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BIOSTATISTICS AND BIOINFORMATICS

Our People_Chen Li

CHEN Li, PhD

Principal Scientist I

Andrea Cremaschi

Andrea CREMASCHI, PhD

Senior Scientist I


Maria De Iorio

Maria DE IORIO, PhD

Joint Principal Investigator

Varsha Gupta

Varsha GUPTA, PhD

Senior Scientist II


Huang Jian

HUANG Jian, PhD

Senior Scientist I

Evelyn Lau

Evelyn LAU, PhD

Senior Scientist I


Pan Hong

PAN Hong, PhD

Principal Scientist I

Dennis Wang

Dennis WANG, PhD

Senior Principal Scientist I



Dennis Wang, Senior Principal Scientist I

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Dennis Wang helms SICS’ 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.

Find out more about him here.


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Maria De Iorio, Joint Principal Investigator

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Maria De Iorio is a joint principal investigator and senior biostatistics consultant at SICS, 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 SICS provide provide customised statistical solutions for research projects, from planning and data collection to analysis and presentation. Find out more here.


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Chen Li, Principal Scientist I

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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.

Find out more about her here.


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Andrea Cremaschi, Senior Scientist I

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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, Senior Scientist II

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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 SICS since 2014.

Find out more about her here.


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Huang Jian, Senior Scientist I

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Besides his role at SICS, Huang Jian is also an honorary research associate at Imperial College London.

Huang’s research interests lie in molecular epidemiology focusing on cardiometabolic health, neurodevelopment, and neurodegeneration. He has a wealth of experience in multi-omic data analysis and causal analysis using causal effect decomposition (based on the counterfactual framework) and Mendelian randomisation. Specifically, Huang investigates the causal relationship of maternal and early-life risk factors with child cardiometabolic and neurodevelopmental health, and the molecular mechanisms underlying this relationship (genomics, epigenomics, metabolomics, and proteomics).

He was granted the Singapore National Medical Research Council, Open Fund - Young Individual Research Grant (NMRC OF-YIRG) in 2022 for a three-year project to study the relationship between early-life metabolic risk and suboptimal neurodevelopment.

Huang obtained a Bachelor of Science in Pharmacy from Jinan University, and his Master of Public Health and PhD in Epidemiology and Biostatistics from The University of Hong Kong. He did his postdoctoral fellowship at Imperial College London investigating the causal relationship of lifestyle factors and molecular biomarkers with risk of Alzheimer's disease.

Find out more about him here. 



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Evelyn Lau, Senior Scientist I

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Evelyn Lau’s research interests lie in exploring early signatures or events that affect the regulation of gene expression profiles and molecular networks, which contribute to the onset of diseases and their progression over time using large genomic datasets – this enables early detection and prediction of disease evolution in population. She is also an Honorary Research Fellow at University College London’s Research Department of Oncology.

A significant research study she worked on involved investigating the early and long-term impact of the expression of oncogenic PIK3CA mutation to gain insights into genetic and epigenetic events that arise at early stages leading up to tumour formation, where these signatures could potentially be exploited therapeutically. She was also involved in several other studies to understand how genetic variants affect regulation of genes involved in immune diseases, in a cell-type specific manner, using functional genomic approaches.

Lau obtained her Master of Research in Tissue Engineering for Regenerative Medicine from the University of Manchester, and PhD in Cancer Cell and Molecular Biology from University College London.

Find out more about her here.



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Pan Hong, Principal Scientist I

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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 SICS, Pan was with A*STAR’s Genome Institute of Singapore and 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 Singapore Centre 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.

Find out more about her here.


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