News and Events
Chair’s Message
We are moving toward our vision with a number of activities across our various programs. We have updated our strategic plan in response to the 10-year academic program review that we recently completed. For our research-oriented MS and PhD programs, we added a specialization in Data Science. We are completing a curriculum revision for our on line applied clinical informatics MS. The work of our fellows in the clinical informatics fellowship program has received plaudits from clinical administrators and faculty. We are at the beginning of a new cycle of admissions to our graduate programs, and we look forward to another productive year and new growth in our department.
Cordially,
Peter Tarczy-Hornoch, MD
Chair and Professor, Department of Biomedical Informatics and Medical Education
New Global Rankings for UW
In the summer of 2026, the University of Washington was recognized by U.S. News & World Report as one of the best universities in the world.
Biomedical Informatics and Medical Education Newsletter
September 21, 2026 – September 25, 2026
UPCOMING LECTURES AND SEMINARS
BIME 590
Presenter: Peter Tarczy-Hornoch, MD, FACMI
Thursday, October 1st – 11-11:50 am
850 Republican Street, Building C, Room 123 A/B
Zoom Information: https://washington.zoom.us/my/bime590
Speaker will present In-Person
Title:
2026 BIME Vision, History, Strategic Plan and Praxis
Abstract:
The presentation will provide an overview of the Department of Biomedical Informatics and Medical Education through the lens of the current strategic plan. The overview will include vision, history and evolution looking at the synergy between research, education and practice (praxis) as well as the synergy between practice, applied research and foundational research. Within each area (research, praxis, education) current activities and future plans will be reviewed.
Presenter Bio:
Peter Tarczy-Hornoch has 45 years of experience in computer science, over 40 years in biomedical informatics and over 20 years in clinical medicine (pediatrics and neonatology). He has been at the University of Washington since 1992, serving as Head of the Division of Biomedical and Health Informatics since 2001 and serving as Chair of the Department of Biomedical Informatics and Medical Education since 2011. He has served in a variety of operational leadership roles in UW Medicine IT Services since 1992 in the analytics, research, and clinical computing domains, including Chief of Research and Data Integration (2005-15), Chief Data Officer (2022-2026), and Chief Research Information Officer (2024-26). He has played a leadership role in the creation and evolution of the BIME educational programs (undergraduate (joint with iSchool), MS/PhD, postdoctoral, applied clinical informatics MS (CIPCT) joint with Nursing, Clinical Informatics Fellowship joint with Family Medicine). He has led a number of key initiatives in informatics practice (praxis) including the areas of telemedicine, digital library, electronic medical records, data warehousing, analytics, clinical research informatics). His unifying theme of research over the last three decades has been data integration of multimodal electronic biomedical data (clinical, genomic and other data) both for a) knowledge discovery and b) in order to integrate this knowledge with clinical data at the point of care for decision support. His current research focuses on a) secondary use of electronic medical record (EMR) for translational research including outcomes research, learning healthcare systems, patient accrual based on complex phenotypic eligibility criteria, b) the use of EMR systems for cross institutional comparative effectiveness research, and c) integration of genomic data into the EMR for clinical decision support.
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October 2026 UW Medical AI & Data Science Journal Club
Monday, October 12, 2026: 1:00pm Pacific Time
Presenter Asa Gilmore will discuss the paper: Position: Epistemic uncertainty estimation methods are fundamentally incomplete
Join us in person: F107, 750 Republican St, Seattle 98109
-or-
via Zoom: https://washington.zoom.us/j/92158637394?pwd=pi87aK9LVz5Jx7KTPgVq0SX7d2xNIL.1
Meeting ID: 921 5863 7394
Passcode: 424586
Bio: Asa is from Seattle, WA, and is currently a senior at the University of Washington studying Mathematics. Prior to working at the Computational Ophthalmology Lab, he spent three years at the UW Neuroinformatics Research and Development Group, where he worked on building software tools for various brain MRI analysis techniques and training a foundation model for anatomical brain MRI. He is currently working on doing survival analysis for Uveal melanoma from ultra widefield optos images.
The UW Medical AI & Data Science Journal Club is led by Acting Assistant Professor, Yue Wu, PhD, UW Medicine Computational Ophthalmology Lab. Our journal club is open to all who are interested, providing an opportunity to exchange ideas, ask questions, and critique scientific methods related to artificial intelligence and big data that support our collective goal of creating better health.
We meet 2nd Mondays, 1-2pm Pacific Time in person and via zoom. At each meeting, the presenter provides an overview of a paper they selected, providing their thoughts and interpretation, and then the presentation is followed by questions and discussion among participants.
We are seeking additional presenters for 2026-2027. If you are interested in presenting or would like to be added to our mailing list, please contact Yue Wu, PhD
UPCOMING EXAMS
Final Exam
Title: mHealth Adoption and System Integration in Thailand’s Primary Care: A Sequential Mixed-Methods Study of Sor Or Nor Buddy
Student: Chak Charoensilpchai
Date/Time: Tuesday, October 6, 8-10am PT
In-Person Location: HSEB – Health Sciences Education Building, HSEB 426
Zoom: https://washington.zoom.us/j/99323566318
Abstract: Mobile health (mHealth) applications may strengthen primary care by supporting community-based documentation, patient follow up, clinical decision-making, and care coordination. Their value, however, depends on whether they fit frontline workers and connect with existing health information systems. When applications operate separately from local databases, hospital systems, and paper records, they can create additional technical, workflow, and administrative burdens for frontline workers.
This dissertation examines mHealth adoption and system integration in Thailand’s public primary care system through a three-stage mixed-methods study of the Ministry of Public Health-supported Sor Or Nor Buddy mHealth application.
First, a scoping review of 30 peer-reviewed studies of mHealth applications used by community health workers in low- and middle-income countries found that 29 studies lacked evidence of full interoperability within national health information systems
Second, a mixed-methods survey of 128 Thai primary care workers found that perceived usefulness, ease of use, social influence, and facilitating conditions were associated with intention to use Sor Or Nor Buddy, while participants also reported duplicate documentation, incomplete system integration, and administrative burden.
Third, qualitative in-depth interviews with 14 workers showed unreliable connectivity, repetitive data entry, reporting requirements, privacy concerns, and limited access to clinical information shaped their daily use.
Overall, findings show that app-level usability alone is insufficient for sustainable mHealth implementation. Effective digital transformation in primary care requires interoperable systems, reliable infrastructure, reduced documentation burden, appropriate data governance safeguards, and end-user participation in design and implementation. The dissertation proposes a human-centered design roadmap to strengthen mHealth development and implementation in Thailand and similar primary care settings.
COMMUNITY BUILDING
BIME Welcome Reception
We hope you can make it to this year’s annual BIME reception to welcome everyone back and welcome our incoming students, postdocs, and fellows! Refreshments will be served.
When: Tuesday, September 29th from 4:30-6:00 PM
Where: SLU Building C, 1st floor Key Bank Lounge (behind parking ticket kiosk and parking lot elevators)
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Fall 2026 International Graduate Student Welcome Mixer
Date: Friday, October 9, 2026
Time: 4:00 PM – 6:00 PM
Location: Husky Union Building (HUB), Room Lyceum, UW Seattle Campus
Address: 4001 E. Stevens Way NE, Seattle, WA 98195
Calling all new and returning international master’s and doctoral students! Join us for an afternoon of connection, community, food, games, and prizes while meeting fellow Huskies and exploring valuable campus resources.
During this two-hour mixer, you’ll have the chance to:
- Discover campus resources designed to support international graduate students.
- Network with peers from a variety of disciplines and backgrounds.
- Enjoy food, games, and prizes in a welcoming and festive atmosphere.
RSVP is required: https://forms.cloud.microsoft/r/Xn3xAEV8bV.
September 14, 2026 – September 18, 2026
UPCOMING LECTURES AND SEMINARS
BIME 590 – See you in fall quarter – 10/1/2026!
_________________________________________
September 2026 UW Medical AI & Data Science Journal Club
Monday, September 21, 2026: 1:00pm Pacific Time
Presenter Faiz Moazzam will discuss the paper: Development and validation of a deep learning model to predict visual and anatomical prognosis of anti-VEGF therapy for neovascular age-related macular degeneration (KongMing Study): a prospective, nationwide, multicentre study
Join us in person: F107, 750 Republican St, Seattle 98109
-or-
via Zoom: https://washington.zoom.us/j/92158637394?pwd=pi87aK9LVz5Jx7KTPgVq0SX7d2xNIL.1
Meeting ID: 921 5863 7394
Passcode: 424586
Bio: Faiz Moazzam is an incoming second-year student. He joined the Computational Ophthalmology Lab in October 2025 and presented his work earlier this year at ARVO 2026. Faiz is currently finalizing his first manuscript and recently participated in a summer fellowship in biomedical machine learning. Faiz is excited to explore the connections between ophthalmology and retinal biomarkers for Alzheimer’s/dementia, and especially how it connects to machine learning. He is looking for new opportunities to learn and grow as a student and engineer and is excited to connect and collaborate. https://www.linkedin.com/in/faiz-moazzam/
ANNOUNCEMENTS
BIME Welcome Reception
We hope you can make it to this year’s annual BIME reception to welcome everyone back and welcome our incoming students, postdocs, and fellows! Refreshments will be served.
When: Tuesday, September 29th from 4:30-6:00 PM
Where: SLU Building C, 1st floor Key Bank Lounge (behind parking ticket kiosk and parking lot elevators)
UPCOMING EXAMS
Final Exam
Title: mHealth Adoption and System Integration in Thailand’s Primary Care: A Sequential Mixed-Methods Study of Sor Or Nor Buddy
Student: Chak Charoensilpchai
Date/Time: Tuesday, October 6, 8-10am PT
In-Person Location: HSEB – Health Sciences Education Building, HSEB 426
Zoom: https://washington.zoom.us/j/99323566318
Abstract: Mobile health (mHealth) applications may strengthen primary care by supporting community-based documentation, patient follow up, clinical decision-making, and care coordination. Their value, however, depends on whether they fit frontline workers and connect with existing health information systems. When applications operate separately from local databases, hospital systems, and paper records, they can create additional technical, workflow, and administrative burdens for frontline workers.
This dissertation examines mHealth adoption and system integration in Thailand’s public primary care system through a three-stage mixed-methods study of the Ministry of Public Health-supported Sor Or Nor Buddy mHealth application.
First, a scoping review of 30 peer-reviewed studies of mHealth applications used by community health workers in low- and middle-income countries found that 29 studies lacked evidence of full interoperability within national health information systems
Second, a mixed-methods survey of 128 Thai primary care workers found that perceived usefulness, ease of use, social influence, and facilitating conditions were associated with intention to use Sor Or Nor Buddy, while participants also reported duplicate documentation, incomplete system integration, and administrative burden.
Third, qualitative in-depth interviews with 14 workers showed unreliable connectivity, repetitive data entry, reporting requirements, privacy concerns, and limited access to clinical information shaped their daily use.
Overall, findings show that app-level usability alone is insufficient for sustainable mHealth implementation. Effective digital transformation in primary care requires interoperable systems, reliable infrastructure, reduced documentation burden, appropriate data governance safeguards, and end-user participation in design and implementation. The dissertation proposes a human-centered design roadmap to strengthen mHealth development and implementation in Thailand and similar primary care settings.
PAPERS, PUBLICATIONS & PRESENTATIONS
- Islam H, Jayarman P, Reid N et al. The Roles of Biomedical Informatics for an Equitable Learning Health System: Lessons from a Year of Scientific Informatics Literature. Methods Inf Med. DOI: 10.1055/a-2937-0980
https://www.thieme-connect.com/products/ejournals/html/10.1055/a-2937-0980
- AMIA iKnow Fireside Chat between Nick Reid (UW) and Laura Wiley (WashU) about how building life-long connections, through “being open to opportunities, but also support and friendships…” can completely change one’s trajectory, professional and personal, in most unexpected ways! (and baking cupcakes)
https://open.spotify.com/show/5ZxV8DHYYriVZP3UJdXhnk
- Liu, J. Gu, Z. Chen, B. Chu, L. Liu, T. Morrison, R.R. Butler, J. Edelson, J. Li, F.M. Longo, H. Tang, I. Ionita-Laza, C. Sabatti, E. Candès, & Z. He, Uncovering heterogeneous effects via localized feature selection, Proc. Natl. Acad. Sci. U.S.A. 123 (38) e2527033123, https://doi.org/10.1073/pnas.2527033123 (2026).
August 17, 2026 – August 21, 2026
UPCOMING LECTURES AND SEMINARS
BIME 590 – See you in fall quarter – 10/1/2026!
_________________________________________
September 2026 UW Medical AI & Data Science Journal Club
Monday, September 21, 2026: 1:00pm Pacific Time
Presenter Faiz Moazzam will discuss the paper: Development and validation of a deep learning model to predict visual and anatomical prognosis of anti-VEGF therapy for neovascular age-related macular degeneration (KongMing Study): a prospective, nationwide, multicentre study
Join us in person: F107, 750 Republican St, Seattle 98109
-or-
via Zoom: https://washington.zoom.us/j/92158637394?pwd=pi87aK9LVz5Jx7KTPgVq0SX7d2xNIL.1
Meeting ID: 921 5863 7394
Passcode: 424586
Bio: Faiz Moazzam is an incoming second-year student. He joined the Computational Ophthalmology Lab in October 2025 and presented his work earlier this year at ARVO 2026. Faiz is currently finalizing his first manuscript and recently participated in a summer fellowship in biomedical machine learning. Faiz is excited to explore the connections between ophthalmology and retinal biomarkers for Alzheimer’s/dementia, and especially how it connects to machine learning. He is looking for new opportunities to learn and grow as a student and engineer and is excited to connect and collaborate. https://www.linkedin.com/in/faiz-moazzam/
_________________________________________
The UW Medical AI & Data Science Journal Club is led by Acting Assistant Professor, Yue Wu, PhD, , UW Medicine Computational Ophthalmology Lab. Our journal club is open to all who are interested, providing an opportunity to exchange ideas, ask questions, and critique scientific methods related to artificial intelligence and big data that support our collective goal of creating better health.
We generally meet on 2nd Mondays, 1-2pm Pacific Time in person and via zoom (exception: 3rd Monday in September 2026). At each meeting, the presenter provides an overview of a paper they selected, providing their thoughts and interpretation, and then the presentation is followed by questions and discussion among participants.
We are seeking additional presenters for 2026-2027. If you are interested in presenting or would like to be added to our mailing list, please contact Yue Wu, PhD
ANNOUNCEMENTS
The University of Washington Institute for Medical Data Science is excited to announce the winners of the 2026-27 Pilot Awards. Now in its fourth year, the Pilot Award initiative seeks to stimulate research that enhances UW real-world data from the Electronic Medical Record and other sources of patient data. Awardees were chosen from a wide range of applicants across many fields. This year’s recipients are working toward improving personalized chemotherapy medication preparation, towards predicting rearrest incidents following out-of-hospital cardiac arrest, as well as developing approaches to safer and broader access to anti-amyloid Alzheimer’s therapy.
The winners of the 2026-2027 IMDS Pilot Awards are:
Hesamoddin Jahanian, with Ali Shojaie and Thomas Grubowski
‘Individualized Prediction of Amyloid-Related Imaging Abnormalities (ARIA) for Safer and Broader Access to Anti-Amyloid Alzheimer’s Therapy’
Nicholas Johnson, with Chen Liang and Daniel To
‘Multimodal Biosignal Analysis for Prediction of Rearrest Following Out-of-Hospital Cardiac Arrest’
Kelly Michaelsen, with Stanley Tan and Anthony Lee
‘Computer Vision to Automate Assessment of Personalized Chemotherapy Medication Preparation to Prevent Medication Errors’
You can read more about their projects and IMDS Pilot Awards in general at the below link:
2026 Pilot Award Recipients | University of Washington Institute of Medical Data Science
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Natera is seeking an innovative and driven bioinformatics scientist to lead and conduct cutting-edge real-world evidence (RWE) analyses and predictive analytics across oncology, organ health, and women’s health datasets. This unique role blends expertise in bioinformatics, artificial intelligence (AI), machine learning (ML), and the manipulation of complex real-world data (RWD). Candidate will leverage advanced AI methodologies to extract actionable clinical insights from vast multimodal datasets (genomics, clinical, demographic), driving impactful data visualization and advancing our application of genomics in a real-world clinical setting. The ideal candidate should have strong project management skills, and a keen eye for visualizing complex data in an impactful way to advance our understanding and application of genomics in a real-world setting.
For Full Details and to apply: https://job-boards.greenhouse.io/natera/jobs/6128396004
_________________________________________
JP Giliberto (Assistant CCIO for Surgery) was senior author and Teresa Kim (former CI fellow and otolaryngologist/informatician at Seattle Childrens) was first author on an important multi center study of EHR documentation burden by gender and academic rank (46 academic departments of otolaryngology). They found that female junior faculty spent almost twice as much time in documentation tasks associated with ambulatory visits than their male senior faculty colleagues. Male senior faculty received more assistance with notes and orders than their junior female colleagues.
Kim T, Abdel-Aty Y, Akkina SR, et al. Gender and academic rank disparities in electronic health record burden among otolaryngologists. The Laryngoscope 2026. 136(suppl): S7-S20.
This study builds on previous single institution studies that suggested similar gender gaps.
PAPERS, PUBLICATIONS & PRESENTATIONS
- STCCP: A Modular, Scalable Pipeline for Single-Cell Spatial Transcriptomic Profiling of the Glioblastoma Tumor Microenvironment” (Zeng, W., Schneidereit, E., Reichel, C., Ernau, F., Oyaizu, S., Damodarasamy, M., Wilson, A., Keene, C., Phuong, J., Galbraith, K.), was accepted as a poster for presentation at the 2026 AMP Annual Meeting & Expo, to be held November 10-14.
COMMUNITY BUILDING
Research After Dark: Happy Hour
Research after Dark (RAD) Networking is holding a community building and networking no-hosted happy hour on Thursday, August 27 at 5 pm, at Flatstick Pub located at 609 Westlake Ave N, Seattle, WA 98109. This is a great opportunity to network with Seattle area professionals and trainees!
RAD Networking group is led by graduate students and postdocs from Fred Hutch, Seattle Children’s and UW organizing networking events for trainees in the Seattle area.
August 10, 2026 – August 14, 2026
UPCOMING LECTURES AND SEMINARS
BIME 590 – See you in fall quarter – 10/1/2026!
_________________________________________
September 2026 UW Medical AI & Data Science Journal Club
Monday, September 21, 2026: 1:00pm Pacific Time
Presenter Faiz Moazzam will discuss the paper: Development and validation of a deep learning model to predict visual and anatomical prognosis of anti-VEGF therapy for neovascular age-related macular degeneration (KongMing Study): a prospective, nationwide, multicentre study
Join us in person: F107, 750 Republican St, Seattle 98109
-or-
via Zoom: https://washington.zoom.us/j/92158637394?pwd=pi87aK9LVz5Jx7KTPgVq0SX7d2xNIL.1
Meeting ID: 921 5863 7394
Passcode: 424586
Bio: Faiz Moazzam is an incoming second-year student. He joined the Computational Ophthalmology Lab in October 2025 and presented his work earlier this year at ARVO 2026. Faiz is currently finalizing his first manuscript and recently participated in a summer fellowship in biomedical machine learning. Faiz is excited to explore the connections between ophthalmology and retinal biomarkers for Alzheimer’s/dementia, and especially how it connects to machine learning. He is looking for new opportunities to learn and grow as a student and engineer and is excited to connect and collaborate. https://www.linkedin.com/in/faiz-moazzam/
_________________________________________
The UW Medical AI & Data Science Journal Club is led by Acting Assistant Professor, Yue Wu, PhD, , UW Medicine Computational Ophthalmology Lab. Our journal club is open to all who are interested, providing an opportunity to exchange ideas, ask questions, and critique scientific methods related to artificial intelligence and big data that support our collective goal of creating better health.
We generally meet on 2nd Mondays, 1-2pm Pacific Time in person and via zoom (exception: 3rd Monday in September 2026).
At each meeting, the presenter provides an overview of a paper they selected, providing their thoughts and interpretation, and then the presentation is followed by questions and discussion among participants.
We are seeking additional presenters for 2026-2027. If you are interested in presenting, or would like to be added to our mailing list, please contact Emily Heindsmann.
ANNOUNCEMENTS
Please join us in congratulating Wesley Surento who successfully passed his final defense!
Title: Quantitative MR Imaging Markers for Breast Cancer Risk Prediction
Abstract: Breast cancer risk assessment can help early prediction and intervention planning, lower mortality, and improve care outcomes. Many current tools utilize risk factors such as age, menopausal status, personal biopsy history, and family history of breast cancer. Although these characteristics do a reasonable job at capturing risk, they omit all information from imaging studies such as mammograms or MRIs. Even when negative for disease, analysis of these images could provide a number of markers that might also capture risk. There remains much unexplored potential in leveraging imaging markers as part of risk assessment models for prevention and early prediction.
In this work, we aim to develop and assess a new risk prediction model that incorporates MRI-derived features along with clinical risk factors.
In the first step, I collected relevant risk factors for predicting cancer within 5 years from the MRI for a cohort of high-risk patients. I built a REDcap data repository to store their clinical risk factors obtained from electronic health records, such as select demographic information, menopausal status, BRCA mutation, and family history of breast cancer.
In the second step, I identified predictive quantitative breast MRI markers. I extracted quantitative markers of background parenchymal enhancement using an image processing pipeline which I helped develop, and proceeded to assess their ability to predict 5-year cancer risk.
In the third step, I performed multimodal combination of imaging markers and clinical risk factors to predict 5-year cancer. We also explored the performance of these combinations for several cohort subsets. Our findings showed that multimodal combination of imaging markers can improve the predictive performance of a conventional risk assessment tool.
_________________________________________
Janice Sabin: The results of our COVID-19 Stigma study are featured on UW Newsroom this week. This was a qualitative study exploring the perceptions and experiences of frontline healthcare workers during the COVID-19 pandemic. The study found that COVID-19 stigma affected both patients and clinicians. Participants identified factors that contributed to stigma and offered insights in how to reduce stigma in future public health emergencies.
https://newsroom.uw.edu/news-releases/healthcare-workers-reflect-on-covid-19-stigma/
PAPERS, PUBLICATIONS & PRESENTATIONS
- Edelson J, Ying KA, Li Q, Robinson A, Chen A, Keshri R, Phal A, Hayes A, Reyes G, Gennari J, Levy S, Mathieu J, Escobar TM, Baker D, Ruohola-Baker H. Novokine Candidate Prioritization. Poster presented at: University of Copenhagen; Copenhagen, Denmark; August 2026
COMMUNITY BUILDING
Biomedical Trainee Empowered Wellness Circle (Virtual)
Empowered Wellness Circle, a time for biomedical trainees to focus on health and wellbeing with each other and a local counselor. All biomedical postdocs, grad students, and postbacs in Fred Hutch, University of Washington, and Seattle Children’s labs/groups are welcome – and no sign-up necessary. The group meets every fourth Wednesday of the month.
Next meeting August 26 @ 3:00 pm – 4:30 pm
Event Link: Biomedical Trainee Empowered Wellness Circle
August 3, 2026 – August 7, 2026
UPCOMING LECTURES AND SEMINARS
BIME 590 – See you in fall quarter – 10/1/2026!
_________________________________________
September 2026 UW Medical AI & Data Science Journal Club
Monday, September 21, 2026: 1:00pm Pacific Time
Presenter Faiz Moazzam will discuss the paper: Development and validation of a deep learning model to predict visual and anatomical prognosis of anti-VEGF therapy for neovascular age-related macular degeneration (KongMing Study): a prospective, nationwide, multicentre study
Join us in person: F107, 750 Republican St, Seattle 98109
-or-
via Zoom: https://washington.zoom.us/j/92158637394?pwd=pi87aK9LVz5Jx7KTPgVq0SX7d2xNIL.1
Meeting ID: 921 5863 7394
Passcode: 424586
Bio: Faiz Moazzam is an incoming second-year student. He joined the Computational Ophthalmology Lab in October 2025 and presented his work earlier this year at ARVO 2026. Faiz is currently finalizing his first manuscript and recently participated in a summer fellowship in biomedical machine learning. Faiz is excited to explore the connections between ophthalmology and retinal biomarkers for Alzheimer’s/dementia, and especially how it connects to machine learning. He is looking for new opportunities to learn and grow as a student and engineer and is excited to connect and collaborate. https://www.linkedin.com/in/faiz-moazzam/
_________________________________________
The UW Medical AI & Data Science Journal Club is led by Acting Assistant Professor, Yue Wu, PhD, , UW Medicine Computational Ophthalmology Lab. Our journal club is open to all who are interested, providing an opportunity to exchange ideas, ask questions, and critique scientific methods related to artificial intelligence and big data that support our collective goal of creating better health.
We generally meet on 2nd Mondays, 1-2pm Pacific Time in person and via zoom (exception: 3rd Monday in September 2026).
At each meeting, the presenter provides an overview of a paper they selected, providing their thoughts and interpretation, and then the presentation is followed by questions and discussion among participants.
We are seeking additional presenters for 2026-2027. If you are interested in presenting, or would like to be added to our mailing list, please contact Emily Heindsmann.
ANNOUNCEMENTS
Please see below for a couple of job postings from Washington State Department of Health. These recruitments are open to Washington residents and those residing on the ID/WA and OR/WA borders.
1
Public Health Data Scientist Informatics Specialist (DSIS 1) DOH8960
Workforce Pathways Program (WFP) Executive Office for Government and Community Affairs, Office of the State Health Officer (OSHO)
Center for Public Health Informatics, Washington State, Department of HEALTH
Closing: August 12, 2026 (11:59pm Pacific)
2
Data Scientist, Public Health Data Linkage (DSIS 3) DOH8949
Data Scientist, Public Health Data Linkage (DSIS 3) The Opportunity As a Data Scientist you will work within the Linkage and Integrated Data Analysis (LIDA) Unit within the Center for Health Statistics (CHS)
Closing: August 9, 2026 11:59 PM Pacific
Washington State Department of Health At a Glance
_________________________________________
On June 3rd, the Institute for Medical Data Science (IMDS) held its 3rd annual IMDS Symposium at UW’s Center for Urban Horticulture. The keynote speaker was Jeff Leek, Vice President and CDO at Fred Hutch Cancer Center. Also, Andrew Connolly, Director of the eScience Institute, gave a presentation on UW’s deployment of data science and AI across campus. Additionally, there were presentations by previous IMDS Pilot Award recipients, as well as by key selected researchers from among the more than twenty poster presentations. To wrap up the day, IMDS Director and UW CRIO Shawn Murphy presented future directions for IMDS, including its focus on development of AI-based digital twins for patient representation.
The event was well-attended and sparked lively discussion, both following talks and presentations, as well as among attendees enjoying breaks and refreshments under the shaded early summer sunlight in the Center’s lovely, botanic courtyards. You can access recordings of the talks via the IMDS 2026 Symposium page. Keep an eye out in early 2027 for information regarding the next installment of the IMDS Symposium.
UPCOMING EXAMS
Final Exam
Title: Quantitative MR Imaging Markers for Breast Cancer Risk Prediction
Student: Wesley Surento
Date/Time: Tuesday, August 11, 10-12pm PT
In-Person Location: Fred Hutch Yale Building
823 Yale Ave N, Seattle, WA 98109, Room J6-102 (6th floor)
Zoom: : https://washington.zoom.us/my/jhgennari?pwd=TUx0clkwKzdnS1ZQV1dXRnZqMWMzZz09
Abstract: Breast cancer risk assessment can help early prediction and intervention planning, lower mortality, and improve care outcomes. Many current tools utilize risk factors such as age, menopausal status, personal biopsy history, and family history of breast cancer. Although these characteristics do a reasonable job at capturing risk, they omit all information from imaging studies such as mammograms or MRIs. Even when negative for disease, analysis of these images could provide a number of markers that might also capture risk. There remains much unexplored potential in leveraging imaging markers as part of risk assessment models for prevention and early prediction.
In this work, we aim to develop and assess a new risk prediction model that incorporates MRI-derived features along with clinical risk factors.
In the first step, I collected relevant risk factors for predicting cancer within 5 years from the MRI for a cohort of high-risk patients. I built a REDcap data repository to store their clinical risk factors obtained from electronic health records, such as select demographic information, menopausal status, BRCA mutation, and family history of breast cancer.
In the second step, I identified predictive quantitative breast MRI markers. I extracted quantitative markers of background parenchymal enhancement using an image processing pipeline which I helped develop, and proceeded to assess their ability to predict 5-year cancer risk.
In the third step, I performed multimodal combination of imaging markers and clinical risk factors to predict 5-year cancer. We also explored the performance of these combinations for several cohort subsets. Our findings showed that multimodal combination of imaging markers can improve the predictive performance of a conventional risk assessment tool.
COMMUNITY BUILDING
Biomedical Trainee Empowered Wellness Circle (Virtual)
Empowered Wellness Circle, a time for biomedical trainees to focus on health and wellbeing with each other and a local counselor. All biomedical postdocs, grad students, and postbacs in Fred Hutch, University of Washington, and Seattle Children’s labs/groups are welcome – and no sign-up necessary. The group meets every fourth Wednesday of the month.
Next meeting August 26 @ 3:00 pm – 4:30 pm
Event Link: Biomedical Trainee Empowered Wellness Circle
_________________________________________
Graduate Student Research Institute
September 14-18, 2026, Virtual
Join the UW Libraries this summer for the eighth annual Graduate Student Research Institute (GSRI)! The Graduate Student Research Institute (GSRI) is a free and asynchronous multi-day online workshop offered by the UW Libraries with the goal of motivating new and returning UW graduate students to explore tools and strategies to research smarter, not harder.
GSRI can help you to:
Learn skills and strategies for effective academic research
Become familiar with core tools and resources for research productivity
Connect with relevant support units across the UW campuses
Build community with other UW students and campus support staff
Students receive five days of online guidance and support from a team of Libraries volunteers as they work through key content and activities from the openly available Graduate Student Research Institute site. There are no required meeting times and learning can take place anytime within the span of the workshop session.
Additional benefits for registered students include access to:
Daily tips and resources for research, using the Libraries, and being a graduate student at UW
Optional synchronous live events and programming
All incoming or current UW graduate students are welcome to register for GSRI.
For questions, email: uwlibidteam@uw.edu.
July 27, 2026 – July 31, 2026
UPCOMING LECTURES AND SEMINARS
BIME 590 – See you in fall quarter – 10/1/2026!
_________________________________________
September 2026 UW Medical AI & Data Science Journal Club
Monday, September 21, 2026: 1:00pm Pacific Time
Presenter Faiz Moazzam will discuss the paper: Development and validation of a deep learning model to predict visual and anatomical prognosis of anti-VEGF therapy for neovascular age-related macular degeneration (KongMing Study): a prospective, nationwide, multicentre study
Join us in person: F107, 750 Republican St, Seattle 98109
-or-
via Zoom: https://washington.zoom.us/j/92158637394?pwd=pi87aK9LVz5Jx7KTPgVq0SX7d2xNIL.1
Meeting ID: 921 5863 7394
Passcode: 424586
Bio: Faiz Moazzam is an incoming second-year student. He joined the Computational Ophthalmology Lab in October 2025 and presented his work earlier this year at ARVO 2026. Faiz is currently finalizing his first manuscript and recently participated in a summer fellowship in biomedical machine learning. Faiz is excited to explore the connections between ophthalmology and retinal biomarkers for Alzheimer’s/dementia, and especially how it connects to machine learning. He is looking for new opportunities to learn and grow as a student and engineer and is excited to connect and collaborate. https://www.linkedin.com/in/faiz-moazzam/
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The UW Medical AI & Data Science Journal Club is led by Acting Assistant Professor, Yue Wu, PhD, , UW Medicine Computational Ophthalmology Lab. Our journal club is open to all who are interested, providing an opportunity to exchange ideas, ask questions, and critique scientific methods related to artificial intelligence and big data that support our collective goal of creating better health.
We generally meet on 2nd Mondays, 1-2pm Pacific Time in person and via zoom (exception: 3rd Monday in September 2026).
At each meeting, the presenter provides an overview of a paper they selected, providing their thoughts and interpretation, and then the presentation is followed by questions and discussion among participants.
We are seeking additional presenters for 2026-2027. If you are interested in presenting, or would like to be added to our mailing list, please contact Emily Heindsmann.
ANNOUNCEMENTS
Washington Health Summary (WAHS) won a Phase 1 Award in the Office of the National Coordinator for Health IT-sponsored EHIgnite Challenge. The award provides a small amount of prize money and a chance to compete in Phase 2. https://healthit.gov/blog/interoperability/nine-teams-one-mission-meet-the-ehignite-phase-1-winners/
WAHS is an app that supports patient-controlled integration and sharing of health records using standard such as FHIR for native data representation, the US Core FHIR API required by the federal 21st Century Cures Act, Blue Button and other Insurance industry standards for patient facing access, Occupational Data for Health, Advance Directives, and SMART Health Links for secure sharing of verified health data. WAHS is a joint initiative of the UW Clinical Informatics Research Group, the UW Digital Initiatives Group, and the Washington State Department of Health.
https://myhealthsummary.demo.cirg.uw.edu/. Contact Bill Lober or Jan Flowers.
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Please join us in congratulating Dr. Faisal Yaseen who successfully passed his final dissertation defense!
Title: Uncertainty-Aware Multimodal and Multiscale Treatment Response Prediction in Advanced Non-Small Cell Lung Cancer
Abstract: Managing patients with metastatic non-small cell lung cancer (mNSCLC) remains challenging due to highly variable responses to standard of care chemoimmunotherapy, with many patients experiencing toxicity and limited clinical benefit. These variable responses are associated with spatially complex heterogeneity that manifests across patients, between organs, and within tumors on medical imaging. Despite its widespread use to inform treatment decisions, programmed death ligand 1 (PD-L1) expression provides limited predictive value and is sensitive to sampling variability due to tumor heterogeneity. Furthermore, conventional response assessment by the Response Evaluation Criteria in Solid Tumors (RECIST) is based on changes in tumor size and does not capture functional or spatial variation within lesions, limiting its ability to guide treatment selection, adaptation, and radiotherapy planning. Current clinical decision-making is also primarily based on point predictions that do not quantify uncertainty, reducing their reliability in high stakes clinical settings. This dissertation addresses these limitations through a progression from biomarker discovery to an uncertainty aware, multimodal, and multiscale framework of informatics methods for treatment response prediction in advanced NSCLC.
The work is structured around three distinct but complementary research objectives that collectively address these limitations. The dissertation first identifies fluorodeoxyglucose positron emission tomography (FDG-PET) imaging and circulating immune biomarkers, including T cell receptor and cytokine metrics, that are associated with treatment response and long-term outcomes in metastatic NSCLC, benchmarked against PD-L1. It then integrates these biomarkers into a multimodal framework that couples response prediction with conformal prediction to quantify patient-specific uncertainty, and demonstrates translational potential through a web-based clinical decision support prototype. To model spatial heterogeneity, it identifies a robust variogram that characterizes tumor spatial correlation across disease stages and treatment regimens. Building on this spatial foundation, it develops a multiscale framework for voxel-level response prediction across primary and metastatic lesions, and introduces a residual-variance conformal method that restores valid uncertainty under spatial dependence.
Across these contributions, a central informatics theme is that predictions become clinically useful only when their reliability is explicitly quantified. By embedding uncertainty quantification at every scale, from patient- to voxel-level, this dissertation moves beyond prediction sets and intervals toward trustworthy, clinically actionable outputs. Although developed and evaluated in advanced NSCLC, the resulting informatics methods are generalizable and can be adapted to other solid tumors and broader biomedical informatics domains.
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Please join us in congratulating Xiaoyi Zhang on successfully defending her doctoral dissertation!
Her dissertation, entitled “Advancing Population Health Management with Artificial Intelligence: Prediction, Interpretation and Outcome Measurement in Real-World Health Care Systems”, addressed critical gaps in the path from AI model development to practical utility, through development of clinically meaningful performance metrics; harmonization of data from outside a healthcare system for more accurate assessment of model performance; prioritization of actionable automated explanatory rules to bridge the gap from model decision to clinical action; assessment of the extent to which deep learning models can obviate the need for extensive feature engineering; and improvement of the granularity of outcome measurement. Xiaoyi gave a clear presentation of her extensive body of work in these areas, and how it advances healthcare AI from a focus on validation set accuracy toward clinical utility. She displayed her command of this work and knowledge of the field in the lively discussion session that followed.
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On June 3rd, the Institute for Medical Data Science (IMDS) held its 3rd annual IMDS Symposium at UW’s Center for Urban Horticulture. The keynote speaker was Jeff Leek, Vice President and CDO at Fred Hutch Cancer Center. Also, Andrew Connolly, Director of the eScience Institute, gave a presentation on UW’s deployment of data science and AI across campus. Additionally, there were presentations by previous IMDS Pilot Award recipients, as well as by key selected researchers from among the more than twenty poster presentations. To wrap up the day, IMDS Director and UW CRIO Shawn Murphy presented future directions for IMDS, including its focus on development of AI-based digital twins for patient representation.
The event was well-attended and sparked lively discussion, both following talks and presentations, as well as among attendees enjoying breaks and refreshments under the shaded early summer sunlight in the Center’s lovely, botanic courtyards. You can access recordings of the talks via the IMDS 2026 Symposium page. Keep an eye out in early 2027 for information regarding the next installment of the IMDS Symposium.
UPCOMING EXAMS
Final Exam
Title: Quantitative MR Imaging Markers for Breast Cancer Risk Prediction
Student: Wesley Surento
Date/Time: Tuesday, August 11, 10-12pm PT
In-Person Location: Fred Hutch Yale Building
823 Yale Ave N, Seattle, WA 98109, Room J6-102 (6th floor)
Zoom: : https://washington.zoom.us/my/jhgennari?pwd=TUx0clkwKzdnS1ZQV1dXRnZqMWMzZz09
Abstract: Breast cancer risk assessment can help early prediction and intervention planning, lower mortality, and improve care outcomes. Many current tools utilize risk factors such as age, menopausal status, personal biopsy history, and family history of breast cancer. Although these characteristics do a reasonable job at capturing risk, they omit all information from imaging studies such as mammograms or MRIs. Even when negative for disease, analysis of these images could provide a number of markers that might also capture risk. There remains much unexplored potential in leveraging imaging markers as part of risk assessment models for prevention and early prediction.
In this work, we aim to develop and assess a new risk prediction model that incorporates MRI-derived features along with clinical risk factors.
In the first step, I collected relevant risk factors for predicting cancer within 5 years from the MRI for a cohort of high-risk patients. I built a REDcap data repository to store their clinical risk factors obtained from electronic health records, such as select demographic information, menopausal status, BRCA mutation, and family history of breast cancer.
In the second step, I identified predictive quantitative breast MRI markers. I extracted quantitative markers of background parenchymal enhancement using an image processing pipeline which I helped develop, and proceeded to assess their ability to predict 5-year cancer risk.
In the third step, I performed multimodal combination of imaging markers and clinical risk factors to predict 5-year cancer. We also explored the performance of these combinations for several cohort subsets. Our findings showed that multimodal combination of imaging markers can improve the predictive performance of a conventional risk assessment tool.
COMMUNITY BUILDING
Graduate Student Research Institute
September 14-18, 2026, Virtual
Join the UW Libraries this summer for the eighth annual Graduate Student Research Institute (GSRI)! The Graduate Student Research Institute (GSRI) is a free and asynchronous multi-day online workshop offered by the UW Libraries with the goal of motivating new and returning UW graduate students to explore tools and strategies to research smarter, not harder.
GSRI can help you to:
Learn skills and strategies for effective academic research
Become familiar with core tools and resources for research productivity
Connect with relevant support units across the UW campuses
Build community with other UW students and campus support staff
Students receive five days of online guidance and support from a team of Libraries volunteers as they work through key content and activities from the openly available Graduate Student Research Institute site. There are no required meeting times and learning can take place anytime within the span of the workshop session.
Additional benefits for registered students include access to:
Daily tips and resources for research, using the Libraries, and being a graduate student at UW
Optional synchronous live events and programming
All incoming or current UW graduate students are welcome to register for GSRI.
For questions, email: uwlibidteam@uw.edu.
July 20, 2026 – July 24, 2026
UPCOMING LECTURES AND SEMINARS
BIME 590 – See you in fall quarter – 10/1/2026!
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PAPERS, PUBLICATIONS & PRESENTATIONS
- Zhang, C. Wilson, H. Eyre, D. Reed, G. Luo, and S.B. Zeliadt. Extracting Pain Severity and Functional Interference from Clinical Narratives Using Domain-Informed Large Language Models: Study Protocol. JMIR Research Protocols, 2026.
- D’Amore JD, Vallapu ER, Samal L, Karras BT, Lober WB. From Pandemic Response to Kill the Clipboard: Patient-Controlled Sharing of Health Data Using International Patient Summary (IPS) and QR codes.Appl Clin Inform. 2026 May;17(3):559-567. doi: 10.1055/a-2902-8515. Epub 2026 Jun 29. PMID: 42372787; PMCID: PMC13354518
https://pubmed.ncbi.nlm.nih.gov/42372787/
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UPCOMING EXAMS
Final Exam
Title: Uncertainty-Aware Multimodal and Multiscale Treatment Response Prediction in Advanced Non-Small Cell Lung Cancer
Student: Faisal Yaseen
Date/Time: Tuesday, July 28, 10-12pm PT
In-Person Location: SLU C122
Zoom: : https://washington.zoom.us/my/jhgennari?pwd=TUx0clkwKzdnS1ZQV1dXRnZqMWMzZz09
Abstract: Managing patients with metastatic non-small cell lung cancer (mNSCLC) remains challenging due to highly variable responses to standard of care chemoimmunotherapy, with many patients experiencing toxicity and limited clinical benefit. These variable responses are associated with spatially complex heterogeneity that manifests across patients, between organs, and within tumors on medical imaging. Despite its widespread use to inform treatment decisions, programmed death ligand 1 (PD-L1) expression provides limited predictive value and is sensitive to sampling variability due to tumor heterogeneity. Furthermore, conventional response assessment by the Response Evaluation Criteria in Solid Tumors (RECIST) is based on changes in tumor size and does not capture functional or spatial variation within lesions, limiting its ability to guide treatment selection, adaptation, and radiotherapy planning. Current clinical decision-making is also primarily based on point predictions that do not quantify uncertainty, reducing their reliability in high stakes clinical settings. This dissertation addresses these limitations through a progression from biomarker discovery to an uncertainty aware, multimodal, and multiscale framework of informatics methods for treatment response prediction in advanced NSCLC.
The work is structured around three distinct but complementary research objectives that collectively address these limitations. The dissertation first identifies fluorodeoxyglucose positron emission tomography (FDG-PET) imaging and circulating immune biomarkers, including T cell receptor and cytokine metrics, that are associated with treatment response and long-term outcomes in metastatic NSCLC, benchmarked against PD-L1. It then integrates these biomarkers into a multimodal framework that couples response prediction with conformal prediction to quantify patient-specific uncertainty, and demonstrates translational potential through a web-based clinical decision support prototype. To model spatial heterogeneity, it identifies a robust variogram that characterizes tumor spatial correlation across disease stages and treatment regimens. Building on this spatial foundation, it develops a multiscale framework for voxel-level response prediction across primary and metastatic lesions, and introduces a residual-variance conformal method that restores valid uncertainty under spatial dependence.
Across these contributions, a central informatics theme is that predictions become clinically useful only when their reliability is explicitly quantified. By embedding uncertainty quantification at every scale, from patient- to voxel-level, this dissertation moves beyond prediction sets and intervals toward trustworthy, clinically actionable outputs. Although developed and evaluated in advanced NSCLC, the resulting informatics methods are generalizable and can be adapted to other solid tumors and broader biomedical informatics domains.
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COMMUNITY BUILDING
Discover UW Medicine Summer Markets
Celebrate the sunshine and support local creators at the annual UW Medicine Summer Markets! Hosted by the Whole U, local vendors will be rotating around hospital campuses throughout July and August.
Market hours are 10:00 AM – 2:00 PM and Market Locations:
Thursday, July 30 – Harborview Medical Center
Tuesday, August 18 – UWMC – Northwest
Tuesday, August 25 – UWMC – Montlake
This seasonal event series will rotate across our campuses, providing employees an excellent midday destination to explore an array of items, including:
- Handcrafted jewelry and accessories
- Photography, art prints and stationery
- Candles, soaps and home goods
- Apparel and specialty items
- Local treats and food options
LEARN MORE
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