Siyi Tangsiyitang.ai@gmail.com I am a Senior Machine Learning Scientist at ArteraAI, where I work on (1) building multimodal deep learning models for prognosticating long-term outcomes and predicting treatment benefits for cancer patients, (2) developing and evaluating robust foundation models under real-world deployment conditions, and (3) translating research models into clinically deployed AI systems. I obtained my PhD Degree from Stanford University, where I was advised by Prof. Daniel Rubin. At Stanford, I worked on developing deep learning methods for modeling medical time series data, with a focus on graph-based modeling approaches. I co-organized the Stanford MedAI Group Exchange Sessions, a weekly seminar series where researchers around the world are invited to present the most recent advances in medical AI research. Check out the YouTube Channel! I received my Bachelor's Degree in Electrical Engineering (Highest Distinction Honors) from National University of Singapore, where I was fortunate to be advised by Prof. Nitish Thakor and Prof. Thomas Yeo. Google Scholar / LinkedIn / Twitter / GitHub |
|
Selected Publications |
Towards trustworthy seizure onset detection using workflow notesKhaled Saab, Siyi Tang, Mohamed Taha, Christopher Lee-Messer, Christopher Re, Daniel L. Rubin npj Digital Medicine, 2024 paper / |
Artificial Intelligence Predictive Model for Hormone Therapy Use in Prostate CancerDaniel E. Spratt*, Siyi Tang*, Yilun Sun*, et al. NEJM Evidence, 2023 paper / |
Modeling Multivariate Biosignals With Graph Neural Networks and Structured State Space Models (Best Paper Award)Siyi Tang, Jared Dunnmon, Liangqiong Qu, Khaled K. Saab, Tina Baykaner, Christopher Lee-Messer, Daniel L. Rubin Conference on Health, Learning, and Inference, 2023 paper / code / |
Predicting 30-day all-cause hospital readmission using multimodal spatiotemporal graph neural networksSiyi Tang*, Amara Tariq*, Jared Dunnmon, Umesh Sharma, Praneetha Elugunti, Daniel Rubin, Bhavik N. Patel, Imon Banerjee IEEE Journal of Biomedical and Health Informatics, 2023 paper / code / |
Machine Learning–Enabled Multimodal Fusion of Intra-Atrial and Body Surface Signals in Prediction of Atrial Fibrillation Ablation OutcomesSiyi Tang, Orod Razeghi, Ridhima Kapoor, Mahmood I. Alhusseini, Muhammad Fazal, Albert J. Rogers, Miguel Rodrigo Bort, Paul Clopton, Paul Wang, Daniel Rubin, Sanjiv M. Narayan and Tina Baykaner Circulation: Arrythmia and Electrophysiology, 2022 paper / |
Self-Supervised Graph Neural Networks for Improved Electroencephalographic Seizure AnalysisSiyi Tang, Jared Dunnmon, Khaled Saab, Xuan Zhang, Qianying Huang, Florian Dubost, Daniel Rubin, Christopher Lee-Messer International Conference on Learning Representations, 2022 paper / code / |
Reconciling dimensional and categorical models of autism heterogeneity: a brain connectomics and behavioral studySiyi Tang*, Nanbo Sun*, Dorothea L Floris, Xiuming Zhang, Adriana Di Martino, BT Thomas Yeo Biological psychiatry, 2020 paper / code / |
Somatosensory-motor dysconnectivity spans multiple transdiagnostic dimensions of psychopathologyValeria Kebets, Avram J Holmes, Csaba Orban, Siyi Tang, Jingwei Li, Nanbo Sun, Ru Kong, Russell A Poldrack, BT Thomas Yeo Biological psychiatry, 2019 paper / |
About MeMy Chinese name is 汤斯怡. I grew up in a beautiful city, Zhang Zhou, in Fujian Province of China. I lived in Singapore for 7+ years before coming to Stanford. Besides research, I also enjoy photography, cooking and baking, violin and traveling around the world. You can find some of my photography works here. |
|
Design and source code from Jon Barron's and Leonid Keselman's websites |