Gastronomy, the art of selecting, preparing, serving, and enjoying fine food, has long been regarded as an artistic discipline, despite numerous efforts to establish its scientific foundations. However, the recent availability of high-quality datasets is fostering the investigation of gastronomy from a data science perspective. This is the vision portrayed by computational gastronomy, a data science that blends food, data, and the power of computation for achieving data-driven food innovations.

Computational gastronomy emerges as an interdisciplinary field that blends food, data, and computational techniques to foster data-driven innovations in food science and culinary arts and investigating questions such as: Why do we eat what we eat? What is the molecular basis of the flavor, of ingredients or recipes? Can we quantify the taste of a recipe? How do we measure the nutritional profile of a recipe? How does one make sense of contradictory assertions about the health consequences of food ingredients? How have world cuisines evolved? Can we design a tasty and healthy recipe?

This workshop aims to explore the application of data mining, machine learning, and artificial intelligence techniques in gastronomy. As the culinary landscape continues to evolve with digital transformation, this workshop provides a platform for researchers, data scientists, and industry professionals to investigate methodologies for extracting valuable insights from diverse food-related datasets.

We invite submissions covering a broad spectrum of topics related to computational gastronomy, including but not limited to:

- Data-driven analysis of food preferences and dietary habits
- Molecular basis of flavor perception and ingredient interactions
- Quantification of taste and sensory experiences using computational techniques
- Nutritional profiling of recipes and health impact analysis
- Resolving contradictory assertions on food ingredients and health outcomes
- Evolution of world cuisines from a data science perspective
- Generative AI for new recipe creation
- Personalized dietary recommendations based on machine learning models
- Food-chemical graphs and graph neural networks for food pairing
- Food identification via spectral analysis and image recognition
- Categorization and clustering of food ingredients and recipes
- Predictive models for nutrition and health impacts
- Large Language Models (LLMs) for food science applications
- Food security, safety, and fraud detection using AI and data mining
- Real-world experiments in food pairing and computational gastronomy

Accepted papers will be included in the ICDM Workshop Proceedings (separate from ICDM Main Conference Proceedings), and each workshop paper requires a full registration.