Generative AI & Foundation Models Center

Advances large-scale foundation models - LLMs, diffusion models, and multimodal systems, spanning pretraining, RLHF, and scaling laws - and adapts them for scientific reasoning, data synthesis, and geoscience-specific applications through domain adaptation and retrieval-augmented generation.

⚙️ GeoVerse System Administration Division 🖥️ AI & Computing Sciences Division 🔬 Basic Sciences Division ⚡ Intelligent Geophysical Exploration Division 🛢️ Resource & Energy Engineering Division 🌍 Applied Geoscience Solutions Division 💼 Economics, Policy & Strategy Division 🎓 Education & Training Development Division 🚀 Innovation & International Collaboration Division

Machine Learning Center Natural Language Processing Center Computer Vision Center High-Performance Computing Center Quantum Computing Center Generative AI & Foundation Models Center Reinforcement Learning & Agents Center AI Safety & Alignment Center AI for Science Center Robotics & Embodied AI Center

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⭐ Center Chief

Ludwig Boltzmann

Center Head

💡 Statistical mechanics, theoretical basis of Boltzmann machines

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1857–1936

Karl Pearson

Researcher

💡 Invented Principal Component Analysis, the first method for learning a compressed low-dimensional latent representation that best reconstructs high-dimensional data — the linear ancestor of autoencoder-based representation learning

🖥️ AI & Computing Sciences DivisionGenerative AI & Foundation Models Center

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1872–1946

Paul Langevin

Researcher

💡 Formulated the Langevin equation describing stochastic (Brownian) motion under random forcing, the exact stochastic differential equation whose discretization is the sampling procedure in modern diffusion/score-based generative models

🖥️ AI & Computing Sciences DivisionGenerative AI & Foundation Models Center

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1926–2009

Ray Solomonoff

Researcher

💡 Founded algorithmic information theory and universal induction, defining the shortest program (most compressed description) that reproduces a given data pattern as its ideal predictor — the theoretical ideal that knowledge distillation approximates when compressing a large model into a smaller one

🖥️ AI & Computing Sciences DivisionGenerative AI & Foundation Models Center

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1851–1931

Melvil Dewey

Researcher

💡 Invented the Dewey Decimal Classification system for systematically organizing knowledge into hierarchical subject domains, the founding methodology for the domain classification and curation that shapes how domain-specific corpora (e.g. geoscience text) are organized for adapting foundation models

🖥️ AI & Computing Sciences DivisionGenerative AI & Foundation Models Center

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1890–1960

John Rupert Firth

Researcher

💡 Formulated the distributional hypothesis of meaning ('you shall know a word by the company it keeps'), the foundational linguistic principle that word embedding methods (word2vec, GloVe, and transformer embeddings) directly operationalize

🖥️ AI & Computing Sciences DivisionGenerative AI & Foundation Models Center

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1871–1956

Emile Borel

Researcher

💡 Founded early game theory and the minimax concept for zero-sum games, the exact mathematical framework (a two-player minimax game between generator and discriminator) that defines Generative Adversarial Networks

🖥️ AI & Computing Sciences DivisionGenerative AI & Foundation Models Center

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1749–1827

Pierre-Simon Laplace

Researcher

💡 Founded Bayesian probabilistic inference and the systematic theory of inferring unobserved causes (latent variables) from observed data, the direct philosophical and mathematical ancestor of latent-variable generative models (VAEs, diffusion models)

🖥️ AI & Computing Sciences DivisionGenerative AI & Foundation Models Center

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1902–1950

Abraham Wald

Researcher

💡 Founded statistical decision theory and sequential analysis, formalizing how an agent should update inference adaptively as new observations arrive within a single decision process — the statistical-decision-theoretic root of in-context learning within a model's forward pass

🖥️ AI & Computing Sciences DivisionGenerative AI & Foundation Models Center

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1856–1922

Andrey Markov

Researcher

💡 Founded the theory of Markov chains — stochastic processes where the next state depends only on the current state — first applied by Markov himself to model letter sequences in text, the direct mathematical ancestor of all autoregressive language modeling including LLMs

🖥️ AI & Computing Sciences DivisionGenerative AI & Foundation Models Center

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1857–1913

Ferdinand de Saussure

Researcher

💡 Founded structural linguistics and semiotics, establishing the theory of the arbitrary sign (signifier/signified relationship) that underlies how multimodal models must learn shared, grounded representations across language and vision

🖥️ AI & Computing Sciences DivisionGenerative AI & Foundation Models Center

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1927–1995

Gerard Salton

Researcher

💡 Founded modern information retrieval, inventing the vector space model representing documents and queries as vectors ranked by similarity — the exact retrieval mechanism (dense vector search) that retrieval-augmented generation systems use to fetch context for language models

🖥️ AI & Computing Sciences DivisionGenerative AI & Foundation Models Center

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1902–1950

George Kingsley Zipf

Researcher

💡 Discovered Zipf's Law — the power-law distribution of word frequency in natural language — the founding empirical power-law regularity that motivates and parallels the power-law scaling laws governing large language model performance versus size, data, and compute

🖥️ AI & Computing Sciences DivisionGenerative AI & Foundation Models Center

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1942–2011

David Rumelhart

Researcher

💡 Co-developed the backpropagation algorithm for training multilayer neural networks and founded parallel distributed processing (PDP) theory, the direct architectural ancestor of the deep sequence and transformer networks underlying modern foundation models

🖥️ AI & Computing Sciences DivisionGenerative AI & Foundation Models Center

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1925–1999

David A. Huffman

Researcher

💡 Invented Huffman coding, the optimal variable-length prefix-code compression algorithm based on symbol frequency, the same frequency-driven merging principle that Byte-Pair Encoding (BPE) tokenization in modern LLMs directly generalizes

🖥️ AI & Computing Sciences DivisionGenerative AI & Foundation Models Center

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1874–1949

Edward Thorndike

Researcher

💡 Founded the psychological theory of transfer of learning (the 'identical elements' theory of why training on one task improves performance on another), the conceptual ancestor of fine-tuning and transfer learning in foundation models

🖥️ AI & Computing Sciences DivisionGenerative AI & Foundation Models Center