Jorge Fandinno

Dr. Jorge Fandinno is an Assistant Professor of Computer Science at the University of Nebraska at Omaha since 2020. His academic journey includes an Alexander von Humboldt Fellowship at the University of Potsdam (Germany) and a Postdoctoral Fellowship at the Toulouse Institute of Computer Science Research (France). He earned his Ph.D. in Computer Science from the University of Corunna (Spain) in 2015.

• Current Role: Assistant Professor, Computer Science Department, University of Nebraska at Omaha (2020–present)
• Previous Roles: Alexander von Humboldt Fellow (University of Potsdam, Germany), Postdoctoral Fellow (Toulouse Institute of Computer Science Research, France)

Dr. Fandinno’s research focuses on Artificial Intelligence, particularly in Knowledge Representation and Reasoning, Answer Set Programming, and Epistemic Logic. His work bridges theoretical and practical aspects, including the development of formal semantics, deductive systems, and applications in causal reasoning and constraint handling. Recent publications emphasize automated reasoning, strong equivalence verification, and integrating quantitative information into logic programming frameworks.

His scholarly output includes over 60 publications in prestigious venues such as Artificial Intelligence, Journal of Artificial Intelligence Research, and conferences like AAAI, IJCAI, and LPNMR. He has received three best technical paper awards at LPNMR (2015, 2017, 2019) and mentored students who won best student papers at JELIA and ICLP. Notably, he secured a NSF CAREER award (2024) for his project on Answer Set Programming for Quantitative Information.

• Scientific Awards:
  • NSF CAREER award (2024)
  • Best technical paper, LPNMR 2015
  • Best technical paper, LPNMR 2017
  • Best technical paper, LPNMR 2019
  • Best student paper, JELIA 2019 (supervised student)
  • Best student paper, ICLP 2020 (supervised student)

Dr. Fandinno’s publications span theoretical advancements in logic programming, epistemic reasoning, and practical applications in causal analysis and constraint satisfaction. His recent work explores automated reasoning tools (e.g., Anthem 2.0), recursive aggregates, and complexity assessments in ASP.