Structured Inference, Graphs & Networks for Applied Learning

We develop principled statistical methods for structured and multimodal data with emphasis on graph-based modeling, spatial structures, and high-dimensional analysis in biological applications.

Explore our projects

News & Announcements

Latest publications

New journal publications in 2026

2026

Our work on tensor and sparse topic models and canonical correlation analysis is appearing in JMLR and the Electronic Journal of Statistics.

Read our latest publications

New preprints from the group

2026

Explore our latest work on data selection for fine-tuning, GNNs for brain connectomes, graph-based learning, network dynamics, and microbial communities.

Browse preprints and papers under review

SpeedCP at ICML 2026

2026

Our students presented the group's work on fast, kernel-based conditional conformal prediction at ICML.

See the poster-session photo

Prospective applicants

The lab is not currently accepting applications for student, research assistant, or postdoctoral positions.

What we do

See all projects

GNN Theory & Methods

bias–variance · generalization · model selection

We study statistical properties of GNNs to try and design interpretable, reliable graph learning pipelines.

Structured Estimation

sparse CCA · graph‑constrained models

We develop high‑dimensional estimators that leverage sparsity and known structure for robust inference.

Spatial Transcriptomics

RNA · spatial programs

We deploy our statistical frameworks to uncover spatially organized gene expression patterns and cell–cell interactions from high-resolution transcriptomic data.

Microbial GWAS

thermotolerance · photosynthesis

We use high-dimensional statistical methods to identify genetic and metabolic determinants of key microbial traits, such as thermotolerance and photosynthetic efficiency.

Past Events

SIGNAL Lab students discussing the SpeedCP poster with attendees at ICML 2026

Our students at ICML 2026

2026

Sharing SpeedCP, our work on fast, kernel-based conditional conformal prediction, at the poster session.

Read the paper

SIGNAL Lab members at NeurIPS

SIGNAL Lab at NeurIPS 2025

Dec. 2025, San Diego, USA.

Sowon, Yating, Yeo Jin, and Claire attended NeurIPS 2025 in San Diego.

Maria Hernández Limón Coralie Rousseau Jeffrey Zhang Thomas Janas

Welcome to our newest lab members

October 2025

The lab welcomes Maria, Coralie, Jeff, and Thomas, who will be working on biological applications.

Meet the team

Yeo Jin presenting the group's research on conformal prediction at RSS 2025

Yeo Jin presented at the RSS meeting

Sept 2025, Edinburgh, UK.

She showcased the group's work on conformal prediction.

Sowon attended the MLoG-GenAI workshop at KDD

Aug. 2025, Toronto, Canada.

She presented her work on unsupervised learning for GNNs.

GNNs for the Sciences: from Theory to Practice

The group hosted a workshop at the NITMB!

April 2025, Chicago, USA

The workshop highlighted recent advances and new perspectives in GNN theory.

SIGNAL Lab members at the SNAB Workshop (2024)

Our group attended the SNAB workshop in Nassau!

June 2024, Nassau, Bahamas.

We showcased our work on topic models, GNNs and CCA during the poster session.

GNNs for the Sciences: from Theory to Practice

The group hosted a workshop at IMSI!

Jan. 2024, Chicago, USA

We were lucky to have great speakers and learned a lot about challenges in the deployment of GNNs in the Physical Sciences.