My research lies at the interface of human Systems Genetics and AI‑driven drug discovery, deploying disease‑agnostic pipelines that combine systems‑level modeling, functional genomics, and validation using in vitro and in vivo preclinical disease models. We integrate multi‑omics data and gene regulatory network analyses from deeply phenotyped cohorts to identify causal disease mechanisms, actionable therapeutic targets, and biomarkers for Precision Medicine. This work led to the discovery of WWP2, a master regulator of fibrosis, and a new class of antifibrotic target, forming the basis of an active antifibrotic drug discovery/development program. I contribute to Precision Health Research, Singapore (PRECISE) under the National Precision Medicine programme, and to the national AI‑Driven Drug Discovery (AIDD) initiative, advancing quantum‑enhanced approaches to drug  discovery in collaboration with the NUS Centre for Quantum Technologies (CQT).

Professor Cheung and his team have been working on study designs and statistical methods for controlling bias and confounding in the evaluation of treatment safety and effectiveness on time-to-event and event rate outcomes using real-world evidence / electronic health records and observational study data, with a focus on recurrent events such as emergency department visits and infectious disease episodes. The research includes novel developments in self-controlled case series method, case-control studies, and prior event rate ratio method. The methodological developments are often motivated and applied to the evaluation of palliative care intervention and childhood vaccination.

Publications:

• Ma X, Yang GM, Zhuang Q, Cheung YB. Strategy to control biases in prior event rate ratio method, with application to palliative care in patients with advanced cancer. Statistics in Medicine 2026; 45(3-5):e70441. doi: 10.1002/sim.70441.
  
  
• Zhao Z, Ma X, Milligan P, Cheung YB. Robust evaluation of vaccine effects based on estimation of vaccine efficacy curve. Vaccine 2025; 63:127673. doi: 10.1016/j.vaccine.2025.127673.
  
  
• Lee KM, Cheung YB. Estimation and reduction of bias in self-controlled case series with non-rare event dependent outcomes and heterogeneous populations. Statistics in Medicine 2024; 43(10):1955-1972. doi: 10.1002/sim.10033.
  

Dr. Chen’s laboratory specializes in AI-powered single-cell and spatial omics analysis for precision medicine, focusing on the development of innovative AI algorithms and comprehensive omics databases. She has pioneered a suite of impactful methodologies, including GraphST (Nature Communications 2023), SEDR (Genome Medicine 2024), STAMP (Nature Methods 2024), SpatialGlue (Nature Methods 2024), and SpaMosaic (Nature Genetics 2026), to address critical computational challenges in spatial omics. Furthermore, her creation of the DISCO platform (NAR 2022, 2025), the largest curated human single-cell database to date, has significantly advanced open access to high-quality data for the global research community. Dr. Chen is currently leveraging large-scale data to develop 'Virtual Immunity', an AI-driven digital twin of the immune system spanning cellular, tissue, and systemic levels. This initiative aims to transform our understanding of Asian immune aging, infectious diseases, autoimmunity, and cancer immunology.

Dr Xiaoyu Song’s research interest is on developing novel statistical and AI methods for -omics data analysis to improve diagnosis, treatment, and clinical outcome for patients with complex human diseases. She has have made significant contributions in the development of analytical tools for the association analysis, data integration, network analysis, prediction modeling, and data visualization of diverse -omics data. These tools can handle genomics, epigenomics, transcriptomics, and proteomics data, at both single-cell and multi-cell resolutions, incorporating spatial information when available. Applications of these tools have significantly improved our understanding of the molecular and cellular mechanisms for many complex human diseases including over ten cancer types. Dr. Song’s work has led to over 60 publications, with 67% appearing in Top 10% journals in the fields of statistics and biomedical sciences, including 10 articles in Cell.

Publications:

• Song, Xiaoyu, et al. "sCCIgen: a high-fidelity spatially resolved transcriptomics data simulator for cell–cell interaction studies." Genome Biology 26.1 (2025): 1-29.
  
  
• Song, Xiaoyu, et al. "MiXcan: a framework for cell-type-aware transcriptome-wide association studies with an application to breast cancer." Nature Communications 14.1 (2023): 377.
  
  
• Song, Xiaoyu, Jiayi Ji, and Pei Wang. "iProMix: A mixture model for studying the function of ACE2 based on bulk proteogenomic data." Journal of the American Statistical Association 118.541 (2023): 43-55.

Dr. Chan leads the Quantitative Science Division of the Duke Center for Human Systems Immunology and oversees a broad research program in quantitative immunology.

Mathematical immunology. We construct mathematical models, informed by experiments, to investigate and generate mechanistic hypotheses for how the immune system interreacts with and responds to microbes, vaccines, cancer, and senescent cells. We have developed deterministic and stochastic mathematical models to provide insight into diverse immune phenomena, including TCR activation, immune synapse function, light and dark zone formation in the germinal center, HIV rebound after treatment interruption, and transplacental transmission of CMV. 

Immune data science. We develop statistics and machine learning methods and software to provide insight into complex assay data from immunological experiments. We have developed these tools for flow and mass cytometry, high-throughput screening for natural product testing, shRNA and CRISPR screens, GWAS, antibody function assays, immunofluorescent imaging, single cell RNA-seq and ATAC-seq, and spatial transcriptomics. Most of these methods are implemented as open-source R or Python packages.

Collaborative projects. We are engaged in long-term collaborative interdisciplinary projects where the focus is on the analysis and interpretation of complex immune data sets. These projects span multiple medicinal domains, including vaccine development, infectious disease, solid organ transplantation, cellular senescence, cancer immunology, allergy and atopy, and autoimmunity. Recently, we are engaged in collaborations to refine the concept of immune resilience (IR) and its role in response to infection, surgical trauma, and climate-change induced immune stressors. 

My primary research focuses on methods related to the development of biostatistics, bioinformatics, and data science units within academic health centers. An essential element of my work is to improve and diversify the workforce of collaborative biostatisticians and data scientists. I develop training programs, operational processes, and organizational infrastructure that foster efficient and effective collaborations between clinical and translational scientists and quantitative scientists.

Dr Mihir Gandhi’s research spans health outcomes research and applied biostatistics, with a particular focus on patient-reported outcome and experience measures, preference-based measures, and health-state valuation. A key area of his research examines how patients value health states and how their preferences may differ from those of the general population, including the development of patient-based value sets in populations with heart disease and cancer. He also led the development of the Quality of Care for Patients with Advanced Illness (QCPAI) measure, a preference-based instrument designed to assess quality of care from the perspectives of patients with advanced illness. His research also encompasses the design and analysis of randomized clinical trials, including cluster-randomized, pragmatic, multicentre, and multi-country studies. He is a Member of the EuroQol Group, the Netherlands, and is a Chartered Statistician (Royal Statistical Society, UK) and Chartered Scientist (Science Council, UK).

Publications:

• Gandhi M, Ang FJL, Neo SHS, Gonzalez JM, Cheung YB, Finkelstein EA; QCPAI Study Group. Quality of Care for Patients with Advanced Illness Scale: Development, Preference Elicitation, and Evaluation of Measurement Properties. Value in Health. 2025;28(9):1417-1425. doi: 10.1016/j.jval.2025.05.006.

• Gandhi M, Chen CK, Zhang DZ, Lee CF, Luo N, Cheung YB. PedsQL Generic Core Scales was more discriminative while EQ-5D-Y-5L was more responsive in a longitudinal study of multiethnic Asian children and adolescents with heart disease. Journal of Clinical Epidemiology. 2025;188:111982. doi: 10.1016/j.jclinepi.2025.111982.
  
  
• Gandhi M, Kanesvaran R, Rashid MFBH, Chong DQ, Chay WY, Tan RL, Norman R, King MT, Luo N. Valuation of the EORTC Quality of Life Utility Core 10 Dimensions (QLU-C10D) in a Multi-ethnic Asian Setting: How Does Having Cancer Matter? Pharmacoeconomics. 2024;42(12):1413-1425. doi: 10.1007/s40273-024-01432-5.

Dr Saffari’s lab focuses on biostatistics, machine learning, and risk prediction modelling applied to neurology and neuroscience. The team develops and validates predictive models, biomarkers, and AI methods to improve early detection, prognosis, and clinical decision-making in neurodegenerative diseases. Research emphasises advanced machine learning approaches, including generative models such as variational autoencoders (VAEs), generative adversarial networks (GANs), and diffusion models, for integrating multimodal clinical and biomarker data, constructing interpretable risk scores, and translating data-driven insights into practical tools that support personalised care and better patient outcomes.

Publications:

• Mohammadi R, Ng SYE, Tan JY, Ng ASL, Deng X, Choi X, Heng DL, et al. Machine learning integration of serial blood biomarkers enhances cognitive decline prediction in early Parkinson’s disease. npj Parkinsons Dis. 2026;12(1):87.
  
  
• Mohammadi R, Ng SYE, Tan JY, Ng ASL, Deng X, Choi X, Heng DL, et al. Machine learning for early detection of cognitive decline in Parkinson’s disease using multimodal biomarker and clinical data. Biomedicines. 2024;12(12):2758.
  
  
• Mohammadi R, Shirazi M, Sadat-Madani SF, Yeo Cheng Long MZ, et al. Common SNCA genetic variants and Parkinson’s disease risk: a systematic review and meta-analysis. Int J Mol Sci. 2025;26(13).

My research aims to provide a holistic view of the molecular mechanisms driving tissue fibrosis and to identify potential targets for therapeutic intervention, with a particular focus on kidney and lung fibrosis. I leverage multi-omics data, including genomics, transcriptomics, and metabolomics, to conduct systems biology research. By incorporating deep learning techniques, I aim to unravel the complexities of immune responses and the progression of tissue fibrosis, with the goal of identifying novel regulatory nodes. Furthermore, I utilize molecular biology, cell biology, and preclinical animal models to elucidate the roles and mechanisms of regulatory molecules in disease progression. Specifically, I study the functional roles of WWP2, which has been implicated in regulating key signaling pathways involved in fibrosis, including TGF-β signaling and metabolism. I am elucidating the precise mechanisms by which WWP2 and other regulatory molecules contribute to disease progression. This comprehensive understanding can inform the development of targeted therapeutic interventions.