👥 People
Key researchers in sample-efficient learning and cognitive architectures
🌍 World Models & MBRL
Danijar Hafner Core
Google DeepMind
Creator of Dreamer series (V1-V4), Director. World models, imagination-based learning, MBRL.
David Silver Core
Google DeepMind
AlphaGo, AlphaZero, MuZero. "Reward is Enough" hypothesis. Pioneer of deep RL.
Yann LeCun
Meta AI / NYU
JEPA architecture, world models, self-supervised learning, energy-based models.
Julian Schrittwieser
Google DeepMind
Lead author of MuZero. Planning with learned models.
🔄 Complementary Learning Systems
James McClelland Core
Stanford University
CLS theory pioneer (1995). Connectionist models, memory systems, cognitive neuroscience.
Dharshan Kumaran Core
Google DeepMind / UCL
CLS theory (2016 update), hippocampus, memory consolidation, neuroscience-inspired AI.
Demis Hassabis Core
Google DeepMind (CEO)
Co-author CLS 2016. Neuroscience-AI integration, memory, imagination, AlphaFold.
Randall O'Reilly
UC Davis
Co-author original CLS (1995). Computational cognitive neuroscience, Leabra.
Kimberly Stachenfeld Core
DeepMind / Columbia
Successor Representation, predictive maps. "Hippocampus as Predictive Map" (2017).
Tim Behrens Core
Oxford / UCL
Tolman-Eichenbaum Machine, structural knowledge, cognitive maps.
James Whittington Core
Oxford / Stanford
TEM lead author. Transformers + hippocampal formation, relational memory.
Matthew Botvinick
DeepMind
Meta-RL, prefrontal cortex-hippocampus interactions, cognitive control.
Irina Rish
Mila
Continual learning, sparse modeling, sample efficiency.
📚 Reinforcement Learning Foundations
Richard Sutton Core
University of Alberta / DeepMind
Co-author "RL: An Introduction". Temporal difference learning, policy gradient. Father of modern RL.
Andrew Barto Core
UMass Amherst
Co-author "RL: An Introduction". Intrinsic motivation, hierarchical RL, options framework.
Satinder Singh Core
University of Michigan / DeepMind
Intrinsically motivated RL (2005). Curiosity-driven learning, options.
🤖 Robot Learning
Sergey Levine Core
UC Berkeley
Robot learning, offline RL, real-world deployment. Berkeley Deep RL course.
Pieter Abbeel
UC Berkeley / Covariant
Robot learning, deep RL, sample-efficient learning, imitation learning.
Chelsea Finn
Stanford University
Meta-learning (MAML), few-shot learning, robot manipulation.
Philipp Wu
UC Berkeley
DayDreamer: Dreamer for physical robots. Quadruped learning.
🏛️ Predictive Coding & Cortical Theory
Andre Bastos Core
MIT
"Canonical Microcircuits for Predictive Coding" (2012). Cortical hierarchies.
Rajesh Rao
University of Washington
Predictive coding pioneer (1999), brain-computer interfaces.
Dileep George Core
Vicarious / Alphabet
Recursive Cortical Network, CSCG (Clone Structured Causal Graph). Neuroscience-inspired AI.
🧭 Spatial Cognition & Navigation
Edvard Moser Core
NTNU (Norway)
Nobel Prize 2014. Grid cells, spatial representation in entorhinal cortex.
May-Britt Moser Core
NTNU (Norway)
Nobel Prize 2014. Grid cells, head direction cells, cognitive maps.
John O'Keefe
UCL
Nobel Prize 2014. Place cells, hippocampal cognitive maps.
🎮 Game AI
Oriol Vinyals Core
Google DeepMind
AlphaStar (StarCraft II). Sequence-to-sequence, attention mechanisms.