Biomedical Informatics and Medical Education > News and Events

News and Events

Chair’s Message

pth-use-this-one 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 have recently added a specialization in Data Science. We are completing a curriculum revision for our on line applied clinical informatics MS which will be effective Fall 2020. The work of our fellows in the clinical informatics fellowship program has received plaudits from clinical administrators and faculty, and we are currently recruiting a new faculty member in our department to assist with this program (view position description). We are also recruiting a faculty member in medical education to start Summer 2020 (view position description). This is 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

The University of Washington Welcomes New Chief Research Information Officer

The University of Washington is delighted to announce Dr. Shawn Murphy, MD, PhD will be joining the University of Washington as a core faculty in Biomedical Informatics and Medical Education (BIME) with a joint appointment in Neurology. In addition to being a core BIME faculty member and attending in an outpatient neurology clinic, he will be serving as UW Medicine Chief Research Information Officer in IT Services, informatics lead of the Institute for Translational Health Sciences (ITHS) Data Science Core, and the Director of the Institute for Medical Data Sciences (IMDS).

“I am honored and excited to join the Data Science workforce at the University of Washington. I have always admired the closeness UW students have to the top teachers in the tech industry. I am hoping to enable Clinical and Informatics Research to use this and take Data Science and AI to a new level at UW,” said Dr. Murphy.

Dr. Murphy is currently the Chief Research Information Officer at Mass General Brigham and a Professor of Neurology. Over the past 30 years, he has been an integral leader and innovator in supporting research data and technology there. He is the creator of the Research Patient Data Registry (RPDR) and co-founded i2b2 (used in > 300 sites globally) enabling scalable, audit-ready clinical data access for the research community nationally and internationally. Shawn has also been a leading scientific collaborator and principal investigator on numerous multi-institutional NIH grants, including RECOVER, eMerge, ACT, AoU, PCORI, SHRINE and other major award programs that helped elevate MGB into a leader in translational informatics. His work has shaped standards for privacy-preserving data sharing, cohort discovery tools, and clinical data harmonization, influencing research data operations across Mass General Brigham hospitals and enabling thousands of studies per year.

“We are delighted to have Dr. Murphy join UW, bringing decades of expertise in informatics and data science research and operational work around instrumenting the health care enterprise for discovery in the course of clinical care,” said Dr. Peter Tarczy-Hornoch, Chair of BIME and Interim Director for IMDS.

“ITHS welcomes Dr. Murphy to this crucial role. He is exactly the right person to take on the new informatics challenges of clinical and translational research,” said Dr. John Amory, Principal Investigator of ITHS and Associate Dean of Translational Sciences.

Dr. Murphy will be bringing this rich and deep expertise to his UW roles as BIME faculty, Neurology faculty, UW Medicine CRIO, ITHS informatics lead and Institute for Medical Data Science Director.

“I am excited to welcome Dr. Murphy to UW Medicine and confident that his decades of leadership in research informatics at Mass General Brigham will significantly advance our research, data science, and innovation efforts,” said Eric Neil, UW Medicine Chief Information Officer.

Dr. Murphy’s position begins effective February 1, 2026.

Biomedical Informatics and Medical Education Newsletter

March 9, 2026 – March 13, 2026

UPCOMING LECTURES AND SEMINARS
BIME 590 – On break until April 2^nd

ANNOUNCEMENTS
Seattle Children’s is launching a new Institute for Pediatric Innovation and hiring a Chief Innovation Officer: https://www.linkedin.com/feed/update/urn:li:activity:7427021264428625920/

To apply: https://careers.seattlechildrens.org/us/en/search-results?keywords=R261462

PAPERS, PUBLICATIONS & PRESENTATIONS

Haendel MA, Ahern R, Bailey KB, Bakas S, Barth-Jones DC, Bohl A, Bian J, Bourne PE, Boyles RR, Chute CG, Cimino JJ, Grannis S, Hartman TS, Holko M, Hotaling NA, Housman DJ, Hunter LE, Hurwitz E, Phua J, Kahn MG, Kuzmanovic D, Lemieux J, Loomba J, Madlock-Brown CR, Mandl KD, Mazumder R, McMurry JA, McMurry AJ, Modi AJ, Moffitt RA, Mosa ASM, Messina N, O’Neil ST, Peterson JF, Pfaff ER, Phuong J, Presskreischer R, Sanders L, Sarker A, Thomson A, Unertl KM, Walden A, Weinstein J Governing real-world health data as a public utility. Science. 2026 Mar 5;391(6789):993-6.DOI:1126/science.aeb1178
Bensken WP, Cottrell EK, Templeton AR, Gioia SA, Lowe S, Stowe S, Hatch BA, Adibuzzaman M, Nichol G, Phuong J, Chung-Bridges K, Sanchez M; Mayer KH, Peretti M, Heintzman JD. The ADVANCE clinical research network past, present, and future: accelerating partnerships for patient-centered research in community-based primary care settings. Medical care. 2026 Feb 1;64(2S):S205-12. DOI: 1097/MLR.0000000000002232

UPCOMING EXAMS
General Exam
Title: Exploring Trustworthy AI Collaborators to Support Patient-Clinician Collaboration Beyond the Clinic
Student: Ray Chung
Date/Time: Thursday March 19, 10-12 pm PT
In-person location: Health Sciences Building, Room 430
Zoom: https://washington.zoom.us/j/3974448205?pwd=RXdkY2dGbTBwVTNXaXlpay8rSkx1UT09

Abstract: This dissertation investigates how responsible, trust-calibrated AI collaborators can extend patient–clinician collaboration beyond the clinic to support decision-making. Grounded in stakeholder-informed design, this work identifies barriers to collaborative care, examines how AI interface and behavioral features influence trust calibration in health contexts, and translates these insights into the design of a deployable AI collaborator. Through a field study, the research evaluates how the AI collaborator can responsibly support continuous care, enhance patient experience, and strengthen patient-clinician collaboration outside in-clinic settings. Specific aims include:

Aim 1:Identify barriers to collaborative decision-making and decision-maker-informed design requirements for decision-support technologies through co-design workshops with youth with chronic kidney disease, caregivers, and clinicians.
Aim 2:Investigate how AI interface design and behavioral features influence trust calibration in health contexts and establish design requirements for responsible health-oriented AI systems.
Aim 3:Deploy an agentic AI collaborator in an outpatient dietitian–patient setting to assess its impact on patient–clinician interactions, agent autonomy preferences, and user experience.

March 2, 2026 – March 6, 2026

UPCOMING LECTURES AND SEMINARS
BIME 590
Presenter: Raina Langevin, PhD
Thursday, March 12^th – 11-11:50 am
850 Republican Street, Building C, Room 123 A/B
Zoom Information: https://washington.zoom.us/my/bime590
Speaker will present Via Zoom

Title: Fostering Patient-Centered Communication in Clinical Note Writing

Abstract:
In healthcare, effective communication between providers and patients is critical for delivering high-quality care and ensuring patient satisfaction. One aspect of this communication is the ability of providers to write clinical notes in a way that is understandable to patients. These skills are even more important due to the “open notes provision” of the 21st Century Cures Act which dictates that patients should be able to access their electronic records without delay. This talk will explore how patient-centered note writing could be supported through health informatics and AI research. The talk will focus on our ongoing research to design a rubric for patient-centered clinical note writing, and to create an annotation dataset to use in large language model (LLM) evaluation of patient-centeredness in clinical notes.

Speaker Bio:
Raina Langevin is a National Library of Medicine Postdoctoral Fellow in Biomedical Informatics and Medical Education at the University of Washington. She earned her Ph.D. and M.S. in Human Centered Design & Engineering from the University of Washington, where she designed and evaluated conversational user interfaces for healthcare applications. Prior to joining UW, she received her Bachelor’s degrees in Computer Science and Studio Arts from the University of Rochester. Her research interests include human-computer interaction (HCI), conversational user interface (CUI) design, machine learning, and health informatics. During her PhD, she contributed to the development of validated usability heuristics for conversational user interfaces, development of a chatbot for social needs screening and resource provision for emergency department patients, and design of a culturally tailored chatbot to improve breast cancer screening outreach. Her current research aims to improve the design of patient-centered technologies, by understanding the needs of patients and healthcare providers and how they could be supported by technology.

ANNOUNCEMENTS
Please join us in congratulating Dr. Ashmitha Rajendran who successfully passed her PhD Defense!

Abstract:
Background. Brain cancers are the leading cause of cancer-related death in children and adolescents, with many childhood central nervous system (CNS) tumors originating during prenatal development. Understanding the developmental origins of pediatric brain cancers requires identifying transcriptional programs that persist from normal development into disease states. Despite remarkable advances in single-cell genomics, fundamental challenges remain in comparing cellular heterogeneity across datasets, particularly the inability of conventional clustering approaches to capture transitional cell states and the difficulty of integrating data across different sequencing technologies, timepoints, and biological contexts.

Objectives. This dissertation develops and validates a transferable topic modeling framework to identify conserved transcriptional programs across developmental and disease contexts. The central hypotheses are that (1) transcriptional signatures present in neurodevelopment persist in pediatric brain cancers, and (2) these signatures can be uncovered using topic modeling while enabling systematic comparison across diverse datasets without requiring data integration or batch correction.

Methods. We use Latent Dirichlet Allocation (LDA) where each cell is represented as a probabilistic mixture of transcriptional signatures (topics), naturally capturing the continuous nature of developmental and differentiation transitions. We combined LDA with Gene Set Variation Analysis (GSVA) to enable topic transfer across datasets, allowing topics learned from one context to be scored in independent datasets. The framework was applied across three biological contexts: (1) mouse embryogenesis to establish validity against comprehensive ground truth, (2) human cerebellar development and medulloblastoma to demonstrate developmental-cancer correspondence, and (3) transcript-level analysis of TARGET pediatric cancers to reveal isoform-specific disease associations.

Results. In mouse organogenesis, topics subdivided major cell lineages (hematopoietic, neural, epithelial) and captured developmental gradients from progenitor to mature cell identities. In human cerebellar development and medulloblastoma, developmental topics transferred successfully across embryonic timepoints and sequencing technologies. Specific developmental topics showed enrichment in molecularly defined medulloblastoma subgroups, with topic enrichment correlating with clinical outcomes. This established topic transfer as a viable approach for linking normal development to cancer biology without requiring data integration. Extension to transcript-level resolution revealed that alternative isoforms organize according to developmental context and isoforms of the same gene showed distinct topic associations and disease enrichment patterns, demonstrating that isoform-level analysis reveals regulatory complexity invisible to gene-level approaches.

Conclusion. This work establishes topic modeling as a powerful analytical framework for understanding how developmental programs organize at multiple scales of resolution and how these programs contribute to disease. The transferable nature of the approach enables systematic comparison across the growing landscape of single-cell atlases without requiring computationally intensive integration methods. The framework is publicly available and broadly applicable to diverse biological contexts where identification of conserved expression programs is relevant, from comparative developmental biology to disease modeling and therapeutic target identification.

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Please join us in congratulating Feng Chen who successfully passed his General Exam!

Title: The Interplay of Text and Timing: Integrating Speech Dynamics and Semantic Content for Mental Health Assessment and Prediction

Abstract:
Speech abnormalities are hallmark features of severe mental illness and neurodegenerative disorders. While advances in NLP and automated speech recognition have enabled objective analysis, most computational approaches examine text or timing in isolation, overlooking their interaction. This is a fundamental limitation: a pause may signal lexical retrieval failure in dementia or disorganized thinking in psychosis, but its clinical significance depends on where it occurs within the linguistic structure. This thesis develops methods that integrate pause dynamics with semantic content to improve mental health assessment. Aim 1 quantifies the complementarity of pause dynamics and semantic coherence for automated thought disorder assessment across multiple clinical datasets. Aim 2 characterizes the transdiagnostic utility of integrated pause-semantic features, spanning semantic, timing, and pause-semantic interaction metrics, extracted relative to IU boundaries, for predicting cognitive and mental health outcomes across psychosis and cognitive decline. Aim 3 enhances LLM-based mental health assessment across multiple tasks (thought disorder severity prediction, cognitive status classification, EMA-based hallucination severity estimation, and short-term clinical risk forecasting) by guiding model reasoning with validated pause-semantic cues, comparing feature-guided approaches against unguided end-to-end baselines. Through these aims, this work seeks to bridge interpretable speech feature engineering with modern language models for scalable, objective clinical assessment.

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Upcoming Event sent from BIME’s Dr. Michael Leu & UW Medicine Well Being Committee:

Community building Circles for Grief & Moral Injury

The Center for Restorative Practices is partnering with the Office of Faculty Affairs’ Well-Being team to provide space for those in our community who are grieving and feeling challenged by professional, personal, national and world events.

The focus of the event will be learning how moral injury contributes and exacerbates grief and stress and proving space via community building circles to be in community for support and collectivistic grieving and coping. Please join us on Thursday, March 12th from 3:00-4:30pm at the South Campus Center Room 301. Light refreshments will be provided as well as mental health support during and after the event. Event will begin with a short presentation on moral injury and a brief meditation which will be followed by community building circles.

RSVP form can be found here: https://www.addevent.com/event/1bcmqjc5z7jr

PAPERS, PUBLICATIONS & PRESENTATIONS

Zeng, C. Wilson, G. Luo, and S. Zeliadt. Adapting the Global Burden of Disease Healthcare Access and Quality Index for the Veterans Health Administration: A Feasibility Study. AcademyHealth’s 2026 Annual Research Meeting, Seattle, WA.
Kaiyan Zhang, Kai Tian, Runze Liu, Sihang Zeng, Xuekai Zhu, Guoli Jia, Yuchen Fan, Xingtai Lv, Yuxin Zuo, Che Jiang, Yuru wang, Jianyu Wang, Ermo Hua, Xinwei Long, Junqi Gao, Youbang Sun, Zhiyuan Ma, Ganqu Cui, Ning Ding, Biqing Qi, Bowen Zhou. MARTI: A Framework for Multi-Agent LLM Systems Reinforced Training and Inference. ICLR 2026.
Sihang Zeng, Youngwon Kim, Wilson Lau, Ehsan Alipour, Ruth Etzioni, Meliha Yetisgen, Anand Oka, Jay Nanduri. Zero-Shot Lung Cancer Risk Prediction from Longitudinal Electronic Health Records with Chain-of-Agents Framework. ISPOR 2026.

UPCOMING EXAMS

General Exam
Title: Exploring Trustworthy AI Collaborators to Support Patient-Clinician Collaboration Beyond the Clinic
Student: Ray Chung
Date/Time: Thursday March 19, 10-12 pm PT
In-person location: Health Sciences Building, Room 430
Zoom: https://washington.zoom.us/j/3974448205?pwd=RXdkY2dGbTBwVTNXaXlpay8rSkx1UT09

Abstract: This dissertation investigates how responsible, trust-calibrated AI collaborators can extend patient–clinician collaboration beyond the clinic to support decision-making. Grounded in stakeholder-informed design, this work identifies barriers to collaborative care, examines how AI interface and behavioral features influence trust calibration in health contexts, and translates these insights into the design of a deployable AI collaborator. Through a field study, the research evaluates how the AI collaborator can responsibly support continuous care, enhance patient experience, and strengthen patient-clinician collaboration outside in-clinic settings. Specific aims include:

Aim 1:Identify barriers to collaborative decision-making and decision-maker-informed design requirements for decision-support technologies through co-design workshops with youth with chronic kidney disease, caregivers, and clinicians.
Aim 2:Investigate how AI interface design and behavioral features influence trust calibration in health contexts and establish design requirements for responsible health-oriented AI systems.
Aim 3:Deploy an agentic AI collaborator in an outpatient dietitian–patient setting to assess its impact on patient–clinician interactions, agent autonomy preferences, and user experience.

February 23, 2026 – February 27, 2026

UPCOMING LECTURES AND SEMINARS
BIME 590
Presenter: Trevor A. Cohen, MBChB, PhD
Thursday, March 5^th – 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: Foundations, Implications and Recent Applications of Mathematical Models of Meaning in Medicine

Abstract: This talk will discuss vector space models of meaning, from early models based on matrix decomposition through to contemporary large language models. The initial focus will be on the role of semantic vector representations as a common thread between models, how these representations facilitate generalization, and the implications of this capacity to make connections for their application (and misapplication) to problems in medicine. Building on these general observations, the remainder of the talk will discuss recent and ongoing research from our group, including adapting neural language models to detect clinically relevant changes in language as indicators of symptom severity, clinical deterioration, and suicide risk; to quantify and improve the interpretability of the scientific literature; and to improve their resilience to differences in outcome distribution in multi-institutional datasets.

Speaker Bio: Dr. Cohen trained and practiced as a physician in South Africa, before obtaining his PhD in 2007 in Medical Informatics at Columbia University. His doctoral work focused on an approach to enhancing clinical comprehension in the domain of psychiatry, leveraging distributed representations of psychiatric clinical text. Upon graduation, he joined the faculty at Arizona State University’s nascent Department of Biomedical Informatics, where he contributed to the development of curriculum for informatics students, as well as for medical students at the University of Arizona’s Phoenix campus. In 2009 he joined the faculty at the University of Texas School of Biomedical Informatics, where (amongst other things) he developed a research program concerned with leveraging knowledge extracted from the biomedical literature for information retrieval and pharmacovigilance. Since joining the University of Washington in 2018, he has developed new lines of research concerning detection of linguistic manifestations of neurocognitive status, plain-language summarization of the biomedical literature, and the development of methods to improve the robustness deep learning models for natural language processing to distribution shifts. He is also an editor of a textbook on AI in medicine, published by Springer Nature, and co-author of a recent book on large language models.

ANNOUNCEMENTS
Please join us in congratulating BIME 2022 PhD graduate Kathleen Ferar on being accepted into the Washington State University MD program class of 2030, and for welcoming her first baby boy, Maël, into the world in November 2025!

______________________________________________
Upcoming Event sent from BIME’s Dr. Michael Leu & UW Medicine Well Being Committee:

Community building Circles for Grief & Moral Injury

RSVP form can be found here: https://www.addevent.com/event/1bcmqjc5z7jr

UPCOMING EXAMS
General Exam
Title: The Interplay of Text and Timing: Integrating Speech Dynamics and Semantic Content for Mental Health Assessment and Prediction
Student: Feng Chen
Date/Time: Friday, March 6^th, 2026, 9:30-11:30 AM PT
In-person location: South Campus Center 228
Zoom: https://washington.zoom.us/my/cohenta

Abstract: Speech abnormalities are hallmark features of severe mental illness and neurodegenerative disorders. While advances in NLP and automated speech recognition have enabled objective analysis, most computational approaches examine text or timing in isolation, overlooking their interaction. This is a fundamental limitation: a pause may signal lexical retrieval failure in dementia or disorganized thinking in psychosis, but its clinical significance depends on where it occurs within the linguistic structure. This thesis develops methods that integrate pause dynamics with semantic content to improve mental health assessment. Aim 1 quantifies the complementarity of pause dynamics and semantic coherence for automated thought disorder assessment across multiple clinical datasets. Aim 2 characterizes the transdiagnostic utility of integrated pause-semantic features, spanning semantic, timing, and pause-semantic interaction metrics, extracted relative to IU boundaries, for predicting cognitive and mental health outcomes across psychosis and cognitive decline. Aim 3 enhances LLM-based mental health assessment across multiple tasks (thought disorder severity prediction, cognitive status classification, EMA-based hallucination severity estimation, and short-term clinical risk forecasting) by guiding model reasoning with validated pause-semantic cues, comparing feature-guided approaches against unguided end-to-end baselines. Through these aims, this work seeks to bridge interpretable speech feature engineering with modern language models for scalable, objective clinical assessment.

- Aim 1: Identify barriers to collaborative decision-making and decision-maker-informed design requirements for decision-support technologies through co-design workshops with youth with chronic kidney disease, caregivers, and clinicians.
- Aim 2: Investigate how AI interface design and behavioral features influence trust calibration in health contexts and establish design requirements for responsible health-oriented AI systems.
- Aim 3: Deploy an agentic AI collaborator in an outpatient dietitian–patient setting to assess its impact on patient–clinician interactions, agent autonomy preferences, and user experience.

February 16, 2026 – February 20, 2026

UPCOMING LECTURES AND SEMINARS
BIME 590
Presenter: Michael Leu, MD, MS, MHS, FAAP, FAMIA
Thursday, February 26^th – 11-11:50 am
850 Republican Street, Building C, Room 123 A/B
Zoom Information: https://washington.zoom.us/my/bime590
More Details to Come

PAPERS, PUBLICATIONS & PRESENTATIONS

Ray Chung: Our poster has been accepted to the ACM CHI 2026 conference (The premier conference in human–computer interaction). Below is the reference, with BIME-affiliated authors highlighted:
“Ray-Yuan Chung, Jaime Snyder, Zixuan Xu, Daeun Yoo, Athena C. Ortega, Wanda Pratt, Aaron Wightman, Ryan Hutson, Cozumel Pruette, and Ari Pollack. Co-designing for the Triad: Design Considerations for Collaborative Decision-Making Technologies in Pediatric Chronic Care. ACM CHI 2026.”

UPCOMING EXAMS
General Exam
Title: Exploring Trustworthy AI Collaborators to Support Patient-Clinician Collaboration Beyond the Clinic
Student: Ray Chung
Date/Time: Thursday March 19, 10-12 pm PT
In-person location: Health Sciences Building, Room TBD
Zoom: TBD

- Aim 1: Identify barriers to collaborative decision-making and decision-maker-informed design requirements for decision-support technologies through co-design workshops with youth with chronic kidney disease, caregivers, and clinicians.
- Aim 2: Investigate how AI interface design and behavioral features influence trust calibration in health contexts and establish design requirements for responsible health-oriented AI systems.
- Aim 3: Deploy an agentic AI collaborator in an outpatient dietitian–patient setting to assess its impact on patient–clinician interactions, agent autonomy preferences, and user experience.

Final Exam
Title:
Student: Ashmitha Rajendran
Date/Time: Friday February 27, 9am PT
In-person location: Seattle Children’s Research Institute, 1912 Boren Ave
Zoom: https://washington.zoom.us/j/97537558639?jst=2

Abstract:
Background. Brain cancers are the leading cause of cancer-related death in children and adolescents, with many childhood central nervous system (CNS) tumors originating during prenatal development. Understanding the developmental origins of pediatric brain cancers requires identifying transcriptional programs that persist from normal development into disease states. Despite remarkable advances in single-cell genomics, fundamental challenges remain in comparing cellular heterogeneity across datasets, particularly the inability of conventional clustering approaches to capture transitional cell states and the difficulty of integrating data across different sequencing technologies, timepoints, and biological contexts.

February 9, 2026 – February 13, 2026

UPCOMING LECTURES AND SEMINARS
BIME 590
Presenter: Ida Sim, MD, PhD
Thursday, February 19^th – 11-11:50 am
850 Republican Street, Building C, Room 123 A/B
Zoom Information: https://washington.zoom.us/my/bime590
Speaker will present Via Zoom

Title: JupyterHealth: Ecosystem-wide Infrastructure for Precision Health

Abstract: Achieving precision health will require AI advances spanning biomedical discovery to healthcare delivery. Yet the digital health ecosystem is siloed between discovery and delivery as well as between remote monitoring and in-person clinical care. This talk discusses how digital public utility — as exemplified by JupyterHealth, an open-source platform that extends Jupyter tools to healthcare – enables real-world precision health solutions.

Speaker Bio: Ida Sim, MD, PhD is Professor of Medicine (UCSF) and Computational Precision Health (UCSF and UC Berkeley), Co-Director of the UCSF UC Berkeley Joint Program in Computational Precision Health, and a primary care physician. Dr. Sim’s research focuses on cyberinfrastructures and policies for large-scale data sharing of health data. She is co-lead of JupyterHealth, an open platform for digital health and AI; co-founder of Open mHealth, a nonprofit organization defining an IEEE global open standard for patient-generated health data interoperability; and co-founder of Vivli, the world’s largest data sharing platform for participant-level clinical trial data. Dr. Sim is a member of the National Academy of Medicine and the American Society for Clinical Investigation, a Fellow of the American College of Medical Informatics, and a recipient of the United States Presidential Early Career Award for Scientists and Engineers (PECASE).

ANNOUNCEMENTS
Please join us in congratulating Tianmai Zhang who successfully passed his General Exam!

Title: Certification of Scientific Manuscripts in the Era of Peer Review Crisis and Generative AI

Abstract: The validity of scientific publications is fundamental for both the evolution and application of scientific knowledge. In the mainstream workflow, certification of scholarly manuscripts is conducted through journal- or conference-mediated peer review. However, the boundary and process of certification is becoming increasingly blurred in recent years due to factors such as the challenge from the peer review crisis, the impact of generative AI, and the rise of preprint platforms and community-driven peer review. My thesis will investigate the interactions between these factors, the scholarly certification process, and researcher trust in science through a combination of qualitative and quantitative research, focusing primarily on biomedicine and AI domains. Specific aims include:

(1) Curating emerging peer review practices and proposals through a literature review.

(2) Developing a framework of requirements, expectations, and boundaries for AI-assisted certification through qualitative interviews.

(3) Demonstrating, evaluating, and analyzing novel AI applications for manuscript assessment and certification.

PAPERS, PUBLICATIONS & PRESENTATIONS

Yein Jeon: I am pleased to share that my abstract, “Imputation Methods for Incomplete Multivariate Longitudinal Data: A Comparative Study Using Real-World CAR-T Therapy Data”, has been accepted as a poster at the Eastern North American Region Biometric Society (ENAR) 2026 Spring Meeting, which will be held in Indianapolis from March 15–18, 2026.

February 2, 2026 – February 6, 2026

UPCOMING LECTURES AND SEMINARS
No BIME 590 on Feb 12^th

UPCOMING EXAMS
General Exam
Title: Certification of Scientific Manuscripts in the Era of Peer Review Crisis and Generative AI
Student: Tianmai Michael Zhang
Date/Time: Friday February 6, 2-4 pm PT
In-person location: Health Sciences Building, E214
Zoom: https://washington.zoom.us/j/2066162813

Abstract: The validity of scientific publications is fundamental for both the evolution and application of scientific knowledge. In the mainstream workflow, certification of scholarly manuscripts is conducted through journal- or conference-mediated peer review. However, the boundary and process of certification is becoming increasingly blurred in recent years due to factors such as the challenge from the peer review crisis, the impact of generative AI, and the rise of preprint platforms and community-driven peer review. My thesis will investigate the interactions between these factors, the scholarly certification process, and researcher trust in science through a combination of qualitative and quantitative research, focusing primarily on biomedicine and AI domains. Specific aims include:

(1) Curating emerging peer review practices and proposals through a literature review.

(2) Developing a framework of requirements, expectations, and boundaries for AI-assisted certification through qualitative interviews.

(3) Demonstrating, evaluating, and analyzing novel AI applications for manuscript assessment and certification.

News and Events

Chair’s Message

The University of Washington Welcomes New Chief Research Information Officer

Biomedical Informatics and Medical Education Newsletter

March 9, 2026 – March 13, 2026

March 2, 2026 – March 6, 2026

February 23, 2026 – February 27, 2026

February 16, 2026 – February 20, 2026

February 9, 2026 – February 13, 2026

February 2, 2026 – February 6, 2026

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