Collaborative Learning Agents

Patents:

·       US 9,026,373 Method and system for knowledge pattern search and analysis for selecting microorganisms based on desired metabolic property or biological behavior, 10/2017

·       US 9,323,837 Multiple domain anomaly detection system and method using fusion rule and visualization, 4/2016

·       US 9,026,373 Method and system for knowledge pattern search and analysis for selecting microorganisms based on desired metabolic property or biological behavior 5/2015

·       US 8,903,756 : System and method for knowledge pattern search from networked agents, 12/2014

 

Awards

·       NPS Special Act Award, 2014, 2017,2019

·       NPS Certification of Recognition for superb performance in improving and understanding the uses of data mining with the Department of Defense, 2012

·       Defense Advanced Research Program Agency: Special Commendation for Outstanding Achievement in the Development of Bio-Spice Technology, 2005

·       Office of Naval Research: Certification of Recognition of Small Business Innovation Research Program, 2006

 

1999 -2001: Senior Consultant, Blue Martini Software, 2600 Campus Dr. San Mateo, CA 94403

Performed statistical analysis, pattern recognition, machine learning and information mining for e-commerce applications. Clients included Saks Fifth Ave and Harley Davidson.

1996-1999: Senior Analyst, IBM Business Intelligence Services at the Almaden Research Center, 650 Harry Road, San Jose, CA 95120

Delivered successful business intelligence consulting engagements and education classes in the areas of targeted marketing, behavior prediction, customer loyalty management, product line quality control, banking credit/investment risk analysis, health care cost management and web mining.  Clients included Bank of America, Fuji Bank, Safeway, CNN, State Farm, McDonald, Florida Hospital, Pacific Bell, National Semiconductor, FingerHut, and Amazon.com.

Teaching Experience

Developed and taught the business intelligence class “IBM DB2 Intelligence Miner for Data Workshop” on AIX, AS400 and S390 for customers from Fuji Bank, Safeway, McDonald and Florida Hospital from 1996-2000.

1991-1996: Senior Scientist, BBN Technologies, 70 Fawcett Street, Cambridge, MA 02138

Conducted research on speech recognition, natural language understanding, optical character/handwriting recognition and text search. Her projects were funded by DARPA.  Applied advanced nonlinear modeling technologies such as projection pursuit learning, neural networks, decision trees, clustering, Bayesian models and Hidden Markov Models to these projects.

Publications

1.       Zhao, Y. and Stevens, E. (2020).  Using Lexical Link Analysis (LLA) as a Tool to Analyze A Complex System and Improve Sustainment. Chapter in Springer Proceedings in Complexity.

2.       Zhao, Y. and Mata, G. (2020). Leverage Artificial Intelligence to Learn, Optimize, and Win (LAILOW) for the Marine Maintenance and Supply Complex System. Submitted to the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 7-10 December 2020, The Hague, Netherlands.

3.       Zhao, Y. et al. (2019). Visualization techniques for network analysis and link analysis. In the proceedings of the 12th International Conference on Knowledge Discovery and Information Retrieval (KDIR).

4.       Zhao, Y. et al. (2019). Causal learning in modeling multi-segment war game leveraging machine intelligence with EVE structures. A poster in the AAAI 2019 Fall Symposium.

5.       Zhao, Y. et al. (2019).  Measures of effectiveness (MoEs) for MarineNet: A case study for a smart e-Learning organization. In the proceedings of the 12th KMIS. Retrieved from https://www.insticc.org/Primoris/Resources/PaperPdf.ashx?idPaper=84807.

6.       Zhao, Y. et al. (2019). Theory and use case of game-theoretic lexical link analysis. In the proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).

7.       Zhao Y., MacKinnon, D., and Jones, J. (2019). Causal Learning Using Pair-wise Associations to Discover Supply Chain Vulnerability. In the proceedings of the 11th International Conference on Knowledge Discovery and Information Retrieval (KDIR 2019), September 17-19, 2019, Vienna, Austria.

8.       Zhao Y. & Zhou C.C. (2019). Collaborative Learning Agents (CLA) for Swarm Intelligence and Applications to Health Monitoring of System of Systems. In: Rodrigues J. et al. (eds) Computational Science – ICCS 2019. ICCS 2019. Lecture Notes in Computer Science, vol. 11538, pp. 706-718. Springer, Cham. Retrieved from https://link.springer.com/chapter/10.1007/978-3-030-22744-9_55

9.       Zhao, Y., Derbinsky, N., Wong, L., Sonnenshein, J. & Kendall, T. (2018). Continual and Real-time Learning for Modeling Combat Identification in a Tactical Environment. Accepted to the NIPS 2018 Workshop on Continual Learning, December 2-9, 2018, Montreal, Canada.  Retrieved from https://sites.google.com/view/continual2018/submissions

10.   Zhao, Y., Polk, A., Kallis, S., Jones, L., Schwamm, R., & Kendall, T. (2018). Big Data and Deep Models Applied to Cyber Security Data Analysis. In the technical report of the Association for the Advancement of Artificial Intelligence (AAAI), the 2018 Fall Symposium: Adversary-aware Learning Techniques and Trends in Cybersecurity (ALEC) of the AAAI Fall Symposium, October 18-19, 2018, Arlington, VA. Retrieved from http://ceur-ws.org/Vol-2269/

11.   Zhao, Y., Wu, R., Xi, M., Polk A., & Kendall, T. Big Data and Deep Learning Models for Automatic Dependent Surveillance Broadcast (ADS-B). In the technical report of the Association for the Advancement of Artificial Intelligence (AAAI), the 2018 Fall Symposium:  Reasoning and Learning in Real-World Systems for Long-Term Autonomy (LTA 2018). AAAI 2018 Fall Symposium. October 18-19, 2018, Arlington, VA, USA. Retrieved from http://rbr.cs.umass.edu/lta/papers/FSS-18_paper_56.pdf

12.   Zhao, Y. (2018).  Deep Models, Machine Learning and Artificial Intelligence Applications in National and International Security. Invited presentation at the Machine Learning, Data Analytics and Modeling (DATAM 2018, http://necsi.edu/events/CCS2018-satellite) – a satellite session at the Conference on Complex Systems (the http://ccs2018.web.auth.gr/), September 23-28, 2018, Thessaloniki, Greece.

13.   Zhao Y., Zhou, C. & Bellonio, J. (2018). New Value Metrics using Unsupervised Machine Learning, Lexical Link Analysis and Game Theory for Discovering Innovation from Big Data and Crowd-sourcing. In the proceedings of  the 9th International Conference on Knowledge Engineering and Ontology Development (KEOD 2018), September 18-20, 2018, in Seville, Spain. Retrieved from http://www.scitepress.org/PublicationsDetail.aspx?ID=yednaU+deM4=&t=1

14.   Zhao, Y. & Zhou C. (2018). A Game-Theoretic Lexical Link Analysis for Discovering High-Value Information from Big Data. In the proceedings the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. Barcelona, Spain, 28-31 Aug. 2018 (ASONAM 2018), page 621 – 625. Retrieved from https://ieeexplore.ieee.org/document/8508317.

15.   Zhao Y., Zhou, C. &  Bellonio, J. (2018). Multilayer Value Metrics Using Lexical Link Analysis and Game Theory for Discovering Innovation from Big Data and Crowd-Sourcing. In the proceedings the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. Barcelona, Spain, 28-31 Aug. 2018 (ASONAM 2018), page 1145 - 1151. Retrieved from https://ieeexplore.ieee.org/document/8508498.

16.   Zhao, Y. & Zhou, C. (2018), Data Sciences Meet Machine Learning and Artificial Intelligence:  A Use Case to Discover and Predict Emerging and High-Value Information from Business News and Complex Systems. Presentation at the 9th International Conference on Complex Systems, the New England Complex Systems Institute, Boston, July 26, 2018.

17.   Zhao, Y. & Kendall, T. (2018). Reinforcement Learning for Modeling Large-Scale Cognitive Reasoning Using the Naval Simulation System and Soar.  Presentation at the 2018 National Fire Control Symposium, 5 - 9 February 2018, Ft. Shafter, Honolulu, Oahu, Hawaii.

18.   Zhao Y., MacKinnon D.  & Zhou, C. (2017). Discovering High-Value Information from Crowdsourcing. In the proceedings the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. Sydney, Australia,   31 July - 03 August, 2017 (ASONAM 2017). Retrieved from https://dl.acm.org/citation.cfm?doid=3110025.3121242

19.   Zhao, Y., Mooren, E. & Derbinsky, N. (2017). Reinforcement Learning for Modeling Large-Scale Cognitive Reasoning. In the proceedings of  the 9th International Conference on Knowledge Engineering and Ontology Development, (KEOD 2017), Nov. 1-3, 2017, Funchal, Portugal.  Retrieved from http://www.scitepress.org/DigitalLibrary/PublicationsDetail.aspx?ID=b0ttus7UXek=&t=1

20.   Salcido, R., Zhao, Y.  & Kendall, A. (2017). Analysis of Automatic Dependent Surveillance-Broadcast Data. In the technical report of the Association for the Advancement of Artificial Intelligence (AAAI), the 2017 Fall Symposium: Deep Models and Artificial Intelligence for Military Applications: Potentials, Theories, Practices, Tools and Risks. November 9-11, 2017, Arlington, Virginia. Retrieved from https://aaai.org/ocs/index.php/FSS/FSS17/paper/view/15996

21.   Halpin, Q., Zhao, Y.  & Kendall A. (2017). Using D3 to Visualize Lexical Link Analysis (LLA) and ADS-B Data. In the proceedings of the Association for the Advancement of Artificial Intelligence (AAAI), the 2017 Fall Symposium: Deep Models and Artificial Intelligence for Military Applications: Potentials, Theories, Practices, Tools and Risks. November 9-11, 2017, Arlington, Virginia. Retrieved from https://aaai.org/ocs/index.php/FSS/FSS17/paper/view/16008

22.   Wu, R., Clarke, A. & Kendall A. (2017). A Framework Using Machine Vision and Deep Reinforcement Learning for Self-learning Moving Objects in a Virtual Environment.” in the proceedings of the Association for the Advancement of Artificial Intelligence (AAAI), the 2017 Fall Symposium: Deep Models and Artificial Intelligence for Military Applications: Potentials, Theories, Practices, Tools and Risks. November 9-11, 2017, Arlington, Virginia. Retrieved from http://www.aaai.org/Library/Symposia/Fall/fs17-03.php

23.   Zhao, Y. & Zhao C. (2016). System Self-Awareness Towards Deep Learning and Discovering High-Value Information. In European Projects in Knowledge Applications and Intelligence Systems, Page 149-169. Ricardo J. Machado, Joao Sequeira, Hugo Placido de Silva and Joaquim Filipe (Eds.), Scitepress, Lisbon, Portugal.

24.   Zhao, Y., Mackinnon, D. J., Gallup, S. P., Billingsley, J. L. (2016). Leveraging Lexical Link Analysis (LLA) To Discover New Knowledge. Military Cyber Affairs, 2(1), 3.

25.   Zhao, Y. &  Zhou, C.  (2016). System Self-Awareness Towards Deep Learning and Discovering High-Value Information. In the Proceedings of the 7th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference, Oct. 20-22, New York, USA. Page 109-116. Retrieved from https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7777885

26.   Zhao, Y., Kendall, T. & Johnson, B. (2016). Big Data and Deep Analytics Applied to the Common tactical Air Picture (CTAP) and Combat Identification (CID). In the Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, Vol. 1, Page 443 – 449. November 9-11, 2016, Porto, Portugal. Retrieved from http://www.scitepress.org/DigitalLibrary/PublicationsDetail.aspx?ID=q+n3kcRRK1w=&t=1

27.   Zhao, Y., Mackinnon, D. J., Gallup, S. P. (2015). Big Data and Deep Learning for Understanding DoD data. Journal of Defense Software Engineering, Special Issue: Data Mining and Metrics.

28.   Zhao, Y., Gallup, S.P., and MacKinnon, D.J., (2014). Lexical Link Analysis Application: Improving Web Service to Acquisition Visibility Portal. Published proceedings for the 11th Annual Acquisition Research Symposium for Acquisition Management, Monterey, California, May 2014.

29.   Zhao, Y., Brutzman, D. & MacKinnon, D.J. (2013). Improving DoD Energy Efficiency: Combining MMOWGLI Social Media Brainstorming with Lexical Link Analysis to Strengthen the Acquisition Process. In Proceedings of the Tenth Annual Acquisition Research Program. Monterey, CA: Naval Postgraduate School. May, 2013.

30.   Zhao, Y., Gallup, S. P., & MacKinnon, D. J. (2012). Applications of Lexical Link Analysis Web Service for Large-Scale Automation, Validation, Discovery, Visualization, and Real-Time Program Awareness. Acquisition Report NPS-AM-12-205. Retrieved from Naval Postgraduate School, Acquisition Research Program website: http://www.acquisitionresearch.net

31.   Zhao, Y., Gallup, S. P., & MacKinnon, D. J. (2011). A Web Service Implementation for Large-Scale Automation, Visualization, and Real-Time Program-Awareness Via Lexical Link Analysis.

32.   Acquisition Report NPS-AM-11-186. Retrieved from Naval Postgraduate School, Acquisition Research Program website: http://www.acquisitionresearch.net

33.   Zhao, Y., Gallup, S. P., & MacKinnon, D. J. (2010). Towards real-time program awareness via Lexical Link Analysis. Acquisition Report NPS-AM-10-174. Retrieved from Naval Postgraduate School, Acquisition Research Program website: http://www.acquisitionresearch.net

34.   Zhao, Y., MacKinnon, D., & Gallup, S. (2012, June). Semantic and social networks comparison for the Haiti earthquake relief operations from APAN data sources using lexical link analysis. In Proceedings of the 17th ICCRTS, International Command and Control, Research and Technology Symposium. Retrieved from http://www.dodccrp.org/events/17th_iccrts_2012/post_conference/papers/082.pdf

35.   Zhao, Y., Gallup, S. P., & MacKinnon, D. J. (2011, September). System self-awareness and related methods for improving the use and understanding of data within DoD. Software Quality Professional13(4), 19–31. Retrieved from http://asq.org/pub/sqp/

36.   Zhao, Y., Gallup, S.P., and MacKinnon, D.J., (2014). Lexical Link Analysis Application: Improving Web Service to Acquisition Visibility Portal. In proceedings for the 11th Annual Acquisition Research Symposium for Acquisition Management, Monterey, California, May 2014. Retrieved from https://calhoun.nps.edu/bitstream/handle/10945/54618/NPS-AM-13-109.pdf

37.  Zhao, Y., Brutzman, D. & MacKinnon, D.J. (2013). Improving DoD Energy Efficiency: Combining MMOWGLI Social Media Brainstorming with Lexical Link Analysis to Strengthen the Acquisition Process. In Proceedings of the Tenth Annual Acquisition Research Program. Monterey, CA: Naval Postgraduate School. May, 2013.

38.   Zhao, Y., Gallup, S. P., & MacKinnon, D. J. (2012). Applications of Lexical Link Analysis Web Service for Large-Scale Automation, Validation, Discovery, Visualization, and Real-Time Program Awareness. Acquisition Report NPS-AM-12-205. Retrieved from https://calhoun.nps.edu/handle/10945/33852.

39.   Zhao, Y., MacKinnon, D., & Gallup, S. (2012, June). Semantic and social networks comparison for the Haiti earthquake relief operations from APAN data sources using lexical link analysis. In Proceedings of the 17th ICCRTS, International Command and Control, Research and Technology Symposium. Fairfax, Virginia, June 19–21, 2012. Retrieved from http://www.dodccrp.org/events/17th_iccrts_2012/post_conference/papers/082.pdf

40.   Zhao, Y., MacKinnon, D., Gallup, S. (2012, June).  Lexical Link Analysis and System Self-awareness: Theory and Practice Poster at the Cyber and Information Challenges 2012 Conference, Utica, NY from 6/11-15.  

41.   Zhao, Y., Gallup, S. P., & MacKinnon, D. J. (2012, May). Applications of Lexical Link Analysis Web Service for Large-scale Automation, Validation, Discovery, Visualization and Real-time Program-awareness. Presentation at the 9th Annual Acquisition Research Symposium, Monterey, California, May 16-17, 2012.

42.   Thomas, G. F., Stephens, K., Zhao, Y., Gallenson, A. (2012, March). Understanding Transactive Memory Systems in Inter-organizational Networks:  An Analysis of Haiti’s 2010 APAN Disaster Response Coordination.  Presentation in Sunbelt XXXII, or the International Sunbelt Social Network Conference is the official conference of the International Network for Social Network Analysis (INSNA), March 12,-18, 2012, Redondo Beach, CA.

43.   Zhao, Y., Gallup, S. P., & MacKinnon, D. J. (2011, September). System self-awareness and related methods for improving the use and understanding of data within DoD. Software Quality Professional, 13(4), 19–31. Retrieved from http://asq.org/pub/sqp/

44.   Zhao, Y., MacKinnon, D., Gallup, S. (2011, June).  Lexical Link Analysis for the Haiti Earthquake Relief Operation Using Open Data Sources In Proceedings of the 16th ICCRTS, International Command and Control, Research and Technology Symposium, Québec City, Canada June 21–23, 2011. Retrieved from https://ntrl.ntis.gov/NTRL/dashboard/searchResults.xhtml?searchQuery=ADA547096.

45.   Zhao, Y., Gallup, S. P., & MacKinnon, D. J. (2011, May). A web service implementation for large-scale automation, visualization and real-time program-awareness via lexical link analysis. In Proceedings of the Eighth Annual Acquisition Research Program. Monterey, CA: Naval Postgraduate School.

46.   Zhao, Y., Gallup, S. P., & MacKinnon, D. J. (2011). A web service implementation for large-scale automation, visualization and real-time program-awareness via lexical link analysis (NPS-GSBPP-11-012). Monterey, CA: Naval Postgraduate School. Retrieved from https://calhoun.nps.edu/bitstream/handle/10945/33967/NPS-GSBPP-11-012.pdf.

47.   Zhao, Y., MacKinnon, D.J, & Gallup, S.P. (2011) System Self-awareness and Related Methods for Improving the Use and Understanding of Data within DoD. In American Society for Data Quality (ASQ), Volume 13, Issue 4, pp. 19-31, Sep 2011.  Retrieved from http://asq.org/qic/display-item/index.html?item=33878.

48.   Zhao, Y., Gallup, S., & MacKinnon, D. (2010). Towards real-time program awareness via lexical link analysis. In Proceedings of the Seventh Annual Acquisition Research Program. Monterey, CA: Naval Postgraduate School.

49.   Zhao, Y., Gallup, S., & MacKinnon, D. (2010). Towards real-time program awareness via lexical link analysis. Acquisition Research Sponsored Report Series, NPS-AM-10-174, Monterey, CA: Naval Postgraduate School.  Retrieved from https://calhoun.nps.edu/bitstream/handle/10945/33482/NPS-AM-10-049.pdf.

50.   Zhao, Y., MacKinnon, D., Gallup, S., & Zhou, C. (2010).  Maritime domain awareness via agent learning and collaboration. In Proceedings of the 15th ICCRTS, International Command and Control, Research and Technology Symposium, Santa Monica, CA. Retrieved from http://www.dodccrp.org/events/15th_iccrts_2010/papers/106.pdf.

51.   Gallup, S., MacKinnon, D., Zhao, Y., Robey, J., & Odell, C. (2009). Facilitating decision making, re-use and collaboration: A knowledge management approach for system self-awareness. In Proceedings of the International Joint Conference on Knowledge Discovery, Knowledge Engineering, and Knowledge Management (IC3K), Madeira, Portugal. Retrieved from http://www.dtic.mil/get-tr-doc/pdf?AD=ADA587494.

52.   Zhao, Y., MacKinnon, D., Gallup, S., & Zhou, C. (2010).  Maritime domain awareness via agent learning and collaboration. In Proceedings of the 15th ICCRTS, International Command and Control, Research and Technology Symposium, Santa Monica, CA, June 22-24, 2010. Retrieved from http://www.dodccrp.org/events/15th_iccrts_2010/papers/106.pdf.

53.   Zhou, C., Zhao, Y., & Kotak, C. (2009). The Collaborative Learning agent (CLA) in Trident Warrior 08 exercise. In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval (KDIR), pp.323-328. Madeira, Portugal.  Retrieved from http://www.dtic.mil/get-tr-doc/pdf?AD=AD1014634.

54.  Zhao, Y., Wei, S., Oglesby, I., Zhou, C. (2009). Utilizing the Quantum Intelligence System for Drug Discovery (QIS D2) for anti-HIV and anti-cancer cocktail detection. In the Journal of Medical Chemical, Biological, & Radiological Defense (JMedCBR), Volume 7. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.465.8000&rep=rep1&type=pdf.

55.  Zhao, Y., Kotak, C. & Zhou C. (2008). Semantical machine understanding, in Proceedings of the 13th International Command and Control Research and Technology Symposium.  Washington, DC. Retrieved from http://www.dodccrp.org/events/13th_iccrts_2008/CD/html/papers/205.pdf

56.  Zhao, Y., Zhou, C.(2005). Large-scale drug function prediction by integrating QIS D2 and BioSpiceIn Proceedings of IEEE Computational Systems. pp. 391-394. Retrieved from https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=1540654.

57.  Zhao, Y., Zhou, C. (2005). Drug characteristics prediction. In Proceedings of IEEE Computational Systems Bioinformatics Workshops. Stanford, CA: Stanford University. pp 395-398. Retrieved from https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=1540655.

58.   John, G. & Zhao, Y. (1997). Mortgage data mining. In Proceedings of the 1997 International Conference on Financial Engineering. New York.  Retrieved from https://ieeexplore.ieee.org/document/618942/.

59.   Zhao, Y. & Atkeson, C. (1996). Implementing projection pursuit. In the IEEE Transactions on Neural Networks, 7(2): p. 362-373. Retrieved from https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=485672.

60.   Zhao, Y. (1995). Hierarchical mixtures of experts methodology applied to continuous speech recognition. In Proceedings of the 1995 International Conference on Acoustics, Speech, and Signal Processing, p. 3443-3446.

61.   Zhao, Y., Schwartz, R., Sroka, J. & Makhoul, J. (1994). Hierarchical Mixtures of Experts Methodology Applied to Continuous Speech Recognition. In Advances in Neural Information Processing Systems 7,  G. Tesauro and D.S. Touretzky and T.K. Leen (Eds.). San Mateo: Morgan Kaufmann Publishers. Retrieved from https://papers.nips.cc/paper/929-hierarchical-mixtures-of-experts-methodology-applied-to-continuous-speech-recognition.

62.   Zhao, Y., Schwartz, R. & Makhoul, J. (1993). Segmental neural net optimization for continuous speech recognition. In Advances in Neural Information Processing Systems 6, J.D. Cowan, G. Tesauro, and J. Alspector (Eds.). San Mateo: Morgan Kaufmann Publishers. Retrieved from https://papers.nips.cc/paper/763-segmental-neural-net-optimization-for-continuous-speech-recognition.pdf.

63.   Zhao, Y. & Atkeson, C. (1994). Projection pursuit learning: Some theoretical issues.  In Computational Learning Theory and Natural Learning Systems. S.J. Hanson, et al.(Eds.). Cambridge: MIT Press. Retrieved from https://dl.acm.org/citation.cfm?id=190821.

64.   Zhao, Y. & Atkeson, C. (1992). How projection-pursuit learning works in high dimensions. In Science of Artificial Neural Networks - Proc. of SPIE. Retrieved from https://www.spiedigitallibrary.org/conference-proceedings-of-spie/1710/0000/How-projection-pursuit-learning-works-in-high-dimensions/10.1117/12.140143.full?SSO=1.

65.   Zhao, Y. & Atkeson, C. (1991). Some approximation properties of projection pursuit learning networks. In Advances in Neural Information Processing Systems 4, J.E. Moody, S.J. Hanson, and R.P. Lippmann (Eds.). San Mateo: Morgan Kaufmann Publishers. Retrieved from https://papers.nips.cc/paper/493-some-approximation-properties-of-projection-pursuit-learning-networks.

 

 

Affiliations

·       AFCEA life-time member

·       IEEE member (2009-now)

·       AAAI life-time member

·       ACM life-time member