About Me

I am a senior year Science student, majoring in Computational Biology, at the National University of Singapore. Being a huge science nerd, I can talk with someone for hours regarding topics ranging from neuroscience to quantum mechanics. My passion for science stems from my obsession to understand and quantify everything, as well as influences from great philosophers and science popularizers such as Galileo, Sagan, and Feynman.
One of the most fascinating fields to me is biology – how billions of seemingly random biomolecules can come together to construct these intricately complicated yet elegant designs we call life. This ignited my passion for computational biology research.
Throughout my undergraduate journey, I am fortunate to have conducted computational biology and bioinformatics research in numerous laboratories. With my ardour for this field and these extensive research experiences, I am more than ready to start my graduate studies and dedicate my life to research!


National University of Singapore

Bachelor of Science (Hons) Major in Computational Biology
Interdisciplinary Special Programme in Science
August 2017 - May 2021

University of California, Santa Cruz

Exchange Semester
January 2020 - April 2020

Dry Lab Skills

Machine Learning (Tensorflow, Pytorch), Artificial Intelligence, Regression Analysis, Statistical Analysis, Image Analysis, Computer Vision, Bioinformatics, Natural Language Processing, Data Structures & Algorithms, Big Data, Database Systems, Graph Theory

Wet Lab Skills

Cell culture, Optogenetics, Western Blotting, DNA-sequencing, RNA-sequencing


Python, Java, R C++, Matlab, Hadoop, Scala, SQL, HTML, Javascript


Agency of Science, Technology and Research, Singapore

Apr 2020 - Current
Roger Foo,
Greg Tucker-Kellogg

Characterizing intra-tumor heterogeneity (ITH) of hepatocellular carcinoma (HCC) using integrated analysis of coherent multi-omics data

For my Final Year Thesis, I worked at the Genome Institute of Singapore under the supervision of Prof Roger Foo and Prof Greg Tucker Kellogg. The project focuses on using multi-omics data (genomics, epigenomics, transcriptomics, and metabolomics) data to provide a quantitative assessment of ITH within HCC tumours. The current methodology utilizes a two-front strategy; where we perform factor analysis on transcriptomics + metabolomics and adopt a GMMHMM model to identify genomic segments of heterogeneity in the genomics + epigenomics + transcriptomics. With this project, I had the freedom to explore different methodologies on my own, which includes VAE, PCA, CNN, and multiple available clustering algorithms.

Illustration of approach

Beth Israel Deaconess Medical Center, Harvard Medical School

Mar 2020 - Jul 2020
Winston Hide

Using known miRNA::mRNA interactions to derive if miRNA induces statistically differential effect on specific pathway clusters contributing to diseases

This project happened in a peculiar time of COVID-19. The initial plan to go to Boston was changed to remote work and I had to re-adjust my schedule to fit the team at Harvard. However, the experience to work with Prof Winston and his team is an enriching one. The project was to prioritize disease-related miRNAs based on gene set analysis of RNA-sequencing data with the subsequent network analysis of the gene sets. My contribution was to develop a prioritization pipeline and test the pipeline on existing datasets such as TCGA data. The work was presented at the Intelligent Systems for Molecular Biology Conference 2020 where I am the second author.

Project poster

UCSC Genome Institute

Jan 2020 - Apr 2020
Benedict Paten

Investigating deep neural network for nanopore sequencing basecalling methods

Any computational biologist will be drawn to a world-renowned institute such as UCSC Genome Institute. Having used some of their tools such as the UCSC Genome Browser, I wanted the opportunity to go and experience research. Fortunately, I managed to secure a slot at the Institute during my exchange. The project focuses on deciphering the nanopore sequencing signal features utilized by commercial deep learning models (Bonito) for the base-calling.

Sackler Faculty of Medicine, Tel Aviv University

Jun 2019 - Dec 2019
Noam Shomron

Identifying potential cancer-related genes with deep learning classification

I came across Prof Noam’s lab when I was looking for some research experience in functional genomics. After talking to Prof Noam online, I was convinced that I had to go to his lab and I flew to Israel armed only with my passion for research. I was tasked with overcoming the low interpretability in the deep learning classification of cancer genomics. The model used is a QRNN model and the input data is raw FASTA sequences. As it was my novice experience in machine learning, I spent a lot of time catching up with the project. Eventually, I adopted the gradient class activation mapping (GCAM) technique and attributed biological significance to the model. My time at this lab was one of my favourites and I learned a lot from Prof Noam, in both research and Israeli culture. The work here laid the foundation of a chapter in his book, Deep Sequencing Data Analysis, where I authored the chapter.

GCAM methodology on images

Lee Kong Chian School of Medicine, Nanyang Technological University

Dec 2018 - Dec 2019
George Augustine

Modeling the computational role of cholinergic input in a claustral recurrent neural network

I had the opportunity to work at Prof George’s lab as a UROPS student. The project focuses on the cholinergic modulation on the claustrum, a part of the brain whose function eluded neuroscientists since its discovery. As the lab does not have any computational biologists, I spent a lot of time reading up literature on my own. Another source of inspiration was my direct mentor, Aditya Nair (who is a graduate student at Caltech now), and we would explore theoretical and computational neuroscience concepts during our weekly meetings. Those meetings were always intriguing and I recalled looking forward to them every week. We settled on the idea of building a firing-rate network model using electrophysiological data collected from claustral neurons in brain slices. By approximating the cholinergic input as a gain-control function, we showed that cholinergic input toggle network encoding efficiencies of different subpopulations in the claustrum. The work here is now a manuscript in review where I am the second author.

Figures detailing the network in the manuscript

National University of Singapore

May 2018 - Dec 2018
Lim Kah Leong

Identifying molecular links between Type II Diabetes and Parkinson’s Disease using network analysis

I had my first taste of research at Prof KL’s lab and I was captivated by the process of scientific research. Although computational biology is not part of Prof KL’s expertise, he encouraged me to explore any methodology that I find interesting. He would constantly push me to read more literature and provide constructive criticism to my ideas. In his lab, I found my love for scientific research. The project I did focused on using random-walk-with-restart algorithm on a protein-protein interaction network built from the STRING database. The approach allowed us to determine molecular links between Parkinson’s disease and Type 2 diabetes, two co-related diseases that have yet to have any explicit overlapping mechanisms.

Visulization of PD and T2D influences in protein-protein interaction network



Nair, A., Teo, Y., Graf, M. & Augustine, G. J. (2020) A functional logic for neurotransmitter co-release in the cholinergic forebrain pathway. Manuscript submitted for review.

Book Chapter

Teo, Y. Danilevsky, A. & Shomron, N. (2020) Overcoming Interpretability in Deep Learning Cancer Classification. Methods Molecular Biology, Vol. 2243, Noam Shomron (Eds): Deep Sequencing Data Analysis. Publication in progress.


Yeganeh, P.N., Teo Y., Morgan, S., Vlachos, I. & Hide, W. (2020) CARAWAY: Capturing miRNA::controlled coordinated pathway activity. Intelligent Systems for Molecular Biology Conference 2020.

Nair, A., Teo, Y., Graf, M., & Augustine, G,J. (2019). Opposing cholinergic gain control of the claustrum. Society for Neuroscience 49th Annual Meeting. Society for Neuroscience.

Nair, A., Teo, Y., Graf, M., & Augustine, G,J. (2019). Opposing cholinergic gain control of the claustrum. Gordon Research Conference for Modulation of Neural Circuits and Behavior 2019.

Research Interests

Machine Learning in Biology

The era of data-driven research is now. Over the last decade, the enhanced sophistication and lower cost of technology have resulted in an explosion of bio-data. One caveat to this is that it becomes harder for us to discern noise from data and understand what the actual data is saying. To this end, I believe machine learning and deep learning is the crux. These algorithms can extract out “invisible” patterns within the data that may elude us. The results obtained can be extended to fields such as precision medicine as well. That being said, I believe we still have to address the issues that these techniques may bring such as low interpretability.

Modelling and Analyzing Systems Biology

To me, biology is one of the most fascinating fields out there. The odds of life is nearly impossible as it requires billions of biomolecules to come together and create a complex and sophisticated system. Despite these odds, life came into existence, and through the mistakes of evolution, here we are. Therefore, to me, this is a giant puzzle that is waiting to be solved and I am more than eager to unlock it. I believe that modelling and analyzing systems biology, be it in basic science or in diseases, is the best way for us to slowly untangle the mysteries of life. My vision is to build a generalizable model that can explain every biological phenomenon and disease. This can then be applied to make unprecedented progress in bioengineering and therapeutics.

Computational Neuroscience and Artificial Intelligence

Another intriguing field to me is computational neuroscience and artificial intelligence. The first biological system I started in is the brain. I strongly believe in the future where Artificial General Intelligence exists and the prospect of that future excites me. However, to achieve that, we need to first elucidate the enigmas of the human mind. Then, we can coherently construct the architecture that is needed for AGI.


  • You don’t need to be bright to be a scientist, you just need to be persistent as hell

    Dudley R. Herschbach
  • If something is important enough, even if the odds are against you, you should still do it.

    Elon Musk
  • The cosmos is within us. We are made of star-stuff. We are a way for the universe to know itself.

    Carl Sagan
  • Nothing is going to happen unless you work with your life's blood.

    Riccardo Giacconi
  • The truth isn't always beauty, but the hunger for it is.

    Nadine Gordimer
  • Nature uses only the longest threads to weave her patterns, so that each small piece of her fabric reveals the organization of the entire tapestry.

    Richard Feynman
  • I have always thought that if the man who places hope in the human condition is a fool, then he who gives up hope in the face of circumstances is a coward.

    Albert Camus
  • Common sense is the collection of prejudices acquired by age eighteen.

    Albert Einstein
  • If God didn't exist, it will be necessary to invent him.

  • You cannot teach a man anything; you can only help him find it within himself.

    Galileo Galilei
  • Mathematics is the language with which God has written the universe.

    Galileo Galilei
  • In questions of science, the authority of a thousand is not worth the humble reasoning of a single individual.

    Galileo Galilei
  • The real end of science is the honour of the human mind.

    Carl Jacobi
  • The answer isn't 42. It is science.