LLMs | Erwin POUSSI

Growing a Virtual Embryo with 3D Convolutional Models

Sat, 01 Aug 2026 00:00:00 +0000

Embryo Growth Simulation

This project trains foundational 3D convolutional models to reproduce embryogenesis in silico, capturing both cell-level gene expression prediction and 3D morphogenetic dynamics. The goal is to learn a generative model of embryonic development that jointly predicts how cells divide, differentiate, and spatially organize — bridging gene regulatory networks with tissue-scale morphology.

The approach leverages volumetric deep learning architectures to model the spatiotemporal evolution of developing embryos, learning from multi-modal developmental biology datasets that combine single-cell transcriptomics with 3D imaging.

I am one of the builders of the Virtual Embryo Challenge — a community benchmark for computational models of embryonic development.

Virtual Embryo Challenge — check it out and don’t hesitate to participate!

Status: More to come soon!

PantheonOS: An Evolvable Multi-Agent Framework for Automatic Genomics Discovery

Fri, 27 Feb 2026 00:00:00 +0000

Pantheon: LLM-Based Bio Assistant

Wed, 01 Oct 2025 00:00:00 +0000

Demo

System overview

This project focuses on the development of Pantheon, an open-source multi-agent LLM platform for biological scientific workflows
(Pantheon Project).

My work specifically targets Gene Panel Selection (GPS), a core capability of Pantheon that automates the design of gene panels for spatial transcriptomics experiments.

The GPS system coordinates five specialized LLM agents, built on open-source models and extended with custom toolchains and domain-specific reasoning. A Leader Agent interprets ambiguous biological intent, decomposes high-level scientific questions into structured computational steps, and orchestrates agents such as the Selection Expert, with iterative validation and error handling.

I am currently fine-tuning and continually pretraining Qwen3 LLMs to power Pantheon’s self-evolving agent backbone, leveraging reinforcement learning from verifiable rewards (RLVR) and tool-augmented inference to build domain-adaptive, evolvable agents for autonomous genomic reasoning. I also collaborate with Vizgen to integrate a reinforcement learning module for autonomous gene panel design by Pantheon.

This research is conducted in collaboration with Prof. Xiaojie Qiu (Qiu Lab, Stanford) and Dr. Weize Xu (Stanford Profile).

Deployed in collaboration with Vizgen, Pantheon processes large-scale multi-omics datasets and produces curated gene panels that previously required extensive manual expertise.

Publication: Xu, Poussi et al., PantheonOS: An Evolvable Multi-Agent Framework for Automatic Genomics Discovery, bioRxiv 2026. Read preprint

GitHub Repository

SelectSmart: Autonomous Gene Panel Design

Wed, 01 Oct 2025 00:00:00 +0000

Training Overview

SelectSmart is a reinforcement-learning framework for gene panel selection in single-cell transcriptomics.
This project was developed as part of coursework with Prof. Mykel Kochenderfer
(AA228 / CS238 – Decision Making under Uncertainty) and extended through research assistantship work with Prof. Xiaojie Qiu
(Qiu Lab, Stanford).

The method combines meta-voted candidate genes, an actor–critic architecture, and a reward balancing clustering fidelity (ARI) with panel-size regularization.

Trained on a 30k-cell kidney dataset and evaluated on an independent CZ Kidney dataset, SelectSmart produces a 500-gene panel that preserves transcriptomic geometry and outperforms classical gene panel selection methods.

Status: 🧪 Active development

Report

📄 Download the technical report (PDF)