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    • iPSC-Cardio Cells
    • HALO: A Unified Visio
  • Publications
    • Adoption and Use of LLMs at an Academic Medical Center
    • Toward AI-Driven Digital Organism
    • You Can Run, You Can Hide: The Epidemiology and Statistical Mechanics of Zombies
    • embryonic stem cell-derived cardiac organoids via synthetic guidance
    • In vitro generation of human pluripotent stem cell derived lung organoids
    • Generating Self-Assembling Human Heart Organoids Derived from Pluripotent Stem Cells
    • SMAD4: A Critical Regulator of Cardiac Neural Crest Cell Fate and Vascular Smooth Muscle Differentiation. bioRxiv
    • Insights into AI Agent Security from a Large-Scale Red-Teaming Competition
    • TxPert: using multiple knowledge graphs for prediction of transcriptomic perturbation effects
    • Self-organizing human heart assembloids with autologous and developmentally relevant cardiac neural crest-derived tissues
    • Path Planning of Cleaning Robot with Reinforcement Learning
    • Reinforcement Learning Approaches in Social Robotics
    • Robotic Packaging Optimization with Reinforcement Learning
    • A Concise Introduction to Reinforcement Learning in Robotics
    • Robot-R1: Reinforcement Learning for Enhanced Embodied Reasoning in Robotics
    • Robotic Surgery With Lean Reinforcement Learning
    • Residual Reinforcement Learning for Robot Control
    • Autonomous robotic nanofabrication with reinforcement learning
    • Heterogeneous Multi-Robot Reinforcement Learning
    • Robot Air Hockey: A Manipulation Testbed for Robot Learning with Reinforcement Learning
    • Reinforcement learning for freeform robot design
    • Geometric Reinforcement Learning For Robotic Manipulation
    • On-Robot Bayesian Reinforcement Learning for POMDPs
    • Efficient Content-Based Sparse Attention with Routing Transformers
    • A foundation model of transcription across human cell types
    • Transformer AI
    • HALO, a unified VLA model that enables embodied multimodal chain-of-thought (EM-CoT) reasoning through a sequential process of textual task reasoning, visual subgoal prediction for fine-grained guidan
    • HALO: A Unified Vision-Language-Action Model for Embodied Multimodal Chain-of-Thought Reasoning
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SubXeurons

  • i
    iPSC-Cardio Cells

    Pluripotent stem cell-derived organoids can recapitulate significant features of organ development in vitro. We hypothesized that creating human heart organoids by mimicking aspects of in utero gestation (e.g., addition of metabolic and hormonal factors) would lead to higher physiological and anatomical relevance. We find that heart organoids produced using this self-organization-driven developmental induction strategy are remarkably similar transcriptionally and morphologically to age-matched human embryonic hearts. We also show that they recapitulate several aspects of cardiac development, including large atrial and ventricular chambers, proepicardial organ formation, and retinoic acid-mediated anterior-posterior patterning, mimicking the developmental processes found in the post-heart tube stage primitive heart

  • H
    HALO: A Unified Visio

    HALO: A Unified Vision-Language-Action Model for Embodied Multimodal Chain-of-Thought Reasoning

Publications

  • R
    Reinforcement learning for freeform robot design

    Inspired by the necessity of morphological adaptation in animals, a growing body of work has attempted to expand robot training to encompass physical aspects of a robot's design. However, reinforcemen

  • G
    Geometric Reinforcement Learning For Robotic Manipulation

    Reinforcement learning (RL) is a popular technique that allows an agent to learn by trial and error while interacting with a dynamic environment. The traditional Reinforcement Learning (RL) approach h

  • O
    On-Robot Bayesian Reinforcement Learning for POMDPs

    Robot learning is often difficult due to the expense of gathering data. The need for large amounts of data can, and should, be tackled with effective algorithms and leveraging expert information on ro

  • E
    Efficient Content-Based Sparse Attention with Routing Transformers

    Despite its effectiveness, self-attention suffers from quadratic compute and memory requirements with respect to sequence length. Our model, the Routing Transformer, endows self-attention with a sparse routing module based on online k-means while reducing the overall complexity of attention to O(n1.5d) from O(n2d) for sequence length n and hidden dimension d.

  • A
    A foundation model of transcription across human cell types

    Introducing new AI model GET (general expression transformer), an interpretable foundation model designed to uncover regulatory grammars across 213 human fetal and adult cell types

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