AI/ML researcher · Singapore
Rom
Uddamvathanak.
World models & simulation.
AI for single-cell research.
I’m an AI/ML researcher at A*STAR in Singapore and the maintainer of the DISCO website. My current focus is world models and simulation, alongside foundation models and single-cell omics.
01 / Introduction
A little about me.
Connecting machine learning with biological research.
My current research focus is world models and simulation. My broader interests span foundation models and single-cell omics, with research contributions to spatial transcriptomics and the DISCO platform. I maintain the DISCO website, helping researchers find its atlases, analysis tools, and toolkit resources. My CellGen research explores computational models of cellular responses to perturbations within the AI in Drug Discovery (AIDD) programme.
I studied at Monash University from 2019 to 2021, receiving a Summer Research Scholarship. I enjoy sharing practical resources and learning through hands-on experiments.
For research collaborations, project discussions, and professional introductions, connect with me on LinkedIn.
Featured / Website maintainer
DISCO.
Deeply Integrated human Single-Cell Omics
I maintain the DISCO website, an entry point to human single-cell atlases and tools for exploring and interpreting cellular data.
The portal brings together DISCO V1 and V2 resources, including cell type annotation, mapping, enrichment, integration, and toolkits.
Explore the DISCO websiteExplore atlases
Find integrated cellular references and the V1 and V2 portals.
Analyze and interpret
Access annotation, mapping, enrichment, and integration tools.
Work programmatically
Find Python and R toolkit resources for research workflows.
02 / Selected publications
Research contributions.
Rediscovering publicly available single-cell data with the DISCO platform
Co-authored research on a platform for accessing, integrating, and analyzing human single-cell data.
Unsupervised spatially embedded deep representation of spatial transcriptomics
Co-authored research introducing SEDR, which combines gene expression and spatial information for transcriptomics analysis.
03 / Current research
CellGen &
world models.
Research in progressModelling how cells respond to change.
I’m developing CellGen to model cellular responses to perturbations using single-cell RNA sequencing data. My CellGen work sits within the AI in Drug Discovery (AIDD) programme.
This work connects with my current focus on world models and simulation: learning representations of cellular states and studying how those states may change under different perturbations. The longer-term aim is to support predictive modelling for biological research and drug discovery.
An ongoing research direction, with models and evaluation under development.
04 / Get in touch
Good conversations
start with a hello.
Have a project, a research question, or an idea to share?
Let’s connect.