Irene Tsapara

Dr. Irene Tsapara is a Professor in the School of Technology and Engineering and the Academic Program Director for the Ph.D. in Data Science and Artificial Intelligence at National University. She instructs graduate courses in artificial intelligence, data science, deep learning, machine learning, predictive modeling, and applied mathematics. She directs a vibrant research community comprising more than 160 Ph.D. students and 15 research faculty members, hosting weekly research colloquia and monthly seminars on AI and Data Science. She is the Co-Principal Investigator for National University’s partnership with the NSF-funded AI Institute TILOS, a five-year, $20 million research institute focused on innovations in AI-driven optimization, in collaboration with UC San Diego, MIT, Yale, UT-Austin, and the University of Pennsylvania. Dr. Tsapara has over 30 years of experience in industry and academia, spanning computational learning theory, AI model optimization, quantum computing applications, and large-scale predictive modeling. Previously, she served as a senior quantitative analyst in the financial sector at Goldman Sachs (Hull Trading) and Hedge Fund Research, where she developed risk management simulations, volatility models, and automated portfolio analytics. She has also created over 30 online technical courses and held faculty appointments at Northwestern University, DePaul University, the University of Illinois at Chicago, and the University of Economics in Athens, Greece. She has supervised and mentored multiple Ph.D. scholars. She maintains active research collaborations in AI for health diagnostics, quantum-enhanced imaging, digital twins for intelligent networks, and domain-adapted LLM systems. Dr. Tsapara presented and published at venues including the IEEE Aerospace Conference, ACM COLT, AIAA SciTech, the International Astronautical Congress, ASEE Conference, and the ASU+GSV Summit. Her professional memberships include ACM, ASEE, SIAM, ASA, AMS, IEEE, and the Royal Statistical Society. She is also an Ambassador for Women in Data Science (WiDS) and serves on multiple national peer-review and advisory committees.

Memberships

• Association for Computing Machinery
• American Mathematical Society
• American Studies Association
• American Society for Engineering Education
• Harvard Business Review Advisory Council
• IEEE Computer Society
• Royal Statistical Society
• Society for Industrial and Applied Mathematics

Publications

• Irene Tsapara. (June 22, 2026). The Vibe Shift in Data Science Education: Foundational Pathways for AI-Assisted Curriculum Design. ASEE.


• Irene Tsapara. (June 22, 2026). Designing for Supervision: Comparing Management Frameworks for Teaching Generative AI in Science, Technology, Mathematics, and Engineering. ASEE.


• Bryan, K. & Tsapara, I.. (March 14, 2026). Entity Resolution for Aircraft Type Matching on Heterogeneous Aviation Data Sources. IEEE Aerospace Conference.


• Hanson, S. & Tsapara, I.. (January 16, 2026). Scaling Keyword Tagging in Space Science: LLM-Driven Automation. AIAA SCITECH.


• Irene Tsapara. (November 06, 2025). AI at the turning point. Humane Technologist.


• Shaik, H. & Tsapara, I.. (October 16, 2025). Digital Twin Foliage Representation for Network Deployment Optimization. IDEA Digital Twins Conference.. IDEA Digital Twins Conference.


• Hanson, S. & Tsapara, I. (. (September 29, 2025). Effective knowledge discovery and management using LLMs with specific application in Lunar Dust research 76th International Astronautical Congress. (IAC 2025), Sydney, Australia, IAC-25-D5,2,2, x103.


• Mark Marino, Irene Tsapara. (August 10, 2018). Math learning and history of mathematics. SCGM.


• Martina Bode, Jenny Ross, Irene Tsapara. (July 10, 2018). Online grading platforms: Improve quality and efficiency of grading. EDULEARN.


• Irene Tsapara. (July 15, 1998). Learning atomic formulas with prescribed properties. COLT.


• Irene Tsapara. (December 17, 1997). On the learnability of atomic formulas. University of Chicago.


• Irene Tsapara. (n.d.). On the construction of asset-weighted indices for hedge funds classified by strategy. HFR Report.


• Irene Tsapara. (n.d.). A taxonomy of datasets based on their shape and other characteristics..

Presentations

• (2025-10-16). Digital Twin Foliage Representation for Network Deployment Optimization. Shaik, H. & Tsapara, I.. Virtual - Germany.
• (2025-06-21). The Future of Education in the Era of AI. Virtual.
• (2025-04-25). Dynamic Work, Dynamic Technology: Exploring the Relationship Between Innovative Leadership and Generative AI. Virtual - San Diego.
• (2024-04-15). AI about, with, and beyond.. San Diego, Florida.
• (2023-10-10). AI – Sentience and anthropomorphism. Chicago, IL.
• (2023-10-06). “Advancing Your AI Career: The Good, the Bad, and the Ugly.”. Bradley, Candice, Irene Tsapara, and Alianna J. Maren.. Chicago.
• (2018-07-10). Online grading platforms: Improve quality and efficiency of grading. Barcelona, Spain.
• (2017-03-10). The use of phones, tablets, and technology in the classroom. Chicago, IL.
• (2016-08-10). The future of prediction: How it affects our privacy.. Chicago, IL.
• (2015-12-10). Gardner’s multiple intelligences in the math classroom.. Athens, Greece.
• (2011-06-10). Using a tablet for mathematics presentations. Online.
• (2010-10-01). Using videos and blogs in the classroom. Fort Lauderdale, FL.
• (2010-09-01). Math in different disciplines. Fort Lauderdale,FL.
• (1999-08-01). On the construction of asset-weighted indices for hedge funds classified by strategy. Chicago, IL.
• (1997-08-03). Learning atomic formulas with prescribed properties. Madison, WI.