🎓GeoAcademyGeoVerse Lab
← AI & Computing Sciences College

Generative AI & Foundation Models Center

The Generative AI & Foundation Models Center gives students a rigorous grasp of how large-scale foundation models - LLMs, diffusion models, and multimodal systems - are pretrained, aligned via RLHF, and governed by scaling laws, paired with hands-on training and fine-tuning of such models. Students run projects adapting general-purpose models to geoscience-specific applications, including scientific reasoning and data synthesis, using domain adaptation and retrieval-augmented generation. Graduates emerge as practicing AI engineers capable of customizing and deploying foundation models for a specific scientific domain.

⚙️ GeoAcademy System Administration College🖥️ AI & Computing Sciences College🔬 Basic Sciences College⚡ Intelligent Geophysical Exploration College🛢️ Resource & Energy Engineering College🌍 Applied Geoscience Solutions College💼 Economics, Policy & Strategy for the Future College🎓 Education & Training Development College🚀 Innovation & International Collaboration College
Machine Learning DepartmentComputer Vision DepartmentNatural Language Processing DepartmentHigh-Performance Computing DepartmentQuantum Computing DepartmentGenerative AI & Foundation Models CenterReinforcement Learning & Agents CenterAI Safety & Alignment CenterAI for Science CenterRobotics & Embodied AI Center

Field-based Curriculum · 15

This department's curriculum is built directly on the GeoVerse Lab research fields its professors hold — click a field to see how it is taught.

Sequence Modeling & Connectionist Architectures
Sequence Modeling & Connectionist Architectures
This course teaches sequence modeling — learning ordered dependencies from examp…
David Rumelhart
Diffusion Models & Score-Based Generative Modeling
Diffusion Models & Score-Based Generative Modeling
This course develops a working understanding of diffusion models — generating ne…
Paul Langevin
Statistical & Autoregressive Language Modeling
Statistical & Autoregressive Language Modeling
This course traces how the single objective of next-token prediction yields mode…
Andrey Markov
Generative Probabilistic Modeling & Latent Variable Inference
Generative Probabilistic Modeling & Latent Variable Inference
This course teaches latent-variable generative modelling — treating observed dat…
Pierre-Simon Laplace
Generative Adversarial Networks & Minimax Game-Theoretic Generation
Generative Adversarial Networks & Minimax Game-Theoretic Generation
This course frames generative adversarial training as a two-player zero-sum mini…
Emile Borel
Autoencoders & Latent Representation Learning
Autoencoders & Latent Representation Learning
This course teaches autoencoders — networks trained to compress inputs through a…
Karl Pearson
Scaling Laws & Emergent Behavior in Large Models
Scaling Laws & Emergent Behavior in Large Models
This course teaches the power-law relationships between model, data and compute …
George Kingsley Zipf
Multimodal & Vision-Language Foundation Models
Multimodal & Vision-Language Foundation Models
This course teaches multimodal models that align physically dissimilar images an…
Ferdinand de Saussure
Retrieval-Augmented Generation & Information Retrieval
Retrieval-Augmented Generation & Information Retrieval
This course teaches retrieval-augmented generation, which supplements a model's …
Gerard Salton
Word/Vector Embeddings & Distributional Semantics
Word/Vector Embeddings & Distributional Semantics
This course teaches vector embeddings as the operationalisation of the distribut…
John Rupert Firth
In-Context Learning & Sequential/Adaptive Statistical Inference
In-Context Learning & Sequential/Adaptive Statistical Inference
This course teaches in-context learning — adapting a model to new tasks purely t…
Abraham Wald
Fine-Tuning & Transfer of Learning
Fine-Tuning & Transfer of Learning
This course teaches fine-tuning — adapting pretrained representations to tasks w…
Edward Thorndike
Subword Tokenization
Subword Tokenization
This course teaches subword tokenization, which lets a finite vocabulary process…
David A. Huffman
Knowledge Distillation & Model Compression
Knowledge Distillation & Model Compression
This course teaches knowledge distillation — transferring a large teacher's rich…
Ray Solomonoff
Corpus Taxonomy & Domain-Adapted Data Curation
Corpus Taxonomy & Domain-Adapted Data Curation
This course teaches domain-adapted data curation — organising a training corpus …
Melvil Dewey