Scalable Modeling · Imaging · Learning
SMILE develops efficient models, algorithms, and systems that turn large-scale scientific data into insight—from whole-slide images to molecular graphs.
Led by Prof. Junzhou Huang · The University of Texas at Arlington
Research
Our work connects rigorous learning methods with problems that matter in medicine, science, and high-performance computing.
Efficient learning and optimization methods designed for data-intensive scientific workloads, with theoretical guarantees and practical performance.
02Multimodal models that connect whole-slide pathology, medical images, molecular profiles, and clinical outcomes.
03Deep graph models that learn from relationships in molecular, biomedical, social, and image-omics data.
04Machine learning for molecular representation, protein interactions, therapeutic discovery, and biological foundation models.
05Robust visual learning for image and video understanding, multimodal alignment, human behavior, and biomedical applications.
Selected work
A selection from the lab’s work across multimodal learning, pathology, graph learning, and AI for biology.
Jitendra Jonnagaddala, Miljana Shulajkovska, Anton Gradisek, Toni Jue, Qifeng Zhou, Yuzhi Guo, Jamil Chayeb, Rujiang Li, Jana Lipkvoa, Jakob Kather and Junzhou Huang
NPJ Digital Medicine , To Appear. · journal
Yi Han, Yuqiu Yang, James Zhu, Farjana J. Fattah, Mitchell S. von Itzstein, Minying Zhang, Casey Bermack, Peixin Jiang, Shailbala Singh, Yanhua Tian, Yifei Hu, Yafang Deng, Xiongbin Kang, Donghan M. Yang, Jialiang Liu, Yaming Xue, Chaoying Liang, Indu Raman, Chengsong Zhu, Olivia Xiao, Jonathan E. Dowell, Jade Homsi, Sawsan Rashdan, Ke Pan, Shengjie Yang, Mary E. Gwin, David Hsiehchen, Yvonne Gloria-McCutchen, Fangjiang Wu, John V. Heymach, Don Gibbons, Junzhou Huang , Chao Cheng, Jianjun Zhang, Cassian Yee, Alexandre Reuben, David E. Gerber and Tao Wang
Nature Communications , June 2026. · journal
Qifeng Zhou, Wenliang Zhong, Thao M. Dang, Hehuan Ma, Saiyang Na, Yuzhi Guo and Junzhou Huang
In Proc. of the 19th European Conference on Computer Vision , ECCV'26, Malmö, Sweden, September 2026. · conference
Saiyang Na, Feng Jiang, Qifeng Zhou, Wenliang Zhong, Thao M. Dang, Yuzhi Guo, Hehuan Ma, Chunyuan Li, Weizhi An and Junzhou Huang
In Proc. of IEEE Conference on Computer Vision and Pattern Recognition , CVPR'26, Denver, Colorado, USA, June 2026. · conference
People
Latest
New papers, collaborations, awards, and milestones from the lab.
Build what comes next
We welcome motivated researchers interested in machine learning, data mining, computer vision, biomedical imaging, and informatics.
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