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Dennis Wang helms A*STAR IHDP’s Bioinformatics platform, which integrates expertise in computer science with biology and mathematics to support analytics with large and/or multi-dimensional data. Additionally, he holds the Academy of Medical Sciences Professorship (Chair in Data Science) at Imperial College London.

Wang’s research focuses on translating patterns in the human genome into actionable information that accelerates the development of treatments for complex diseases. Specialising in big data analysis, drug development (small molecules and biologics), algorithmic development, software design, web service development, genomic profiling, and statistical inference, his experience includes applying machine learning and statistical approaches to identify patterns from large genomic data sets and providing clinically actionable biomarkers to drug development teams.

Having worked in both academia and industry, Wang enjoys mentoring junior bioinformaticians and clinicians wanting to apply genomics to patient care and marrying research and teaching to promote data driven approaches for personalising medicines. Among his most significant projects are a crowd-sourcing machine learning challenge to predict drug combinations involving partners from pharma and technology industries; improving whole genome sequencing methods for the diagnosis of complex diseases, such as dementia, motor neurone disease and COVID-19; and developing computational methods for integrating omics data from patients to advance precision medicine for treating lung cancer and pulmonary hypertension.

He has received numerous awards, including the EPSRC Healthcare Technologies grant in 2021, the Rosetrees Seedcorn Award and Academy of Medical Sciences Springboard Award in 2019, the MRC Proximity to Discovery Award in 2017, and the AstraZeneca Innovative Medicines Award in 2016.

Wang obtained his Bachelor of Science in Computer Science, Microbiology and Immunology from The University of British Columbia, and both his Master of Philosophy in Computational Biology and PhD in Biostatistics from the University of Cambridge.

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Maria De Iorio is a joint principal investigator and senior biostatistics consultant at A*STAR IHDP, a professor of science at Yale-NUS College, and a professor of biostatistics in the Department of Statistical Science at the University College London. Her previous appointments were as a postdoctoral research assistant at the University of Oxford and lecturer/senior lecturer at the Imperial College London.

De Iorio’s research interests include Bayesian statistics and how to apply Bayesian methods to analyse data, Bayesian nonparametrics, biostatistics and computational methods, medical statistics, genomics, and metabolomics. She boasts an impressive track record in modelling complex biomedical data and analysing high throughput data in genomics and metabolomics.

She obtained her undergraduate degree in economics from Bocconi University, Italy, and her MSc and PhD in statistics from Duke University in the U.S.

Additionally, De Iorio and her team at A*STAR IHDP provide customised statistical solutions for research projects, from planning and data collection to analysis and presentation. Find out more .

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Chen Li's research interests lie in genetics, DNA methylation analysis, lipidomics, metabolomics, gene expression analysis, single nucleotide polymorphism (SNP), copy number variation analysis, next-generation sequencing (NGS) data analysis and clinical data analysis. In particular, she focuses on the multi-omics studies of gestational diabetes-related research, homocysteine pathway, and telomere length.

Her scientific work includes the pre-processing of genetics, DNA methylation andlipidomics data in the GUSTO and S-PRESTO birth cohort studies – making them ready for multi-omics analysis; and genome-wide association studies of numerous plasma micronutrients such as vitamins B12 and D and homocysteine, and clinical biomarkers like insulin-like growth factors (IGF), insulin-like growth factor-binding proteins (IGFBP), adiponectin, leptin and C-reactive protein (CRP).

Chen obtained her bachelor and master's degrees in engineering from Tianjin University, and her PhD from the National University of Singapore.

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Andrea Cremaschi’s field of expertise is in statistical modelling for biomedical applications, developing methodologies in the field of Bayesian Statistics (often including nonparametric aspects), graphical models, and analysis of datasets involving variables of diverse nature.

His research interests include both the methodological and applied aspects of statistics – particularly within the Bayesian framework, Bayesian nonparametrics, the use of flexible mixture models for inference in scenarios where the data present peculiar features, concern model-based cluster analysis, and the use of graphical models for inference in dataset characterised by a sparse dependency. Besides analysing data from GUSTO in the neuroscience and metabolic fields, he also works on manuscripts with collaborators to develop novel methodologies in the field of Bayesian nonparametric statistics with applications. He received an honourable mention for the Lindley Prize by the International Society for Bayesian Analysis (ISBA) in 2019.

Cremaschi obtained his Bachelor and Master’s degrees in Mathematical Engineering from the Polytechnic University of Milan, and his PhD in Statistics from the University of Kent in the U.K. He did his postdoctoral fellowship in at the Yale-NUS College.


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Varsha Gupta's area of expertise lies in inventing and developing algorithms for longitudinal data analysis and data related to spectral and imaging studies; understanding underlying data dimensions and structures; machine learning; computer vision; pattern recognition; clustering; predictions; and risk analysis.

She plays a significant role in developing and applying mathematical models to clinical data for large-scale studies like GUSTO. Her notable projects include trajectories of maternal mood symptoms influence the course of childhood anxiety during development by gene environment interactions; identification of growth patterns in GUSTO children and associated risk factors; and study effects of gene-lipids interactions on prenatal maternal moods.

Gupta obtained her Bachelor of Science, Master of Science and PhD in Physics from the University of Delhi, and has been with A*STAR IHDP since 2014.

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Christine Maria Lim is a Scientist with the Bioinformatics platform at A*STAR IHDP.

Her research interests lie in network approaches to systems biology, target identification, and drug response and resistance,

Lim obtained her Bachelor of Engineering (Bioengineering) from the Nanyang Technological University, Singapore, and her PhD in Chemistry from the University of Cambridge.

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Pan Hong's research involves identifying molecular signatures related to health and development for women and children. In addition to studying high-throughput genome wide measures such as genetics, epigenetics, transcriptomics, lipidomics and metabolomics for Singaporean cohort studies, she also investigates the correlations and interactions among multiple omics for in-depth understanding and novel discovery through association studies and quantitative trait loci (QTL) analyses.

Possessing a background in computer science, Pan has a keen interest in cellular gene regulation from both computational methods and single cell technologies, and optimised computing and machine learning algorithms. She developed a R software package for efficiently computing the relations between gene, environment and methylation (GEM tool suites). Her other notable achievements include developing an integrative multi-omics database (iMOMdb) for Asian pregnant women and lipid quantitative trait loci (LipidQTL) of the longitudinal lipidomics for Asian mothers and children.

Prior to joining A*STAR IHDP, Pan was with A*STAR Genome Institute of òòò½Íøand A*STAR Institute for Infocomm Research where she received honours, awards, patents and publications for informatics and computational biology. She also boasts industry experience in Eli Lilly ‘s now defunct òòò½ÍøCentre for Drug Discovery where she was involved in award-winning projects for disease ontology and precision medicine.

Pan obtained her Bachelor of Engineering and Master of Signal processing from Anhui University in China, and her Master of Computer Vision and PhD in Computer Science from the Nanyang Technological University, Singapore.

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Willem van den Boom is a Scientist with the Biostatistics platform at A*STAR IHDP. He previously did postdoctoral fellowships with the NUS Yong Loo Lin School of Medicine, the Division of Science at the Yale-NUS College, and the Department of Statistics and Data Science at the National University of òòò½Íø(NUS). 

Van den Boom's research interests are in Bayesian statistics, more specifically applications, scalable computation, and Gaussian graphical models. He aims to impact health by working on data from A*STAR IHDP and medical records in close collaboration with clinicians. His most impactful work to date has been analysis of medical records – first in collaboration with clinicians from Duke University, and more recently with the National University Hospital. At Duke, he focused on blood glucose control of patients undergoing elective surgery. While at NUH, the team he worked with cautioned against the overly liberal administration of oxygen in the ICU. Both projects have been included in treatment guidelines around the world.