Su‐In Lee

University of Washington

Full Professor(heuristic)

Explainable Artificial Intelligence (XAI) · Artificial Intelligence

h-index42

i10-index83

Publications173

Citations25,782

Grants

28 total · 6 active

NIH #2P30AG013280-31

Core F EXPLAINABLE ARTIFICIAL INTELLIGENCE FOR AGING

Active

Amount: $336K
Period: 1997-07-15 → 2030-05-31
PIs: 1
Est. capacity / PI: ~0 students

NIH #5R01EB035934-03

XAI-TRUST: Explainable AI Techniques to Rigorously Understand, Scrutinize, and Trust Clinical AI

Active

Amount: $436K
Period: 2024-04-01 → 2028-03-31
PIs: 1
Est. capacity / PI: ~3 students

NIH #5R01EB035934-02

XAI-TRUST: Explainable AI Techniques to Rigorously Understand, Scrutinize, and Trust Clinical AI

Active

Amount: $446K
Period: 2024-04-01 → 2028-03-31
PIs: 1
Est. capacity / PI: ~3 students

Student capacity is a rough estimate from remaining award size, split equally among PIs. Actual funding decisions vary.

Lab Funding Health

Find elsewhere

Institution / lab homepage Search on ResearchGate

In-Field Influence

0.0159

PageRank within Artificial Intelligence co-authorship network

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Admission Signals

New paper: Dissecting and directing pathology foundation models (bioRxiv (Cold Spring Harbor Laboratory))2026-06-16
New preprint: Agents that Matter: Optimizing Multi-Agent LLMs via Removal-Based Attribution2026-05-26
New NIH award: $231K — Illuminating early microglial dysfunction in Alzheimer's disease through integration of explainable AI and iPSC models2024-09-15
New NIH award: $1,762K — IDEAL-XAI: Advancing Explainable AI to Identify Early Driver Events of Alzheimer's Disease2024-08-15
New NIH award: $436K — XAI-TRUST: Explainable AI Techniques to Rigorously Understand, Scrutinize, and Trust Clinical AI2024-04-01
New NIH award: $446K — XAI-TRUST: Explainable AI Techniques to Rigorously Understand, Scrutinize, and Trust Clinical AI2024-04-01
New NIH award: $448K — XAI-TRUST: Explainable AI Techniques to Rigorously Understand, Scrutinize, and Trust Clinical AI2024-04-01
New NIH award: $336K — Core F EXPLAINABLE ARTIFICIAL INTELLIGENCE FOR AGING1997-07-15

Data Freshness

Publications as of 2026-07-01
Grants as of 2026-06-30

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ORCID: 0000-0001-5833-5215

Career Trajectory

Research Cluster Map

Direct collaborators. Node size = influence, colour = community, dashed = past collaboration.

Potential Collaborators

Recent Publications

Last 2 years · showing 12 of 12

Dissecting and directing pathology foundation models
bioRxiv (Cold Spring Harbor Laboratory) · 2026-06-16
Agents that Matter: Optimizing Multi-Agent LLMs via Removal-Based Attribution
ArXiv.org · 2026-05-26
Agents that Matter: Optimizing Multi-Agent LLMs via Removal-Based AttributionPreprint
arXiv (Cornell University) · 2026-05-26
ALEX: Automatic Language Explanations for Interpreting Treatment Effects via Multi-Agents
medRxiv · 2026-05-01
Probabilistic modelling of single-cell bisulfite sequencing data with MethylVI
Nature Machine Intelligence · 2026-04-28
Where to Steer: Input-Dependent Layer Selection for Steering Improves LLM AlignmentPreprint
arXiv (Cornell University) · 2026-04-04
An Explainable AI Framework for Identifying Universal Aging Signatures in Cell EmbeddingsPreprint
bioRxiv (Cold Spring Harbor Laboratory) · 2025-11-10
Transparency of medical artificial intelligence systems
Nature Reviews Bioengineering · 2025-09-10 · 22 citations
Ensembling Sparse AutoencodersPreprint
ArXiv.org · 2025-05-21
Deep profiling of gene expression across 18 human cancers
Nature Biomedical Engineering · 2024-12-17 · 15 citations
LIFT - XAI: Leveraging Important Features in Treatment Effects to Inform Clinical Decision-Making via Explainable AIPreprint
medRxiv · 2024-09-06 · 1 citations
A deep generative model for capturing cell to phenotype relationshipsPreprint
bioRxiv (Cold Spring Harbor Laboratory) · 2024-08-09

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Su‐In Lee

Grants

Core F EXPLAINABLE ARTIFICIAL INTELLIGENCE FOR AGING

XAI-TRUST: Explainable AI Techniques to Rigorously Understand, Scrutinize, and Trust Clinical AI

XAI-TRUST: Explainable AI Techniques to Rigorously Understand, Scrutinize, and Trust Clinical AI

All Grants

Core F EXPLAINABLE ARTIFICIAL INTELLIGENCE FOR AGING

XAI-TRUST: Explainable AI Techniques to Rigorously Understand, Scrutinize, and Trust Clinical AI

XAI-TRUST: Explainable AI Techniques to Rigorously Understand, Scrutinize, and Trust Clinical AI

XAI-TRUST: Explainable AI Techniques to Rigorously Understand, Scrutinize, and Trust Clinical AI

IDEAL-XAI: Advancing Explainable AI to Identify Early Driver Events of Alzheimer's Disease

Illuminating early microglial dysfunction in Alzheimer's disease through integration of explainable AI and iPSC models

Mapping the landscape of the aged human brain for neurodegenerative disease models

Mapping the landscape of the aged human brain for neurodegenerative disease models

Illuminating early microglial dysfunction in Alzheimer's disease through integration of explainable AI and iPSC models

Interpretable Machine Learning to Identify Alzheimer's Disease Therapeutic Targets

Core F: Artificial Intelligence and Bioinformatics

Core F: Artificial Intelligence and Bioinformatics

Core F: Artificial Intelligence and Bioinformatics

Core F: Artificial Intelligence and Bioinformatics

Core F: Artificial Intelligence and Bioinformatics

Interpretable Machine Learning to Identify Alzheimer's Disease Therapeutic Targets

Interpretable Machine Learning to Identify Alzheimer's Disease Therapeutic Targets

Interpretable Machine Learning to Identify Alzheimer's Disease Therapeutic Targets

Interpretable Machine Learning to Identify Alzheimer's Disease Therapeutic Targets

Opening the Black Box of Machine Learning Models

Opening the Black Box of Machine Learning Models

Opening the Black Box of Machine Learning Models

Opening the Black Box of Machine Learning Models

Opening the Black Box of Machine Learning Models

Application of Data Sciences in Traumatic Brain Injury

Application of Data Sciences in Traumatic Brain Injury

A machine learning approach to identify Alzheimer's disease therapeutic targets

A machine learning approach to identify Alzheimer's disease therapeutic targets

Career Trajectory

Research Cluster Map

Potential Collaborators

Recent Publications