Publications

Robin, a multi-agent system capable of fully automating both hypothesis generation and data analysis for experimental biology. By integrating literature search agents with data analysis agents, Robin can generate hypotheses, propose experiments, interpret experimental results and generate updated hypotheses, achieving a semi-autonomous approach to scientific discovery

Co-Scientist, a multi-agent artificial intelligence (AI) system built on Gemini for structured scientific thinking and hypothesis generation. Co-Scientist aims to help scientists discover new original knowledge.

scVision, a vision foundation model that reframes single-cell representation learning as a vision problem. It places genes at fixed positions on a single shared, pan-tissue layout so that co-expressed genes become spatial neighbors, turning a transcriptome into an image in which gene programs appear as local texture. scVision pretrains a vision transformer by masked image modelling on 72 million human cells and use the frozen encoder with no fine-tuning.

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.

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

Robin, a multi-agent system capable of fully automating both hypothesis generation and data analysis for experimental biology. By integrating literature search agents with data analysis agents, Robin can generate hypotheses, propose experiments, interpret experimental results and generate updated hypotheses, achieving a semi-autonomous approach to scientific discovery

Co-Scientist, a multi-agent artificial intelligence (AI) system built on Gemini for structured scientific thinking and hypothesis generation. Co-Scientist aims to help scientists discover new original knowledge.

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.

Robin, a multi-agent system capable of fully automating both hypothesis generation and data analysis for experimental biology. By integrating literature search agents with data analysis agents, Robin can generate hypotheses, propose experiments, interpret experimental results and generate updated hypotheses, achieving a semi-autonomous approach to scientific discovery

Co-Scientist, a multi-agent artificial intelligence (AI) system built on Gemini for structured scientific thinking and hypothesis generation. Co-Scientist aims to help scientists discover new original knowledge.

scVision, a vision foundation model that reframes single-cell representation learning as a vision problem. It places genes at fixed positions on a single shared, pan-tissue layout so that co-expressed genes become spatial neighbors, turning a transcriptome into an image in which gene programs appear as local texture. scVision pretrains a vision transformer by masked image modelling on 72 million human cells and use the frozen encoder with no fine-tuning.

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.