AI & Machine Learning
Cornell is a recognized leader in AI and Machine Learning (ML). Researchers rely on CAC systems, consulting and expertise to enable AI/ML application innovations.
CAC provides access to HPC resources, including GPU-enable instances, and offers expert training and guidance on the use of both Cornell and external resources such as Empire AI, NSF-funded systems, AWS, Azure, and Google Cloud.
Sample Projects
- Weill Cornell Medicine – Secure Clinical AI
CAC consultants support WCM researchers using large language models (LLMs) on clinical data within secure cloud environments (AWS, GCP). We design and build custom containers with GPU support and specialized software and help optimize infrastructure and code performance.
- Pollinator Health via Deep Learning
In partnership with the University of Massachusetts Amherst and the Cornell Lab of Ornithology, CAC helps develop Deep Learning models on bioacoustic data to identify bee species and behaviors from the buzzing sounds they make in the wild. This is an ongoing Partner Program project supporting biodiversity and ecosystem health.
- arXiv AI Prototype for Institutional Recognition
A Cornell Tech master’s student team, managed by CAC, developed a prototype Retrieval-Augmented Generation (RAG) AI model to recognize institutional affiliations in arXiv submissions. A CAC consultant later validated and deployed a production-ready version in arXiv.
AI/ML Training & Education
CAC provides training in both AI/ML theory and the practical use of software tools, with resources available through:
Topics include:
Collaborative Education – Chishiki.ai Project
In collaboration with the University of Texas at Austin, CAC contributes to the
https://www.chishiki-ai.org/, which provides AI-focused courses
integrating principles and concepts from civil and environmental engineering within the CVW.
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