Funded AI and Machine Learning Projects
Summaries of FSI-funded AI/ML projects and links to datasets, code, and publications (when available)
RFP focus: Creating benchmark datasets and common task frameworks for alternative protein
Tasting stiffness: An open-source dataset for the mechanical features of alternative protein products
Principal Investigator:
Ellen Kuhl, Ph.D.
Catherine Holman Johnson Director of Stanford Bio-X
Walter B. Reinhold Professor in the School of Engineering
Stanford University
As part of this project on Tasting stiffness: An open-source dataset for the mechanical features of alternative protein products, we have developed an open-source dataset and computational framework to systematically quantify the mechanical behavior of alternative protein products [1]. Using a combination of uniaxial tension, compression, and shear tests and biaxial tests, we characterized plant-based and animal meats [2] and deli meats [3] across the full three-dimensional mechanical spectrum. We custom-designed physics-based neural network to automatically discover physics-based models for each product and extract its stiffness and texture parameters [4]. In addition, we have completed IRB-approved sensory texture surveys with human participants to link the measured mechanical stiffness to perceived textural attributes such as softness, hardness, fibrousness, and moistness. All experimental data, testing protocols, and analysis software have been released on our open-source GitHub platform [1] to advance transparency, reproducibility, and innovation in the study of food texture. We have successfully secured follow up funding to gradually test more products and expand this database, for example, to include fungi-based steaks [5].
[1] https://github.com/LivingMatterLab/CANN
[2] Dunne RA, Darwin EC, Perez Medina VA, Levenston ME, St. Pierre SR, Kuhl E. Texture profile analysis and rheology of plant-based and animal meat; Food Res Int. 2025; 115876. (download) (fri)
[3] St. Pierre SR, Somersille Sibley L, Tran S, Tran V, Darwin EC, Kuhl E. Biaxial testing and sensory texture evaluation of plant-based and animal deli meat. Curr Res Food Sci 2025; 10: 101080. (download) (crfs)
[4] Kuhl E. AI for Food: Accelerating and democratizing discovery and innovation. Science of Food. 2025; 9: 82. (download) (sci food)
[5] Vervenne T, St Pierre SR, Famaey N, Kuhl E. Probing mycelium mechanics and taste: The moist and fibrous signature of fungi steak. Acta Biomat. 2025. 22: 341-351. (download) (actabm)
Creating a protein thermostability dataset to accelerate cultured meat and seafood development
Principal Investigator: Breanna Duffy, PhD
Director of Responsible Research & Innovation - US
New Harvest
Understanding growth factor stability across a wide range of temperatures, including production, harvest, storage, and cooking, is important for scaling the cultured meat industry. While recent advances in artificial intelligence have led to models that help predict protein stability, their application remains limited by a lack of public data relevant to cultured meat.
This open-source dataset compiles growth factor thermostability data compatible with AI workflows. It integrates publicly available information collected using a custom LLM-assisted toolset, experimentally derived melting temperature data, and in silico protein feature data. The dataset is intended to support growth factor optimization, guide culture media evaluation, and enable computational workflows that accelerate development in the field.
Links to the dataset/manuscript will be added when publicly available.