Yixin Wang

I am a Ph.D. candidate in Bioengineering at Stanford University, with a Ph.D. minor in Computer Science. I am fortunate to be co-advised by Kilian M. Pohl and Michael Zeineh.

I hold bachelor's and master's degrees in Computer Science, and during my studies I developed a strong interest in AI for biomedicine. My Ph.D. research develops AI and computational imaging methods to identify interpretable biomarkers of brain disorders. My work spans adolescent brain development, neuropsychiatric disorders, and Alzheimer's disease, linking brain network organization to its biological substrates through multimodal neuroimaging, ultra-high-resolution MRI, and histology. I also develop LLM agents to support medical research and clinical reasoning.

I am honored to have been named a 2027 Siebel Scholar, a $35,000 award recognizing “the world's brightest graduate students and distinguished leaders.” I am also grateful to have received the Society for Neuroscience's 2026 Trainee Professional Development Award, the Postmortem Research in Dementia Workshop's Young Investigator Award, and Stanford Wu Tsai Neurosciences Institute's MBCT Fellowship. As a member of the Student Editorial Board of npj Health Systems (Nature Portfolio), I initiated and lead a collection on Digital and AI Approaches for Mental Health Systems. Beyond research, I find particular joy in teaching and helping students gain confidence and discover their own interests. I have provided hands-on mentorship to more than 10 students, served as Technical Lead for Stanford AIMI's summer programs, where I helped guide more than 100 high school students, and received the Stanford Bioengineering TA Award.

Portrait of Yixin Wang — **PhD Candidate, Stanford University**
**Siebel Scholar, Class of 2027**

yxinwang@stanford.edu

CV

News

Sep 2026Honored to be selected as a 2027 Siebel Scholar.
Sep 2026Our paper, 7TCDM, was accepted for publication in Academic Radiology.
Sep 2026Received the Trainee Professional Development Award from the Society for Neuroscience.
Aug 2026Our paper was published in BMJ Digital Health & AI.
Aug 2026Received the Young Investigator Award and Trainee Travel Award at the Postmortem Research in Dementia Workshop.
Jul 2026Co-organizing the NeurIPS 2026 Workshop on Generative AI for Health.
Jul 2026GLANCE was accepted at ACM Multimedia 2026.
Jul 2026Launched the npj Health Systems collection Digital and AI Approaches for Mental Health Systems as Lead Guest Editor.
Jun 2026Co-authored “Agents' Last Exam,” a large-scale benchmark for evaluating AI agents on long-horizon professional tasks.
May 2026Received the Audience Choice Award and was named a finalist at the Stanford Wu Tsai Neural Data Science Visualization Competition.
Apr 2026Our work on brain networks in alcohol use disorder was accepted at Translational Psychiatry.
Mar 2026Co-organizing the KDD 2026 Workshop on SciSoc Agents & LLMs.
Feb 2026Two abstracts were accepted at AAIC 2026 and the National Neurotrauma Society Symposium 2026.
Jan 2026One abstract on BBQ-SLI was accepted as an oral presentation at ISMRM 2026.
Dec 2025Joined the Student Editorial Board of npj Health Systems.
Nov 2025Our paper on multi-level patterns predicting cannabis use onset among youth was published in Developmental Cognitive Neuroscience.
Sep 2025Our paper Tabula was accepted at NeurIPS 2025.
Aug 2025Our paper Mem-GAN was selected for an oral presentation at BioKDD 2025.
Jul 2025Our BBQ paper on precise MRI-histology coregistration was published in Imaging Neuroscience.
Jun 2025Received the Stanford Bioengineering Teaching Assistant Award.
Jun 2025MMedAgent was highlighted in the AI for Healthcare Annual Progress Report at VALSE 2025.
Jun 2025Appointed Technical Lead for the Stanford AIMI Summer Health AI Programs.
May 2025Received the ISMRM Trainee Award and Magna Cum Laude Merit Award for Volumetric Histology via High-Fidelity Coregistration with MRI.
Mar 2025Presented one oral presentation and three posters at ISMRM 2025.
Feb 2025Received the Stanford Bio-X Travel Award.
Sep 2024Mentored students in the Stanford REU, SIMR, and SSRP summer programs.
Aug 2024Selected as a Mind, Brain, Computation and Technology Ph.D. Fellow at Stanford Wu Tsai Neurosciences Institute.
Jul 2024MMedAgent was accepted at EMNLP 2024.
Jul 2024Presented work on Alzheimer's disease biomarkers at AAIC 2024.
Jul 2024Brain-Cognition Fingerprinting received an oral presentation and Best Paper Award at MLCN 2024.
Jun 2024Selected as Student Lead for the Stanford AIMI Summer Health AI Bootcamp.
May 2024Passed my Ph.D. qualifying exam.
Mar 2024SpatialCTD was accepted at the Journal of Computational Biology.
Mar 2024Our work on universal multimodal cell segmentation was accepted at Nature Methods.
Mar 2024DANCE, our deep learning library and benchmark for single-cell analysis, was published in Genome Biology.
Mar 2024Our review of deep learning in single-cell analysis was published in ACM Transactions on Intelligent Systems and Technology.
Feb 2024Presented an oral pitch on correlative MRI-histology and received a Student Travel Award at ISMRM 2024.
Jan 2024Our radiology report generation paper was accepted at Neurocomputing.
Aug 2023Our ambiguity-selective consistency regularization paper was published in Medical Image Analysis.
Jul 2023Our brain measurement imputation paper was accepted at PRIME 2023.
Jul 2023Our medical knowledge graph paper was accepted at the ICML IMLH Workshop 2023.
2023Received the Stanford School of Engineering Fellowship.
Jun 2023Our expert-amateur collaboration paper was accepted at MICCAI 2023.
Jan 2023SAP-DETR was accepted at CVPR 2023.
Nov 2022Our survey on deep learning in single-cell analysis became available online.
Oct 2022Two papers were accepted at MICCAI 2022: registration dataset quality control and source-free fundus image segmentation.
Sep–Dec 2022Worked with Prof. Bo Wang on transformer language models for cross-species cell type mapping.
Sep 2022Started my Ph.D. at Stanford and moved to California.
Jun 2022Received the Presidential Scholarship of the Chinese Academy of Sciences.
Jun 2022Named an Outstanding Graduate of the Chinese Academy of Sciences.
May 2022Our cross-domain few-shot learning paper was accepted at ICASSP 2022.
Mar 2022Our cyclic prototype consistency learning paper was accepted at IEEE JBHI.
Nov 2021Received the National Scholarship.
Nov 2021Our survey on visual transformers became available online.
Oct 2021Our uncertainty-aware neurosurgical path-planning paper was accepted at IEEE TMRB.
Jun 2021Two papers were accepted at MICCAI 2021: ACN and Noisy Labels are Treasure.
Feb 2021Our transfer-learning paper was accepted at Computer Methods and Programs in Biomedicine.
Jan 2021Received the Dean's Award at the Chinese Academy of Sciences.
Dec 2020Our COVID-19 CT benchmark paper was accepted at Medical Physics.
Sep 2020Won 2nd place in the MICCAI M&Ms Challenge.
Sep 2020Won 2nd place in the MICCAI BraTS Challenge.
Apr 2020Our uncertainty-aware semi-supervised learning paper was accepted at MICCAI 2020.
Jan 2020Our organ detection paper was accepted at ISBI 2020.
Sep 2019Placed 4th in the MICCAI KiTS Challenge.

Research

My research spans six related themes. Select a theme to see the corresponding papers; some papers appear under more than one theme.

AI for Brain Health: computational methods for understanding brain disorders, cognition, and behavior.

Neurodevelopment & Neurodegeneration: biomarkers of adolescent brain development, neuropsychiatric disorders, and Alzheimer's disease.

Multimodal Learning: learning across neuroimaging, histology, cognition, language, and medical tools.

Generative & Agentic AI: generative imaging, clinical report generation, and multimodal medical agents.

AI for Biology: machine learning for single-cell data, microscopy, and spatial biology.

Data-Efficient Learning: learning with limited labels, missing modalities, incomplete data, and domain shifts.

* represents equal contribution and co-first authorship. † denotes the corresponding author(s).

31 selected papers · complete list on Google Scholar

2026

Accelerating 7T Gradient-Echo Brain MRI with Generative AI Denoising

Yixin Wang, Binxu Li, Yihao Liang, et al.

Academic Radiology · Accepted

[Paper] [Code]

2024

MMedAgent: Learning to Use Medical Tools with Multi-modal Agent

Binxu Li, Tiankai Yan, Yuanting Pan, et al., Yixin Wang†

EMNLP 2024

[Paper] [Code]

2026

Agents' Last Exam

Yiyou Sun*, Xinyang Han*, Weichen Zhang*, et al., Yixin Wang, et al.

arXiv 2026

[Paper] [Project]

2026

Using Deep Learning to Identify Brain Networks Mediating Cognitive and Motor Impairments in Alcohol Use Disorder

Yixin Wang, Eva M. Muller-Oehring, Stephanie A. Sassoon, et al.

Translational Psychiatry · Nature Portfolio

[Paper] [Code]

2025

Multi-level Patterns Predict Cannabis Use Onset among Youth

Yixin Wang, Robbie Fraser, Laika Aguinaldo, et al.

Developmental Cognitive Neuroscience

Yixin Wang

News

Research

Accelerating 7T Gradient-Echo Brain MRI with Generative AI Denoising

MMedAgent: Learning to Use Medical Tools with Multi-modal Agent

Agents' Last Exam

Using Deep Learning to Identify Brain Networks Mediating Cognitive and Motor Impairments in Alcohol Use Disorder

Multi-level Patterns Predict Cannabis Use Onset among Youth

Precise MRI-Histology Coregistration of Paraffin-Embedded Tissue with Blockface Imaging

Mem-GAN: A Pseudo Membrane Generator for Single-cell Imaging in Fluorescent Microscopy

Brain-Cognition Fingerprinting via Graph-GCCA with Contrastive Learning

Trust It or Not: Confidence-Guided Automatic Radiology Report Generation

Rethinking Medical Report Generation: Disease Revealing Enhancement with Knowledge Graph

Imputing Brain Measurements Across Data Sets via Graph Neural Networks

Cross-Domain Few-Shot Learning for Rare-Disease Skin Lesion Segmentation

ACN: Adversarial Co-training Network for Brain Tumor Segmentation with Missing Modalities

Does Non-COVID-19 Lung Lesion Help? Investigating Transferability in COVID-19 CT Image Segmentation

Double-Uncertainty Weighted Method for Semi-supervised Learning

Modality-Pairing Learning for Brain Tumor Segmentation

Ambiguity-Selective Consistency Regularization for Mean-Teacher Semi-Supervised Medical Image Segmentation

COPE: Chain-of-Thought Prediction Engine for Open-Source Large Language Model-Based Stroke Outcome Prediction from Clinical Notes

Longitudinal Cognitive Performance and Cerebral Perfusion in High- and Low-Contact Sport Athletes

Longitudinal Changes of Resting-State Functional Connectivity in High-Contact Sports

Denoising for Relaxing: Unsupervised Domain Adaptive Fundus Image Segmentation Without Source Data

Noisy Labels Are Treasure: Mean-Teacher-Assisted Confident Learning for Hepatic Vessel Segmentation

SAP-DETR: Bridging the Gap Between Salient Points and Queries-Based Transformer Detector for Fast Model Convergence

Toward Data-Efficient Learning: A Benchmark for COVID-19 CT Lung and Infection Segmentation

A Survey of Visual Transformers

Incorporating Uncertainty Into Path Planning for Minimally Invasive Robotic Neurosurgery

The State of the Art in Kidney and Kidney Tumor Segmentation in Contrast-Enhanced CT Imaging: Results of the KiTS19 Challenge

Evaluation of 3D Counterfactual Brain MRI Generation

The Multi-modality Cell Segmentation Challenge: Towards Universal Solutions

Tabula: A Tabular Self-Supervised Foundation Model for Single-Cell Transcriptomics

DANCE: A Deep Learning Library and Benchmark for Single-Cell Analysis

Selected Talks

Honors & Awards

Teaching

Mentorship

Graduate students

Medical student

Undergraduate students

Research programs

Service

Editorial

Workshop & seminar organization

Conference reviewing

Journal reviewing

Education

Personal