Biomedical Data Science Immersion Scheme (BMDSIS)

What is BMDSIS?

The biomedical data science immersion scheme (BMDSIS) is a 6 to 8-month research programme where you deepen your domain knowledge in the biosciences via internship with a professor. You also gain the opportunity to apply your skills and develop your maturity as a biomedical data scientist. Depending on the project, this may also lead towards achievements such as deployed software or publications. Such achievements are useful “stamps” that signify ability.
Currently, you need to be enrolled in the biomedical data science masters programme to be eligible for BMDSIS. However, we will also consider masters students from relevant competitive programmes in NTU on a case-by-case basis (for example, Master of Science in Analytics or Master of Science in Artificial Intelligence).

Can part-time students take part?
BMDSIS students are required to spend their day times working in the labs. If you have an existing day job, this may be problematic.

Can BMDSIS be used for graduation?
BMDSIS is non-credit bearing. And so, it does not count towards your graduation requirements for the masters programme.

How am I recognized for BMDSIS participation?
You will receive a certification at the end of BMDSIS. Depending on performance, the certificate is tiered at High Distinction, Distinction or Merit. There is also an award for the best intern, with a prize and social media coverage on our NTU platforms.
The final decision for performance tier and awards will be made jointly by the Biomedical Data Science Programme Director, the Associate Chair for Graduate Studies, and a panel comprising two other senior faculty members.

What do I need to submit to complete my BMDSIS internship?
Nearing the end of your internship, you need to submit a report highlighting your key achievements and findings of your internship to the graduate office ( There is no specified page limit, but length should be commensurate with your achievement. Do not waste words!
To improve your competitiveness, we also ask you to submit a video recording with slides. This is optional.
The final report submission is required to be eligible for certification.

When does BMDSIS take place?
Typically, trimester 1 and 2. Except for 2020 cohort, which is trimester 2 and 3.

Can I be paid for BMDSIS?
Usually no. However, discretionary payment can be made depending on the supervisor and the project requirements.

Where do I sign up for BMDSIS?
Sign-up form link:

Name of PI Project Title
Assoc Prof Miao Yansong Computational biology-enabled biotechnology development
Assoc Prof Miao Yansong Data Science in Disease Prediction via Establishment of Molecular Grammar
Dr Wilson Goh Resolving batch effects using data smart strategies
Dr Wilson Goh Missing protein prediction and functional validation using PROTEC
Assoc Prof Mu Yuguang Design cyclic peptides for inhibiting SARS2 virus invasion
Asst Prof Melissa Fullwood Development of an improved bioinformatics pipeline for Circular Chromosome Conformation Capture analysis
Assoc Prof Lu Lei Developing analytic tools for Golgi imaging
Assoc Prof Francesc Xavier Roca Castella In silico analysis of alternative splicing in human myeloid cells (Computational project with option for wet lab experiments (not compulsory))
Assoc Prof Francesc Xavier Roca Castella Prediction of genomic variations in Asians that change splicing and potentially cause disease (Computational project with option for wet lab experiments (not compulsory)
Asst Prof Li Yinghui Integration and optimisation of expression Quantitative Trait Loci (eQTLs) predictions with machine learning to target novel regulatory elements in cancer
Asst Prof Li Yinghui Epigenome-aware identification and prediction of enhancer characteristics influencing gene expression in cancer with Deep Learning
Asst Prof Jarkko Tapani Salojarvi Evolution of fatty acid desaturase 2 (FAD2) family in plants
Assoc Prof Tan Nguan Soon Understanding the progression of NAFLD using a genomic-transcriptomic integrative analysis
Assoc Prof Tan Nguan Soon Develop a transcriptome-based pipeline to deconvolute the kinomic landscape for precision oncology
Adjunct Assoc Prof Chiam Keng Hwee Detecting and predicting behaviors of Alzheimer's disease mouse models
Adjunct Assoc Prof Chiam Keng Hwee Detecting tumorigenesis and morphogenesis genes in tumor spheroid formation
Assoc Prof Lee Guat Lay Caroline Machine Learning to predict disease severity/drug response based on potentially functional SNPs (pfSNPs) / Haplotypes
Prof Wang Yulan Metabolic heterogeneity of liver cancer assessed by mass spectrometry imaging
Prof Wang Yulan Deep learning our gut microbiomes
Adjunct Prof Frank Eisenhaber Biological function derivation from gene and protein sequences
Asst Prof Ayumu Tashiro Investigation of neural mechanism underlying spatial information processing in the brain
Adjunct Assoc Prof Lee Hwee Kuan Using AI to distinguish lung infection from lung inflammation
Assoc Prof Melvin L.K. Chua Using AI to distinguish lung infection from lung inflammation
Assoc Prof Melvin L.K. Chua Conventional and machine-learning approaches to predict cancer survivial from tumour gene expression profiles
However, you should speak with the professors first to find out more about the project and make an informed choice.
You also need to negotiate properly with the supervisor regarding work arrangements and expectations.
Some professors are more selective. And would like more formal interview to ensure you are a good fit with their labs. They will indicate their preferences to the masters programme office when we confirm the allocations.
The masters programme office will inform you of the allocation.

Other questions What is expected of me?
You need to negotiate and manage expectations between yourself and the supervisor.
In general, you should spend at minimum 10-15 hours a week working on the project and/or interfacing with the supervisor.
You are not expected to know all the relevant domain knowledge. The purpose of this immersion is to improve your understanding and demands of the bioscience sector, while also providing opportunity for you to develop your skills as a biomedical data scientist.

Can I self-source BMDSIS projects externally?
No. For purposes of accountability, calls for projects to approved professors are managed by SBS Graduate Office directly.
What do I do if I am treated unfairly?
You may lodge a complaint with SBS Graduate Office at

What if I want to change project?
Strong justification (e.g. evidence of misalignment of expectations/capabilities or insufficient training/support) is required. In general, SBS Graduate Office does not entertain vexatious complaints or act as arbiters.
In the event there is misalignment, we recommend first discussing with the supervisor to modify existing project, to provide relevant training, and/or adjust expectation. Inform SBS Graduate Office at that we are aware. Keep us closely informed on developments.

Can I take multiple BMDSIS projects?

Can my BMDSIS project eventually become my practicum project?
Yes. However, to ensure fairness, only the component done during the practicum phase will be evaluated.