Generative Problem Solving (GPS) is defined as the capability of automatically or semi-
automatically generating data, problems, and solutions. In addition to text-based
question answering, GPS addresses computational problem solving that integrates
classic and modern AI technologies, data and knowledge engineering, and other
computer science disciplines.

TOPICS OF INTEREST​​ include applications of Generative AI and Generative Problem
Solving in health-related areas that include, but are not limited to:

​

• Medicine

• Psychology

• Computational biology

• Computational chemistry

• Health care

• Pharmaceutical science

Generative technologies for:

• Structured, semi-structured, and unstructured data

• Use cases

• Scenarios

• Problems

• Solutions

And fundamental AI and computer science technologies that include, but are not limited
to:

 

• Computer vision

• Software engineering

• Computer architecture

• Robotics

• Multimedia

• Data and knowledge engineering  

• Semantic computing

• Multi-modal human-computer interaction

 

Contributions will be reviewed for quality and relevance to the workshop’s theme.
Theoretical and applied papers and papers that capture best practices and lessons
learned from field studies are encouraged. Submission of preliminary results would also
be considered.

Submissions in PDF format through this link by August 20, 2025.

 

IMPORTANT SUBMISSION INSTRUCTION:

• When submitting a paper to the GPSH 2025 Workshop, please choose the "Workshop on Generative Problem Solving for Health" track in Easychair.

• Page limits:

  • Regular and Workshop Papers – 8 pages (up to 2 extra pages allowed at $150 per extra page)

  • Short Papers – 4 pages (1 extra page allowed at $150)

  • Position Papers – 2 pages (1 extra page allowed at $150)

 
Workshop Organizers
 
Dick Bulterman, Vrije Universiteit Amsterdam, The Netherlands
Chih-Hung Chang, Washington University School of Medicine in St. Louis, USA
Bryan Chou, Cal State Pomona, USA 
Tinglong Dai, Johns Hopkins University, USA
Daniela D'Auria, Free University of Bozen-Bolzano, Italy
Luca Muratore, IIT, Italy
​Fabio Persia, University of L'Aquila, Italy
Giovanni Pilato, Italian Research Council, Italy
Florian Schimanke, HSW University of Applied Sciences, Germany
Phillip C.-Y. Sheu, University of California, Irvine, USA
Mustafa Sert, Başkent University, Turkey
Jackson Wu, Taipei Medical University, Taiwan
Atsuo Yoshitaka, JAIST, Japan