Ian McCulloh

Ian McCulloh is an associate professor in the Bloomberg School of Public Health and the Whiting School of Engineering at Johns Hopkins University.  He manages the Artificial Intelligence (AI) portfolio within the Johns Hopkins Lifelong Learning program.  His research intrests include social neuroscience, social network analysis, and AI.  He is the author of “Social Network Analysis with Applications” (Wiley: 2013), “ISIS in Iraq: Understanding the Social and Psychological Foundations of Terror” (Oxford: 2023), “Networks Over Time” (Oxford: forthcoming), and has published over 100 peer-reviewed papers.  He established the Brain Rise Foundation which is a non-profit organization that advances neuroscience and artificial intelligence research to better equip front-line recovery centers treat patients with substance abuse disorder, mood disorder, PTSD, and related pathologies.  He previously established Accenture’s federal artificial intelligence practice in 2019 and led its rapid growth reaching 1200 data scientists and engineers by his retirement in 2023.  He oversaw five of the largest artificial intelligence programs in the US Federal Government.  He also retired as a Lieutenant Colonel from the US Army after 20 years of service in special operations and improvised explosive device forensics.  He founded the West Point Network Science Center and created the Army’s Advanced Network Analysis and Targeting (ANAT) program. In his last military assignments as chief of strategy, he led interdisciplinary teams of Ph.D. scientists at Special Operations Command Central (SOCCENT) and Central Command (CENTCOM) to conduct social science research in 15 countries across the Middle East and Central Asia to included denied areas, which he used to inform data-driven strategy for countering extremism and irregular warfare, as well as empirically assess the effectiveness of military operations.  He holds a Ph.D. and M.S. from Carnegie Mellon University’s School of Computer Science, an M.S. in Industrial Engineering, and M.S. in Applied Statistics from the Florida State University, and a B.S. in Industrial Engineering from the University of Washington.  He is married with four children and a granddaughter.

Education History

• BS, Industrial Engineering, University of Washington
• MS, Applied Statistics, Florida State University
• MS, Industrial Engineering, Florida State University
• MS, Sociology, Carnegie Mellon University
• PhD, Computer Science, Carneige Mellon University

Work Experience

Director, Brain Rise Foundation

Publications

McCulloh, I. & Rasmussen, P. (Under Review). Expression Lost in Translation: Quantifying VR Avatar Fidelity in Neuromarketing and Therapy. In Proc NeurIPS 2024. Vancouver, Canada: NeurIPS.

McCulloh, I. & Newton, S. (under review). Strain Theory as a Driver of Violent Extremism. Journal of Dynamics of Asymmetric Conflict.

McCulloh, I. & Dagher, M. (Under Review). Exploring Neural Correlates of Cooperation and Compromise in Sectarian Contexts Using Functional Near-Infrared Spectroscopy (fNIRS). In Proc NeurIPS 2024. Vancouver, Canada: NeurIPS.

McCulloh, I., Older, M., McCulloh, A. (in press). The Paradox of Food Addiction and the Obesity Epidemic: A Public Health Perspective. JMIR Informatics.

Lowetz, C. & McCulloh, I. (2024). Russian Invaders on the Internet’s Front Page – A Survey of Behaviors in Ukraine-Related Subreddits. In Proc. 2024 IEEE/ACM Foundations of Open Source Intelligence and Security Informatics. Calabria, Italy: IEEE & ACM.

McCulloh, I., Bergamini, R. & Mackey, C. (2024). Echoes of War: How Reddit Narratives Shape Sectarian Views of the Israel-Hamas War. In Proc. 2024 IEEE/ACM Foundations of Open Source Intelligence and Security Informatics. Calabria, Italy: IEEE & ACM.

Erasala, R., McCulloh, I. (2024). Tweets to Touchdowns: Predicting NFL Achievement from Social Media Optimism. In Proc. International Conference on Sports Analytics and Data Visualization (ICSADV 2024). New York, NY: WASET.

Cohen, B., McCulloh, I. (2023). Fragile Minds: Exploring the Link Between Social Media and Young Adult Mental Health. In Proc. 2023 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. Kusadasi, Turkey: IEEE & ACM.

Duncan, C., McCulloh, I. (2023). Unmasking Bias in Chat GPT Responses. In Proc. 2023 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. Kusadasi, Turkey: IEEE & ACM.

McCulloh, I., Stewart, D., Kiernan, K., Yazicioglu, F., Patsolic, H., Zinner, C., Mohan, S., Cartwright, L. (2023). An experiment on the impact of predictive analytics on kidney offer acceptance decisions. American Journal of Transplantation. 23(7): 957-965
McCulloh, I., Placona, A., Stewart, D., Gause, D., Kiernan, K., Morgan, S., Zinner, C. Cartwright, L. (2023). Improving clinical decision making for organ transplant with AI. In Proceedings, Cognitive Control and Goal-Driven Decision-Making (ICCCGDDM 2023). New York, NY: WASET.

McCulloh, I., Zinser, M., Patsolic, J., Ramos, M. (2023) Impact of Similarity Ratings on Human Judgement. In Proceedings, International Conference on Semantics-Enabled Recommender Systems and Applications (ICSERSA 2023). New York, NY: WASET.

Kent, T.G., Phillips, N.E., McCulloh, I., Pavon-Harr, V., Patsolic, H.G. (2021). Microscopic Markov Chain Approach for Measuring Mobility Driven SARS-CoV-2 Transmission, In Proceedings Complex Networks 2021. Springer.

Heymann, D., Pavon-Harr, V., Schwantes, C., McCulloh, I. (2021). Methods in Constrained Community Detection: An Integer Optimization Model and Heuristic Approach for Cohort Creation, In Proceedings 2021 Eighth International Conference on Social Network Analysis, Management and Security (SNAMS). IEEE.

Stevens, N.T., Wilson, J.D., Driscoll, A.R., McCulloh, I., Michailidis, G., Paris, C., Paynabar, K., Perry, M.B., Reisi-Gahrooei, M., Sengupta, S. & Sparks, R. (2021) Broader impacts of network monitoring: Its role in government, industry, technology, and beyond, Quality Engineering, DOI: 10.1080/08982112.2021.1974036.

Stevens, N.T., Wilson, J.D., Driscoll, A.R., McCulloh, I., Michailidis, G., Paris, C., Paynabar, K., Perry, M.B., Reisi-Gahrooei, M., Sengupta, S. & Sparks, R (2021) Research in network monitoring: Connections with SPM and new directions, Quality Engineering, DOI: 10.1080/08982112.2021.1974035

Stevens, N.T., Wilson, J.D., Driscoll, A.R., McCulloh, I., Michailidis, G., Paris, C., Paynabar, K., Perry, M.B., Reisi-Gahrooei, M., Sengupta, S. & Sparks, R (2021) The interdisciplinary nature of network monitoring: Advantages and disadvantages, Quality Engineering, DOI: 10.1080/08982112.2021.1974034

Stevens, N.T., Wilson, J.D., Driscoll, A.R., McCulloh, I., Michailidis, G., Paris, C., Paynabar, K., Perry, M.B., Reisi-Gahrooei, M., Sengupta, S. & Sparks, R (2021) Foundations of network monitoring: Definitions and applications, Quality Engineering, DOI: 10.1080/08982112.2021.1974033

Koshute, P., Zook, J., McCulloh, I. (2021). Recommending Training Set Sizes for Classification. arXiv. http://arxiv.org/abs/2102.09382

Oler, M., Johnson, A., McCulloh, A., Dagher, M., Day, A., McCulloh, I. (2021). Reforming Sectarian Beliefs in Iraq: Winning the Peace. Journal of Communication, Society and Media. 4 (1).

Kahn, L., McCulloh, I., (2021). Getting Through the Trough of Artificial Intelligence (AI) Disillusionment with a Human-Centered Digital Twin Framework. Proc. AAAI 2021. AAAI.

Williams, E., Novak, V., Blackwell, D., McCulloh, I., Phillips, N. (2020). Homophily and Transitivity in Bot Disinformation Networks. In Proc. Seventh International Conference on Social Networks Analysis, Management and Security (SNAMS). Paris: IEEE.

McCulloh, I., Ellis, N., Savas, O., Rodrigues, P. (2020). Assessing e-Recruiting on Social Media: FBI Case Study. In Proc. 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. Hague: IEEE & ACM.

Kahn, L., McCulloh, I. (2020). Simulating the Impact of Artificial Intelligence Innovations with a Modular Framework and Digital Twin. In Proceedings of the 2020 Winter Simulation Conference. IEEE & ACM.

Lin, J., Lam, S., Savas, O., McCulloh, I. (2020). Approaches for quantifying video prominence, narratives, & discussion: Engagement on COVID-19 public health interventions YouTube videos. In Proc. 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. Hague: IEEE & ACM.

Williams, E., Levin, D., McCulloh, I. (2020). Improving LDA Topic Modeling with Gamma and Simmelian Filtration. In Proc. 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. Hague: IEEE & ACM.

McCulloh, I., Savas, O. (2020). k-Truss Network Community Detection. In Proc. 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. Hague: IEEE & ACM.

Cooper-White, M.A., Gillespie, G.S.R., Burns, S.M., McCulloh, I., Ames, D.L., Dagher, M., Falk, E.B., Lieberman, M. (2020). A Synchrony-Based Classification Approach for Predicting Attitudes Using fNIRS. Journal of Social Cognitive and Affective Neuroscience. nsaa, https://doi.org/10.1093/scan/nsaa115

McCulloh, I., Kiernan, K., Kent, T. (2020). Improved Estimation of Daily COVID-19 Transmission Rate from Incomplete Data. Proc. COMPandemics2020. IEEE.

Lam, S., Hohman, E., Pavon-Harr, V., Patsolic, J., Schwantes, C., Willner, M., Schulz, K., Kent, T., Kiernan, K., McCulloh, I. (2020). Social determinates of health and COVID-19 mortality rates at the county level. Proc. COMPandemics2020. IEEE.

McCulloh, I., Kiernan, K., Kent, T. (2020). Inferring True COVID19 Infection Rates from Deaths. Frontiers in Big Data Medicine and Public Health.

McCulloh I., Savas O., & Ortiz B. (2020) Social Judgement Theory: A Network Based Implementation. Proc. of the 2019 Winter Simulation Conference. National Harbor, MD.

Savas, O., Ding, L., Papaleo, T., and McCulloh, I. (2020). Adversarial Attacks and Countermeasures against ML Models in Army Multi-domain Operations. Proc. of the Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II, SI20 SPIE Defense + Commercial Sensing Symposium. Anaheim, CA.

Ortiz Ulloa, B., Kahn, L., Bosch, M., Bogden, P., Savas, O. McCulloh, I. (2020). Improving Community Resiliency and Emergency Response With a Multi-Pronged Artificial Intelligence System. ISCRAM 2020.

Nassar, J., Pavon-Harr, V., Bosch, M., McCulloh, I. (2019). Assessing Data Quality of Annotations with Krippendorff’s Alpha for Applications in Computer Vision. In Proc. AAAI 2019 Fall Symposium. Arlington, VA: AAAI

Sehatbakhsh, N., Daw, E., Savas, O., Hassanzadeh, A., McCulloh, I. (2019) Security and Privacy Considerations of Machine Learning Models Deployed in the Government and Public Sector. In Proc. AAAI 2019 Fall Symposium. Arlington, VA: AAAI

Grubner, S., Piorkowski, J., & McCulloh, I. (2019). Social Media as a Main Source of Customer Feedback – Alternative to Customer Satisfaction Surveys. In Proc. 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. Vancouver, BC: IEEE & ACM.

Hegde, M., McCulloh, I., Piorkowski, J. (2019). Examining Massive Open Online Course (MOOC) superposter behavior using social network analysis. In Proc. 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. Vancouver, BC: IEEE & ACM.

Rashed, M., Piorkowski, J., McCulloh, I. (2019) Evaluation of Extremist Cohesion in a Darknet Forum Using ERGM and LDA. In Proc. 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. Vancouver, BC: IEEE & ACM.

Tackacs, R. and McCulloh, I. (2019). Dormant Bots in Social Media: Twitter and the 2018 U.S. Senate Election, In Proc. of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). Vancouver, BC: IEEE/ACM.

Savas, O., Ortiz-Ulloa, B., and McCulloh, I. (2019). Causal Analysis of Online Outcomes of Interactions between Organic and Inorganic Accounts, In Proc. of the 2019 International Conference on Social Computing, Behavioral-Cultural Modeling, & Prediction and Behavior Representation in Modeling and Simulation (SBP-Brims). Washington, DC: Springer.

Bargar, A., Pitts, S., Butkevics, J., & McCulloh, I. (2019, May). Challenges and Opportunities to Counter Information Operations Through Social Network Analysis and Theory. In 2019 11th International Conference on Cyber Conflict (CyCon) (Vol. 900, pp. 1-18). IEEE.

Burns, S.M., Barnes, L.N., McCulloh, I., Dagher, M., Falk, E.B., Lieberman, M.D. (in press). Making social neuroscience less WEIRD: Neural signatures of persuasive influence in the Middle East as measured with fNIRS. Journal of Personality and Social Psychology.

Reed, A., Piorkowski, J., & McCulloh, I. (2018). Correlating NBA Team Network Centrality Measures with Game Performance. In Proceedings 2018 IEEE/ACM Conference on Advances in Social Network Analysis and Mining 2018.

Sadayappan, S., Piorkowski, J., & McCulloh, I. (2018). Evaluation Political Party Cohesion Using Exponential Random Graph Modeling. In Proceedings 2018 IEEE/ACM Conference on Advances in Social Network Analysis and Mining 2018.

McCulloh, I., Cohen, R., Takacs, R. (2018) Better Quality Classifiers for Social Media Content: Crowdsourcing with Decision Trees. In Proceedings 6th International Conference on Advanced Computing, Networking, and Informatics. Silchar, India: Springer.

McCulloh, I., Burck, J., Behling, J., Burks, M., Parker, J. (2018) Leadership of Data Annotation Teams. In Proceedings Social Sens 2018. Orlando, FL: IEEE.

McCulloh, I. (2018). IkeNet: Email and Friendship Evolution. Connections, 36(1).

McCulloh, I., McCulloh, L. (2018). Knowing the Terrain: Explicit and Implicit Measures of the Population. In SMA White Paper What Do Others Think and How Do We Know What They Are Thinking? (Ed. M. Yager). Washington DC: Office of Secretary of Defense.

McCulloh, I., Seese, G. (2017). Assessing Neural Bases of Persuasion with Functional Near Infrared Spectroscopy (fNIRS) in Amman, Jordan. Johns Hopkins Applied Physics Laboratory Technical Report AOS-17-0198. February 2017. Laurel, MD.

McCulloh, I., Healy, S., Markakis, P. (2017). Characterization of Open Resources for Planning and Understanding Strategies (Corpus). Johns Hopkins Applied Physics Laboratory Technical Report AOS-17-0196. February 2017. Laurel, MD.

McCulloh, I. & Perrone, A. (2017). Review of R Packages for Social Network Analysis. In Encyclopedia of Social Network Analysis and Mining, (Ed. Reda Alhajj and Jon Rokne). Berlin: Springer.

McCulloh, I. & Newton, S. (2016). Social Strain as a Driver of Violent Extremism: Strain Theory and Emotions. Johns Hopkins Applied Physics Laboratory Technical Report AOS-17-0038. December 2016. Laurel, MD.

McCulloh, I. (2016). Neuroscience of Influence. In Bio-Psycho-Social Determinants of Behavior (Ed. Jason Spitaletta). Washington DC: Office of Secretary of Defense.

Moore, C.L., Steed, B., Shaikh, S., Eyre, D., McCulloh, I., Spitaletta, J., Munch, R., Worret, C. (2016). SMA White Paper: Maneuver and Engagement in the Narrative Space. Washington DC: Office of Secretary of Defense.

Sullivan, C., McCulloh, I., Schreurs, B., Goyea, T. & Heyman, M. (2015). Open Source Intelligence Integration Using Twitter for the Air Force Distributed Common Ground System. Johns Hopkins Applied Physics Laboratory Technical Report AOS-15-0618. September 2015. Laurel, MD.

McCulloh, I. (2014). Iraq: Mixed Method Research January 2014. US Central Command Information Operations Technical Report. Tampa, FL.

McCulloh, I. & Berger, M. (2014). Cultural Domain Analysis and Influence Opportunities in Syria (Interim Report January 2014). US Central Command Information Operations Technical Report. Tampa, FL.

McCulloh, I. (2013). Social Conformity in Networks. Connections, 33(1): 35-42.

McCulloh, I., Johnson, A. N., & Carley, K.M. (2012). Spectral Analysis of Social Networks to Identify Periodicity. Journal of Mathematical Sociology, 36 (2): 80-96.

Sailer, K. & McCulloh, I. (2012). Social Networks and Spatial Configuration – How Office Layouts Drive Social Interaction. Journal of Social Networks, 34(1): 47-58.

Alexander, P., Armstrong, H. & McCulloh, I. (2011). Towards supply chain excellence using network analysis, In Proceedings, 2011 IEEE Network Science Workshop, 22-24 June 2011, pp. 90-97.

Casey, K. & McCulloh, I. (2011). HTS Support to Information Operations: Integrating HTS into COIN Operations. Military Intelligence Professional Bulletin, Oct-Dec 2011, pp. 28-32.

McCulloh, Ian & Carley, K.M. (2011). Detecting Change in Longitudinal Social Networks. Journal of Social Structure, 12(3).

McCulloh, Ian (2010). Network Topology Effects on Correlation Between Centrality Measures. Connections, 30(1): 21-28.

Armstrong, H. & McCulloh, I. (2010). Organizational risk using network analysis, In Clarke, N. and Furnell, S. and von Solms, R. (ed), South African Information Security Multi-Conference (SAISMC 2010), May 17 2010, pp. 132-141. Port Elizabeth, South Africa: Centre for Security, Communications & Network Research.

Armstrong, H., Armstrong, C. & McCulloh, I. (2010). A Course Applying Network Analysis to Organizational Risk, In Clarke, N. and Furnell, S. and von Solms, R. (ed), South African Information Security Multi-Conference (SAISMC 2010), May 17 2010, pp. 204-214. Port Elizabeth, South Africa: Centre for Security, Communications & Network Research.

McCulloh, I., & Carley, K.M., (2010) The Link Probability Model: An Alternative to the Exponential Random Graph Model for Longitudinal Data. Carnegie Mellon University, School of Computer Science, Institute for Software Research, Technical Report CMU-ISR-10-130. Pittsburgh, PA.

McCulloh, I. (2009) Comparison of Relational Networks. In Proceedings of the 5th U.K. Social Networks Conference, University of Greenwhich, UK.

Lospinoso, J., McCulloh, I., & Carley, K.M. (2009). Utility Seeking in Complex Social Systems: An Applied Longitudinal Network Study on Command and Control. In Proceedings of Artificial Intelligence and Social Behavior Modeling, Edinburgh, Scotland.

McCulloh, I. & Carley, K.M. (2009). Longitudinal Dynamic Network Analysis Using the Over Time Viewer Feature in ORA. Carnegie Mellon University Institute for Software Research Technical Report 09-118. Pittsburgh, PA.

McCulloh, I., Lospinoso, J. (2009) Statistically Significant Changes in Social Networks. U.S. Military Academy Network Science Center Technical Report 09-003, West Point, NY.

Lospinoso, J., McCulloh, I., & Johnson, A. (2009). Interfacing Network Simulations and Empirical Data. US Military Academy Network Science Center Technical Report 09-001, West Point, NY.

McCulloh, I., & Carley, K.M. (2008). Dynamic Network Change Detection. In Proceedings, 26th Army Science Conference, Orlando, Florida. 5.8% acceptance rate.

Lospinoso, J., McCulloh, I., & Carley, K.M. (2008). Network Simulation Models. In Proceedings, 26th Army Science Conference, Orlando, Florida. 5.8% acceptance rate.

McCulloh, I., Ring, B., Frantz, T., & Carley, K.M. (2008). Unobtrusive Social Network Data from Email. In Proceedings, 26th Army Science Conference, Orlando, Florida. 5.8% acceptance rate.

McCulloh, Ian, Daimler, Eric, & Carley, K.M . (2008). Using latent semantic analysis of email to detect change in social groups. Proceedings of the 2008 International Conference on Data Mining, Las Vegas, NV.13-17 July 2008, 48% acceptance rate.

Ring, B., Henderson, S., & McCulloh, I. (2008). Gathering and Studying Email Traffic to Understand Social Networks. Proceedings of the 2008 International Conference on Information and Knowledge Engineering, Las Vegas, NV.13-17 July 2008.

McCulloh, I., Daimler, E., & Carley, K.M . (2008). Using term-frequency-inverse-document frequency of email to detect change in social groups. Proceedings of the 2008 International Conference on Information and Knowledge Engineering, Las Vegas, NV.13-17 July 2008.

Morton, J., Jantzi, J.K., Rodriguez, A.M., McCulloh, I.A., & Graham, J. (2008). Quantifying the Efficacy of a Translator: The Effect of Syntactical and Literal Written Translations on Language Comprehension using the Machine Translation System FALCon (Foreign Area Language Converter). Applied Language Learning. 18(1):17-25.

McCulloh, Ian, & Carley, K. M. (2008). Detecting Change in Human Social Behavior Simulation. Carnegie Mellon University, School of Computer Science, Institute for Software Research, Technical Report CMU-ISR-08-135, Pittsburgh, PA.

McCulloh, Ian, & Carley, K. M. (2008). Social Network Change Detection. Carnegie Mellon University, School of Computer Science, Institute for Software Research, Technical Report CMU-ISR-08-116, Pittsburgh, PA.

McCulloh, I.A., Webb, M., & Graham, J. (2008). Change Detection in Social Networks. U.S Army Research Institute for the Behavioral and Social Sciences Technical Report 1235, Arlington, VA.

McCulloh, I.A., Lospinoso, J., & Carley, K.M. (2007). Social Network Probability Mechanics. In Proceedings, 12th International Conference on Applied Mathematics of the World Science Engineering Academy and Society, Cairo, Egypt. 30-31 December 2007. pp 319-325.

Moxley, F.I., Graham, J.M., & McCulloh, I. (2007). On the Science of Networks – An Emerging Approach. In Proceedings, 12th International Command and Control Technology Symposium (ICCRTS). Washington DC: Office of the Secretary of Defense.

McCulloh, I.A., Webb, M., & Carley, K. (2007). Social Network Monitoring of Al-Qaeda. Network Science. 1(1): 25-30.

McCulloh, I.A., Graham, J., Garcia, G., MacGibbon, J., Tardieu, K., Dye, H., & Moores, K. (2007). IkeNet: Social Network Analysis of e-mail Traffic in the Eisenhower Leadership Development Program. U.S Army Research Institute for the Behavioral and Social Sciences Technical Report 1218, Arlington, VA.

Paynter, J., McCulloh, I., & Graham, J. (2006). Application of Confidence Intervals to Text-Based Social Network Construction. U.S. Military Academy Technical Report, West Point, NY.

McCulloh, I.A., Massie, D., & Cordova, E. (2006). Response Surface Optimization of Lead-Azide for Explosive Detonators.In Proceedings, 25th Army Science Conference. Orlando, FL. November 2006. 46% acceptance rate.

McCulloh, I.A., Morton, J., Jantzi, J.K., Rodriguez, A.M., Stanford, J., & Graham, J. (2006). Efficacy in Automated Language Translators.In Proceedings, 25th Army Science Conference. Orlando, FL. November 2006. 46% acceptance rate.

Stanford, J., & McCulloh, I. (2006). Text Analysis Using Automated Language Translators. In Proceedings, 14th Army Research Lab – US Military Academy Technical Symposium. Aberdeen, MD November 2006.

Benedosso, A., & McCulloh, I.A. (2006). Confronting the Bio-Terrorism Dilemna. Army Chemical Review. October 2006: 8-9.

McCulloh, I.A., & Wattenberg F. (2005). factorResponse. MathDL, OSSLETS.

McCulloh, I.A., McInvale, H.D., & Gussenhoven, R. (2005). Take Boards. PRIMUS. June 2005.

Ferriter, E. A., McCulloh, I. A., & deRosset, W. (2005). Techniques Used to Estimate Limit Velocity in Ballistics Testing with Small Sample Size. In Proceedings, 13th Army Research Lab – US Military Academy Technical Symposium. Aberdeen, MD November 2005.

McCulloh, I.A. (2004). Computer Simulation of Decontamination Operations. Army Chemical Review. October 2004:15-19.

Honors and Awards

• Sidney D. Drell Academic Intelligence Achievement Award, Intelligence and National Security Alliance (INSA) (2013)

Professional Organizations

International Network of Social Network Analysts
Association for Computing Machinery (ACM)
Association for the Advancement of Artificial Intelligence (AAAI)