Lab profile
About SAFE
SAFE focuses on harmonizing methodologies in computer science, applied mathematics, biostatistics, chemistry and pharmacology to facilitate and expedite biomedical data research and discovery. The center combines secure and privacy-preserving solutions with advanced machine learning models to explore massive and sensitive healthcare data across different modalities and sources.
Homomorphic encryptionPrivacy-preserving machine learningComputational phenotypingReinforcement learning for treatment optimizationPrivacy-protecting video and image analysisBreast cancer screening and diagnosisDeep learning for medical imagingFederated learning
01 · Research focus
Research areas and highlights
Topics and recent work as stated on the lab’s own site.
- Efficient multi-key homomorphic encryption with packed ciphertextsDeveloping practical multi-key variants of homomorphic encryption scheme with packed ciphertexts for secure computation between multiple data owners, applied to secure neural networks.
- Secure and differentially private machine learning for distributed dataDeveloping secure and privacy-preserving machine learning frameworks by harmonizing homomorphic encryption and differential privacy techniques.
- Development of secure genotype-phenotype association models with efficient correction for population stratificationProposing a novel framework to develop secure genotype-phenotype association models based on homomorphic encryption.
- Practical applications for securely outsourced genomic data analysisDeveloping secure technology for using patients' genomic data in clinical applications while ensuring patient information security and privacy during computation.
- Secure outsourced genotype imputation using homomorphic encryptionProviding a secure framework of genotype imputation in genome-wide association study based on homomorphic encryption.
- On-chip private computation of deep neural networks for face recognitionDeveloping on-chip computations with homomorphic encryption for face recognition with secure evaluation on encrypted data.
- Federated TF for computational phenotypingEnabling federated tensor factorization for computational phenotyping without sharing patient-level data across multiple hospitals.
- Combinatorial drug repositioningDeriving combinations of drugs effective to prevent Alzheimer's disease using Cerner Health Facts dataset.
- Real-time detection of end of EEG suppressionDetecting end of EEG suppression after seizure using real-time EEG signal data to automatically monitor patient status.
- Privacy-protecting video and image analysisDeveloping AI models on edge devices for live video processing to monitor senior people and cognitively impaired patients without violating privacy.
- Breast cancer screening and diagnosisDeveloping AI-driven multi-modality pipeline for breast cancer screening and diagnosis incorporating imaging and non-imaging information.
- Blueprint for tissue engineeringDeveloping AI models capable of generating blueprints for human tissue printing using multi-faceted biological data.
02 · People
Leadership and researchers
People listed on the lab’s team pages, by role.
Xiaoqian JiangDirector · Associate Vice President for Medical AI Chair, Department of Health Data Science and Artificial Intelligence, Christopher Sarofim Family Professorship in Biomedical Informatics and Bioengineering, CPRIT Scholar in Cancer Research Director, Center of Secure Artificial Intelligence for Healthcare (SAF
Miran KimAssistant Professor
Yejin KimAssistant Professor
Shayan ShamsAssistant Professor
03 · Research intelligence
Research Intelligence evidence
Publications, grants and clinical trials linked to the lab’s researchers in Health AI Central’s research data.
Publications & preprints
- Usability of a deep learning platform for detecting radiographic bone loss and furcation involvement.Journal of periodontology · 2026
- iGTP: learning interpretable cellular embedding for inferring biological mechanisms underlying single-cell transcriptomics.Briefings in bioinformatics · 2025
- A pioneering artificial intelligence tool to predict treatment outcomes in ovarian cancer via diagnostic laparoscopy.Scientific reports · 2025
04 · Updates
Updates and lab news
News and events from the lab’s own site.
- Towards Explainable AI in Precision MedicineEventFeb 27, 2020
- Augmenting Human Perceptual and Reasoning Capabilities with Intelligent Multimodal Analytics: From Healthcare to EducationEventFeb 25, 2020
- Understand Cybercrime through Automatic Analysis of Online Text TracesEventDec 27, 2019
- Algorithmic and Implicit Bias in Health Care: Exploring Challenges of Data, Ethics, and TrustEventDec 5, 2019
- Your DNA is only a Click Away: Regulating Personal GenomicsEventOct 15, 2019
05 · Official presence
Lab sources and channels
The lab’s own pages and accounts.
06 · Related
Related labs and centers
Labs at the same institution and labs working on similar topics.