Lee Lab of
AI for Biological and Medical Sciences (AIMS)
The AIMS lab, led by Prof Su-In Lee, aims to conceptually and fundamentally advance how AI/ML can be integrated with biomedical sciences by addressing novel, forward-looking and stimulating questions, enabled by advancing foundational AI/ML or applying advanced AI/ML methods. For example, when the primary focus of AI applications in biology and medicine was in accurately predicting patient's outcome or individual's phenotype (e.g., predicting the response to certain chemotherapy based on the patient's gene expression profile), we uniquely focused on why a certain prediction was made, which can help medical professions make diagnoses or decisions on appropriate clinical actions or point to the molecular mechanisms underlying an individual phenotype.
AI models, such as deep neural networks, are transforming biomedical sciences; however, their black-box nature has been a well-known bottleneck impeding the widespread adoption of AI in biomedicine and beyond. These models do not answer the key questions, such as mechanistic explanations or causal relationships for biological understanding, therapeutics, or clinical decisions.
The AIMS lab’s recent research focuses on a broad spectrum of problems, including developing explainable AI (a.k.a. interpretable ML) techniques, identifying the cause and treatment of challenging diseases such as cancer and Alzheimer’s disease, and developing and auditing clinical AI models. See our latest publications.
This year, we created a new course on explainable AI for our professional maters program.
3/23/2021: Ethan Weinberger receives the NSF Graduate Research Fellowship (GRF).
6/16/2020: Gabe Erion receives the F30 fellowship from NIH.
3/27/2020: Will Chen receives the Mary Gates Research Scholarship.
1/17/2020: Scott's TreeExplainer is published as a cover article of the January issue of Nature Machine Intelligence.
Allen School News - Seeing the forest for the trees: UW team advances explainable AI for popular machine learning models used to predict human disease and mortality risks.
11/20/2019: Gabe won the Madrona Prize (1st place) at the 2019 Allen School Annual Research Day.
"CoAI: Cost-Aware Artificial Intelligence for Health Care"
Safiye and Scott won this prize in 2018 and 2017, respectively.
11/19/2019: Scott's SHAP paper (NeurIPS, Dec 2017) is cited 500 times over <2 years after publication.
2/21/2019: Nao's AIControl paper got accepted for publication in Nucleic Acids Research (IF: 11.56). See Allen School News.
12/31/2018: Scott's SHAP paper (NeurIPS oral presentation) got cited 100 times as of today after about 1 year it was published.
11/1/2018: Safiye's EMBARKER project on identifying therapeutic targets for Alzheimer's disease won the Madrona Prize (1st place) at 2018 Allen School Annual Research Day.
GeekWire - From fighting Alzheimer’s to AR captions, UW computer science students show cutting-edge innovations
BusinessWire - Madrona Awards 2018 Madrona Prize to UW Project That Applies Machine Learning to Fighting Alzheimer’s Disease
Last year, Scott won this prize on his model interpretation work, SHAP (NIPS oral presentation) and his Nature BME paper featured on the cover (see below).
10/10/2018: Scott's Prescience paper is published as a cover article of the October issue of Nature Biomedical Engineering.
Nature BME Editorial - Towards trustable machine learning
UW News - Prescience: Helping doctors predict the future
GeekWire - Univ. of Washington researchers unveil Prescience, an AI system that predicts problems during surgery
Allen School News - “Prescience” interpretable machine-learning system for predicting complications during surgery featured in Nature Biomedical Engineering
5/3/2018: Safiye Celik's and Su-In Lee's research is featured in GeekWire.
4/3/2018: Hugh Chen receives the NSF Graduate Research Fellowships Program (GRFP).
3/27/2018: Safiye Celik's MERGE paper (Nature Communications 2018) is recommended in F1000Prime as being of special significance in its field.
3/21/2018: Safiye Celik's research is featured in I Am CSE.
Su-In's lecture was selected as the best talk at CGWI 2018 (Computational Genomics Winter Institute): Interpretable Machine Learning for Precision Medicine.
9/4/2017: Scott Lundberg's SHAP paper is accepted to Neural Information Processing Systems (NIPS) 2017 for Full Oral Presentation.
8/7/2017: Gabriel Erion won the Best Poster Award: "Prediction and Prevention of Perioperative Adverse Events with Machine Learning Models", University of Washington MSTP (MD/PhD program) retreat, 2017.
2/7/2017: Scott Lundberg's ChromNet paper (Genome Biology 2016) is recommended in F1000Prime as being of special significance in its field.
11/20/2016: Scott Lundberg receives the Best Paper Award at the NIPS workshop "Interpretable Machine Learning for Complex Systems".
8/12/2016: Javad Hosseini's GRAB paper is accepted to Neural Information Processing Systems (NIPS) 2016.
7/15/2016: Nao Hiranuma's CloudControl paper is accepted to ACM Conference on Bioinformatics, Computational Biology (ACM-BCB) 2016.
7/6/2016: Safiye Celik's INSPIRE paper is featured in Casey Greene. The future is unsupervised. Science Translational Medicine July 2016.
6/10/2016: Safiye Celik's INSPIRE work is published in Genome Medicine June 2016.
5/4/2016: Maxim Grechkin's DISCERN work is published in PLOS Computational Biology May 2016.
2/8/2016: Javad Hosseini got ranked #1 in the DiMSUM competition. See Hosseini, Smith and Lee. NAACL Workshop SemEval 2016 Task10.
Prof. Su-In Lee's lab seeks to develop interpretable machine learning techniques to learn from big data: (1) how the human genome or protein works, (2) how to improve healthcare, and (3) how to treat challenging diseases such as cancer and Alzheimer's disease. Her research page lists her projects, including treating cancer based on a patient's own expression profile, finding therapeutic targets for Alzheimer's, predicting kidney disease, preventing complications during surgery, enabling pre-hospital predictions for trauma patients, analyzing medical images, and improving our understanding of pan-cancer biology and genome biology.