CBDS Faculty's Research Interest Areas

Enrico Petretto, Director and Professor

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).

Bibhas Chakraborty, Deputy Director and Associate Professor

Thompson Sampling with Bayesian Additive Regression Trees for Mobile Health

Mobile health (mHealth) interventions (e.g., nudges to promote healthy behaviors) are becoming increasingly common in tandem with advances in mobile and wearable sensor technologies. In this project, our team is developing novel Reinforcement Learning algorithms to adaptively personalize mHealth interventions based on complex user data. Specifically, we are building on a basic algorithm called Thompson sampling (TS), existing versions of which often lack flexibility for non-linear outcomes. We are developing a novel version of TS by integrating Bayesian Additive Regression Trees (BART) – a probabilistic tree ensemble approach – with TS. Theoretical as well as experimental results are being generated to validate the proposed approach. This work is motivated by a real micro-randomized trial (MRT) for behavior change via mHealth interventions.

Cheung Yin Bun, Professor

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.

Jacques Behmoaras, Associate Professor

The Behmoaras group revealed the primary role of macrophage gene and metabolic networks during inflammatory and fibrotic disease. These findings showed therapeutic impact as they highlighted the importance of targeting macrophage leucine and iron metabolism during renal inflammation and fibrosis. Using spatial and single cell omics, the group is currently aiming to target specific pro-fibrotic macrophage polarization states. Recently, Prof Jacques’ group at Duke-NUS has initiated a research program aiming to target immune ageing with natural compounds. The group combines multi-omics approaches and AI-based methodology for a mechanistic understanding of immune ageing through macrophage-nutraceutical interactions.

Chen Jinmiao, Associate Professor

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.

Liu Nan, Associate Professor

My research focuses on the ethical and globally equitable implementation of cuttingedge AI in healthcare. I develop and evaluate clinical AI systems that use advanced methods such as generative AI and large language models, with a strong emphasis on interpretability, fairness, robustness, and privacy-preserving technologies. My team leads international efforts to create frameworks and guidelines for safe, trustworthy AI deployment and regulation, and studies real-world implementation in hospitals, including health-economic and ethical impacts. Across these efforts, I aim to ensure that AI improves patient outcomes while protecting privacy and narrowing, rather than widening, global health inequities.

Song Xiaoyu, Associate Professor

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.

Regina Hoo, Assistant Professor

The laboratory of cancer ecosystems studies how diverse cellular lineages shape tissue function in health and disease. At the intersection of cancer biology, computational data science, and artificial intelligence, we develop innovative experimental and analytical platforms to uncover mechanisms of tumour initiation, progression, and therapeutic response or resistance. A major focus is Asian genotype cancer, including tumour-immune interactions, cancer plasticity, and microenvironment-driven mechanisms of differential therapeutic response. By integrating genomics and other multi-omics approaches, we aim to identify actionable biomarkers and therapeutic targets for functional validation and clinical translation.

Cliburn Chan, Professor

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.

Li Yi-Ju, Professor

• Development of association tests for quantitative, non-zero inflated, and survival phenotypes for related and unrelated data

• Genetics of Alzheimer’s disease (AD), Fuchs endothelial corneal dystrophy, and drug-induced liver injury

• Biomarker research for osteoarthritis (OA) and its progression

• Clinical and genetic factors for postoperative cognitive dysfunction

• Applications of Machine Learning methods to develop prediction models for postoperative outcomes

Gina-Maria Pomann, Associate Professor

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.

Fan Qiao, Associate Professor

Dr. Fan’s research primarily focuses on high-dimensional genomic data analysis and predictive modeling, gene and environment interactions, and genetic pleiotropy of multiple human traits.

Mihir Gandhi, Assistant Professor

- Development, validation, and application of patient-reported outcome measures

- Health state valuation and preference-based utility measures for health economic evaluation

- Clinical trial design and analysis, including pragmatic and multi-country trials

Lee Chun Fan, Assistant Professor

Health-related quality-of-life studies, Clinical trials, Modelling transmission of infectious disease

Seyed Ehsan Saffari, Assistant Professor

Electronic health record (EHR) data, neurology/neuroscience, machine learning and data science, risk prediction models

Ouyang Fengcong John, Principal Research Scientist

• Development of new computational tools applied to sRNA-seq data

• Prioritisation of drugs to reverse dysregulated gene regulatory programs

• Application to stem cell biology, neural models, and hematological malignancies

Chen Huimei, Principal Research Scientist

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.

Books Authored or Edited by CBDS Faculty

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