Arturo Castellanos

Assistant Professor

Dean's Research Fellow: 2022-2024
Area: Operations and Information Systems Management
Phone: 757-221-1706
Email: [[m|arturo.castellanosbueso]]
Office: Miller 3088

Biography

Dr. Arturo Castellanos is an Assistant Professor in the Operations and Information Systems Management area within the Raymond A. Mason School of Business at William & Mary. Prior to joining William & Mary he was at Baruch College (CUNY) in the Department of Information Systems and Statistics in the Zicklin School of Business. His research interests are in the area of Business Analytics, FinTech, Blockchain, Systems Analysis and Design, and Conceptual Modeling. He has published his work in top IS journals including MIS Quarterly, Journal of the Association for Information Systems, and Decision Support Systems. He has taught courses on Business Analytics, Information Systems, Data Mining, Data Warehousing, Big Data, and Prescriptive Analytics. He holds a Doctor of Philosophy in Information Systems and Business Analytics and a Master of Science in Management Information Systems from Florida International University, and a Bachelor's and Master's degree in Telecommunications Engineering from University of Navarra in Spain.

Areas of Interest/Expertise

Artificial Intelligence
Business Analytics
Blockchain
Healthcare Analytics

CV

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Ph.D., Information Systems & Business Analytics, Florida International University, Miami, FL (2012-2016)
M.S., Management Information Systems, Florida International University, Miami, FL (2011-2012)
B.S. & M.S., Telecommunications Engineering, University of Navarra, San Sebastiá, Spain (2004-2009)
Exchange Program, Luleå University of Technology, Sweden (Fall 2009)

Chiarini Tremblay, M., Castellanos, A., Kohli, R., Espinosa, E., & Roderick, T. (2025). Integrating mental health and juvenile justice outcomes: A case study of model-agnostic interpretable machine learning. INFORMS Journal on Computing. In press. (SJR: 1.439)
Castellanos, A., Jiang, H., Gomes, P., Vander Meer, D., & Castillo, A. (2025). Large Language Models for Thematic Summarization in Qualitative Health Care Research: Comparative Analysis of Model and Human Performance. JMIR AI, 4, E64447. (SJR: 0.677)
Storey, V. C., Parsons, J., Bueso, A. C., Tremblay, M. C., Lukyanenko, R., Castillo, A., & Maass, W. (2025). Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability. Data & Knowledge Engineering, 102482. (SJR: 0.682)
Storey, V. C., Lukyanenko, R., & Castellanos, A. (2023). Conceptual Modeling: Topics, Themes, and Technology Trends. ACM Computing Surveys, 55, 38 pages. doi:10.1145/3589338 (SJR: 4.45)
Jiang, H., Castellanos Bueso, A. A., Castillo, A., Gomes, P., Li, J., & VanderMeer, D. (2023). Nurses' Work Concerns and Disenchantment during the COVID-19 Pandemic: Machine Learning Analysis. JMIR Nursing. (SJR: 1.992)
Recker, J. C., Lukyanenko, R., Jabbari Sabegh, M., Samuel, B., & Castellanos, A. (2021). From representation to mediation: A new agenda for conceptual modeling research in a digital world. MIS Quarterly, 45(1), 269-300. (SJR: 3.793)
Castellanos, A., Tremblay, M. C., Lukyanenko, R., & Samuel, B. (2020). Basic classes in conceptual modeling: Theory and practical guidelines. Journal of the Association for Information Systems, 21(4), 3. (SJR: 1.741)
Castillo, A., VanderMeer, D., & Castellanos, A. (2018). ExUP: Leveraging product description dimensionality for individualized recommendations. Decision Support Systems, 108, 69-78.
Villela, M. A., Bravo, C., Shah, M., Patel, S., Jorde, U.P., Stehlik, S., & Castellanos, A. (2020). Prediction of Outcomes after Heart Transplantation Using Machine Learning Techniques. The Journal of Heart and Lung Transplantation, 39(4), S295-S296.
Shah, M., Villela, M. A., Bravo, C., & Castellanos, A. (2020). Impact of Donor Hemodynamics on Heart Transplant Outcomes: Using Machine Learning Techniques. The Journal of Heart and Lung Transplantation, 39(4), S295.
Parra, C. M., Tremblay, M. C., Paul, K., & Castellanos, A. (2017). Exploratory Content Analysis using Text Data Mining: Corporate Citizenship Reports of Seven U.S. Companies from 2004-2012. Journal of Corporate Citizenship.

Samuel, B.M., Tegarden, D.P., Larsen, K.R., Aziz, R.A., Almeida, J.P., Amrollahi, A., Beard, J.W., Bellatreche, L., Bork, D., vom Brocke, J., Cabot, J., Castellanos, A., Gerber, A., Green, P., Grover, A., Guizzardi, G., et al. (2005). Software Development and Modeling in the Age of Artificial Intelligence Manifesto, SSRN. ssrn.com/abstract=5881104

Castellanos, A., Tremblay, M. C., Storey, V. C., Lukyanenko, R., & Parsons, J. (2025). Improving performance and transparency in machine learning: A ML model-world fit framework. Information Systems Research. FT50 (Under Review)
Roderick, T., Tremblay, M. C., Kohli, R., & Castellanos, A. (2025). When vulnerability drives action: Designing forward-looking frameworks for disaster preparedness. Information Systems Research. FT50 (Under Review)
Maaß, W., Castellanos, A., Storey, V. C., & Tremblay, M. C. (2025). Desiderata model alignment: Closing the representational gaps between AI predictions and human understanding. MIS Quarterly. (Pending Decision)
Samuel, B., Castellanos, A., & Honigsberg, S. (2025). Navigating the valleys and the peaks: An analysis and design framework of evolving information systems. Journal of the Association for Information Systems. (Under Review)
Farooq, M., Doshi, H., Alvarez Villela, M., Berlot, A., Kankaria, R., Shitole, S., Castellanos, A., & Bortnick, A. E. (2025). Machine learning strongly predicts one-year mortality but not readmission after ST-evelation myocardial infarction. PLOS One. (Under Review)
Tremblay, M., Castellanos, A., Kohli, R., & Espinosa, E. Transforming Juvenile Justice: Ethical AI for Predictive Accuracy and Social Equity. Journal of Computing, UT Dallas Special Issue. (Submitted)
Castellanos, A., Castillo, A., Lukyanenko, R., & Tremblay, M. C. The Role of Institutional Factors in Unstructured Data Collection Practices. European Journal of Information Systems. (In Preparation)

Beard, J. W. Wiedemann, A., Peterson, T.O., Sarma, H., & Castellanos, A. (2025, August). Agile education: Exercises and activities to enhance learning agile practices [Workshop]. AMCIS 2025, Montréal, Canada.
Castellanos, A., Tremblay, M. C., Kohli, R., Espinosa, E., Porter, R., & Mallick, A. (2025). Process mining for juvenile justice: Integrating mental health insights to enhance interventions [Paper presentation]. VRSS 2025, University of Virginia.
Tremblay, M. C., Castellanos, A., Espinosa, E., Kohli, R., et al. (2024). Process Mining for Juvenile Justice: Integrating Mental Health Insights to Enhancing Interventions. WITS 2024. (Accepted)
Tremblay, M., Castellanos, A., & Kohli, R. Data Integration and Augmentation to Deliver Juvenile Justice ECIS. (Submitted)
Maass, W., Castellanos Bueso, A. A., Tremblay, M., Lukyanenko, R., & Storey, V. C. (2023). Conceptual Alignment Method. AMCIS 2023, Panama.
Maass, W., Castellanos, A., Tremblay, M., Lukyanenko, R., & Storey, V. C. (2022). ConceptSuperimposition: Using Conceptual Modeling Method for Explainable AI. AAAI Spring Symposium.
Castellanos, A., Castillo, A., Chiarini Tremblay, M., Lukyanenko, R., Parsons, J., & Storey, V. C. (2021). Conceptual modeling guidelines for improving machine learning performance. AAAI-MAKE.
Lukyanenko, R., Storey, V. C., & Castellanos, A. (2020). Introducing GSO: A General Systemist Ontology. ER Forum 2020, Vienna.
Castellanos, A., Lukyanenko, R., & Storey, V. C. (2020). Modeling Observational Crowdsourcing. ER Forum 2020, Vienna.
Lukyanenko, R., Castellanos, A., Storey, V. C., Castillo, A., Tremblay, M. C., & Parsons, J. (2020). Superimposition: Augmenting Machine Learning Outputs with Conceptual Models for Explainable AI International Conference on Conceptual Modeling, pp. 26-34.
Lukyanenko, R., Parsons, J., Storey, V. C., Castellanos, A., & Tremblay, M. C. (2019). Modeling Matters: Can Conceptual Modeling Support Machine Learning? SIGSAND, New York.
Castillo, A., Castellanos, A., & VanderMeer, D. (2019). Inferring Structure for Design: An Inductive Approach to Ontology Generation. ER 2019, Salvador, Brazil.
Benbunan-Fich, R. & Castellanos, A. (2018). Digitization of Land Records: From Paper to Blockchain. ICIS, San Francisco. (Best Paper Nominee)
Castellanos, A., Castillo, A., Lukyanenoko, R., & Tremblay, M. C. (2017). Understanding Benefits and Limitations of Unstructured Data Collection. SIGAND/PLAIS EuroSymposium, Gdansk-Sopot, Poland.
Amrollahi, A., Lukyanenko, R., & Castellanos, A. (2017). Multi-Paradigmatic Theorizing: Mixing Design and Exploration. AMCIS, Boston.
Jabbari Sabegh, M., Lukyanenko, R., Recker, J., Samuel, B., & Castellanos, A. (2017). Conceptual Modeling Research in Information Systems: What we now know and what we still do not know. SIGSAND, Cincinnati.
Castellanos, A., Lukyanenko, R., Samuel, B. M., & Tremblay, M. C. (2016). Conceptual Modeling in Open Information Environments. SIGSAND, Lubbock, TX.
Parra, C. M., Tremblay, M. C., & Castellanos, A. (2016) Identifying Prominent Voices: In Social Corporate Responsibility Context. IEEE ICDIM, Lisbon, Portugal.
Castellanos, A. (2015). Relevance is in the Eye of the Beholder: Design Principles for the Extraction of Context-Aware Information from Healthcare Data. WITS, Forth Worth, TX.
Tremblay, M. C., Parra, C., & Castellanos, A. (2015). Corporate Social Responsibility Reports: Understanding Topics via Text Mining. AMCIS, Puerto Rico.
Tremblay, M. C., Parra, C., & Castellanos, A. (2015). Analyzing Corporate Social Responsibility Reports using Unsupervised and Supervised Text Data Mining. DESRIST, Dublin, Ireland.
Castillo, A., Castellanos, A., & Tremblay, M. C. (2014). Improving Case Management via Statistical Text Mining in a Foster Care Organization. DESRIST, Miami, FL. In: Advancing the Impact of Design Science, pp. 312-320.
Castellanos, A., Castillo, A., & VanderMeer, D. (2013). ExUp: Inferring Multiple-Dimension Rating System. WITS, Milan, Italy.
Castellanos, A., Castillo, A., Gudi, A., & Lee, R. Decision Making Drivers and Preparedness in Emergency Planning: A Case Study. WIT Press.

Castellanos Bueso, A. (Moderator). (2024). SIGSAND: Shaping the future of Systems Analysis and Design scholarship in the digital age. AMCIS 2024 Panel. Featuring J. W. Beard, B. Samuel, & C. Woo.
Lukyanenko, R., Samuel, B. M., Castellanos, A., & Maddah, M. (2016). Introducing Semantics-Contingent Syntax for Conceptual Modeling. DESRIST, St. John's NL, Canada.
Castellanos, A., Castillo, A., Tremblay, M. C., & Lukyanenko, R. (2016). Fueling the Manual Process: Extending the 'Gold Standard' in Case Management. ACM Digital Health, Montreal, Canada.
Castellanos, A., Castillo, A., & Tremblay, M. C. (2014). Improving Case Management via Statistical Text Mining in a Foster Care Organization. AMIA, Washington, D.C.
Tremblay, M., Castellanos, A., & Deckard, G. (2012). Research Prototype: A Knowledge Management System to track the Evaluation of the Implementation of a Statewide Health Information Exchange. WITS, Orlando, FL. (Best Prototype Runner-up)
Panelist, 3rd Annual South Florida Healthcare Trade Fair & Regional Conference (HIMSS South Florida). Panel: South Florida HIE Initiatives—Leadership Update and Perspectives on HIE.

Castellanos, A. (2025, April 4). AI tutors in the classroom: LLMs and retrieval augmented generation. AI in Business Education Summit, William & Mary, Raymond A. Mason School of Business, Williamsburg, VA.
Raymond A. Mason School of Business (2025, October 3). Mason AI Power Hour: We went to Harvard so you don't have to (AI edition). William & Mary.
Castellanos, A., Tremblay, M. C., Kohli, R., Espinosa, E., Porter, R., & Mallick, A. (2025, February 21). Process mining for juvenile justice: Integrating mental health insights to enhance interventions [Research presentation]. Mason Internal Research Seminar Series, Raymond A. Mason School of Business, William & Mary.
STLI Teaching and Learning Symposium (May 6, 2025). Poster presentation on AI Tutors. William & Mary.

BUAD 5722-02 - Big Data (MSBA), William & Mary (Spring 2025), 4.52/5
BUAD 5722-01 - Big Data (MSBA), William & Mary (Spring 2025), 4.29/5
BUAD 5722-01 - Big Data (MSBA), William & Mary (Spring 2024), 4.41/5
BUAD 5722-02 - Big Data (MSBA), William & Mary (Spring 2024), 4.41/5
BUAD 468-01 - Prescriptive Analytics (UGrad), William & Mary (Spring 2024) 4.00/5
BUAD 5722-02 - Big Data (MSBA), William & Mary (Spring 2023), 4.36/5
BUAD 5722-01 - Big Data (MSBA), William & Mary (Spring 2023), 4.00/5
BUAD 468-01 - Prescriptive Analytics (UGrad), William & Mary (Spring 2023), 3.83/5
BUAD 5722-02 - Big Data (MSBA), William & Mary (Spring 2022), 3.77/5
BUAD 5722-01 - Big Data (MSBA), William & Mary (Spring 2022), 3.52/5
BUAD 468-01 - Prescriptive Analytics (UGrad), William & Mary (Spring 2022), 3.49/5
BUAD 468-02 - Prescriptive Analytics (UGrad), William & Mary (Spring 2022), 3.47/5
CIS9660 - Data Mining for Business Analytics (Grad), Baruch College, CUNY (Fall 2020), 4.4/5
CIS9557 - Business Analytics (Grad), Baruch College, CUNY (Fall 2020), 4.6/5
CIS9660 - Data Mining for Business Analytics (Grad), Baruch College, CUNY (Fall 2019), 4.4/5
CIS9557 - Business Analytics (Grad), Baruch College, CUNY (Fall 2019), 4.1/5
CIS9557 - Business Analytics (Grad), Baruch College, CUNY (Fall 2018), 4.3/5
CIS4400 - Datawarehousing for Analytics (UGrad), Baruch College, CUNY (Fall 2017), 4.4/5
CIS4400 - Introduction to Information Systems (UGrad), Baruch College, CUNY (Fall 2017), 4.3/5
CIS4400 - Datawarehousing for Analytics (UGrad), Baruch College, CUNY (Fall 2016), 4.6/5
CIS4400 - Introduction to Information Systems (UGrad), Baruch College, CUNY (Fall 2016), 4.2/5
ISM4420 - Business Analytics (UGrad), Florida International University (Fall 2015), 4.5/5
CGS3300 - Introduction to Information Systems (UGrad), Florida International University (Spring 2015), 4.5/5
QMB4930 - Business Intelligence, Sec. U01 (UGrad), Florida International University (Fall 2014), 4.48/5
QMB4930 - Business Intelligence, Sec. U02 (UGrad), Florida International University (Fall 2014) 4.83/5

Alfred N. Page Best Teacher Award MSBA (AY 2024-2025)
Celonis AI & Process Mining Public Sector Modernization Award (2025) - Received on behalf of William & Mary for "Processing Mining for Juvenile Justice" - Celonis AI & PI Playbook Summit
Raymond A. Mason School of Business Mini Research Grant Award (2025), $6,000
Google Cloud Research Grant (2025), Google Cloud Credits, $5,000
AI Innovation Grant (2024), William & Mary School of Business—AI Tutors using Retrieval Augmented Generation, $10,000
AIS Distinguished Member (December 2020), Association for Information Systems—recognizes members with significance impact on the association
PSC-CUNY Research Award (April 2018), Cycle 49 PSC-CUNY Research Award #61371-00 49, $3,459
Doctoral Consortium, AMCIS 2015, San Juan, Puerto Rico
Doctoral Consortium, DESRIST 2014, Miami, FL
University Graduate School Fellowshi (2012-2015)
WITS 2015 Dissertation Proposal Track, U.T. Dallas

School Service

AI Tiger Team, Raymond A. Mason School of Business (2025)
TEC Member for Prof. Monica Chiarini Tremblay (2025)
Curriculum Committee, Raymond A. Mason School of Business (2024-Present)
Faculty Advisor, FGLI (Sep 2022-Present)
Library Committee (Sep 2022-Present)
Faculty Advisor, Undergrad (Sep 2021-Present)

IS Community Leadership

President, AIS SIGSAND
ER 2021 Committee Chair (Co-Publicity)
Vice-President, SIG-SAND

Journals: MIS Quarterly, Data & Knowledge Engineering, International Journal of Information Management, JAIS, JMIS, EJIS, BMC Medical Informatics, ISM, Decision Support Systems
Conferences: ICIS, AMCIS, DESRIST, WITS, PACIS, ECIS, AMIA
Conference Volunteering: AMCIS 2014 (Savannah), DESRIST 2014 (Miami, Session Chair), ICIS 2013 (Milan), ICIS 2012 (Orlando)

Association for Information Systems (AIS)
Member, AIS Doctoral Student Activities Subcommittee
INFORMS
HIMSS
AMIA

Business Analyst

C-Mater Dei Healthcare, Honduras

Project management responsibilities: drafted RFP and managed negotiation, design, and implementation of an ERP. Led business process reengineering to optimize clinical processes reduce costs. Improved sales/marketing using analytics. Pioneered preventive medical plans revenue stream—innovative at the time and later adopted by competitors. Performed statistical analysis including predictive models and customer/product segmentation.

Leading Hondorun hospital and strategic partner of the Honduran Breast Cancer Foundation (FUNHOCAM); partnerships with U.S. hospitals including Methodist Hospital Houston.

Business Development

FARSENS S.L., Technology, Spain

Business development for proprietary long-range passive RFID sensors, focusing on healthcare and logistics applications (perishable goods, temperature monitoring).

Research highlighted in diginomica (2025): Process intelligence for juvenile justice systems—demonstrating data-driven public sector modernization
Research referenced in VentureBeat (2025): "Tracking every decision, dollar and delay: The new process intelligence engine driving public-sector progress"
Finalist, IBM Watson Competition: AI-powered Smart Speaker for Homeless Veterans (HelpUSA)
Invited Speaker, World IA Day (Miami, 2016)—Keynote by Richard Saul Wurman (TED founder)
U.S. Winning Team, Santander Bank NEOs Challenge—big data competition (2M+ customers, 65M+ transactions)
AIS FIU Student Chapter—Speaker and Analytics Advisor
Honorable Mention, Tableau Student Data Challenge (2012)
Tableau Public Top 1% Author (2013)
Judge, NYC Science and Engineering Fair (NYCSEF)—Computer Science & Engineering
Fluent in Spanish

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