Publications

The authors introduce ChatEHR, an internally developed, vendor-agnostic system for integrating large language models (LLMs) directly into clinical workflows within an academic medical center's electronic health record. By enabling real-time access to longitudinal patient data through both automated tasks and an interactive chat interface, the platform successfully reduces manual documentation burden and facilitates decision-making. The authors also establish a robust framework for governance, continuous performance monitoring, and value assessment, demonstrating a replicable model for other health systems.

This paper proposes the development of an AI-Driven Digital Organism (AIDO), a comprehensive, multiscale foundation model system designed to simulate biological systems from the molecular level to entire organisms. By integrating multimodal datasets and hierarchical deep learning architectures, AIDO aims to provide a safe, scalable, and programmable environment for biomedical research and drug discovery. The framework establishes a unified blueprint for modeling complex biological networks, bridging gaps between genotype, phenotype, and environmental influences.

quantum-classical supercomputing: quantum chemistry of protein-ligand complexes

Researchers developed a novel stem cell-based protocol to generate 'cardiobots'—self-organizing aggregates capable of muscle-powered motility. By optimizing mesoderm and cardiogenic induction through precise growth factor signaling and synthetic organizers, the team successfully engineered bio-aggregates with enhanced contractile areas and increased motility compared to existing gastruloid models. This approach provides a new framework for creating autonomous, muscle-propelled biological machines and exploring the evolutionary origins of movement in early multicellular organisms.

One of the biggest hurdles robotics faces is the facet of sophisticated and hard-to-engineer behaviors. Reinforcement learning offers a set of tools, and a framework to address this problem. In parall

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

The authors introduce ChatEHR, an internally developed, vendor-agnostic system for integrating large language models (LLMs) directly into clinical workflows within an academic medical center's electronic health record. By enabling real-time access to longitudinal patient data through both automated tasks and an interactive chat interface, the platform successfully reduces manual documentation burden and facilitates decision-making. The authors also establish a robust framework for governance, continuous performance monitoring, and value assessment, demonstrating a replicable model for other health systems.

The ability to handle single molecules as effectively as macroscopic building-blocks would enable the construction of complex supramolecular structures inaccessible to self-assembly. The fundamental c

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.

The authors introduce ChatEHR, an internally developed, vendor-agnostic system for integrating large language models (LLMs) directly into clinical workflows within an academic medical center's electronic health record. By enabling real-time access to longitudinal patient data through both automated tasks and an interactive chat interface, the platform successfully reduces manual documentation burden and facilitates decision-making. The authors also establish a robust framework for governance, continuous performance monitoring, and value assessment, demonstrating a replicable model for other health systems.

This paper proposes the development of an AI-Driven Digital Organism (AIDO), a comprehensive, multiscale foundation model system designed to simulate biological systems from the molecular level to entire organisms. By integrating multimodal datasets and hierarchical deep learning architectures, AIDO aims to provide a safe, scalable, and programmable environment for biomedical research and drug discovery. The framework establishes a unified blueprint for modeling complex biological networks, bridging gaps between genotype, phenotype, and environmental influences.

quantum-classical supercomputing: quantum chemistry of protein-ligand complexes

Researchers developed a novel stem cell-based protocol to generate 'cardiobots'—self-organizing aggregates capable of muscle-powered motility. By optimizing mesoderm and cardiogenic induction through precise growth factor signaling and synthetic organizers, the team successfully engineered bio-aggregates with enhanced contractile areas and increased motility compared to existing gastruloid models. This approach provides a new framework for creating autonomous, muscle-propelled biological machines and exploring the evolutionary origins of movement in early multicellular organisms.