main | WONG Jin Yung

Bioinformatics

Fri, 08 May 2026 00:00:00 +0000

Course code

MBR357 (undergrad), under the Department of Marine Biotechnology and Resources.
DO451(undergrad)/ DO727(grad), under the Department of Oceanography.

Semesters taught

115-1 (offered every year in the first semester)

Course description

Bioinformatics integrates biology, statistics and computer science. This course is suitable for students who want to learn how to use computers to run analysis by building pipelines from existing software, or by designing a new algorithm. I will use some classical problems to demonstrate: sequence alignment, phylogeny reconstruction, motif finding, etc. The focus is not on the topics per se, but rather on how we formulate them as computationally solvable problems. The course is therefore designed to be general, with the hopes that the skills and concepts learned will be transferable across the vast number of topics in bioinformatics, and even outside of biology. Therefore, this course is also suitable for students with little biology background.

The first part of the course focuses on the practical data analysis. Students will learn how to work in a Linux virtual machine (using Github Codespaces):

How to identify suitable software for a question
How to install software for reproducible analysis
How to format inputs and outputs so that different software tools can work together
How to combine multiple software tools into a pipeline
How to work with large files

These basics are best taught in a command line interface, inside a Linux environment so that students can better understand how a computer works.

The second part of the course focuses on learning the algorithms underlying these software. Students will learn:

How many possible solutions does this problem have?
How can we score a solution?
How can we find the solution with the best score using the least computation?
When the number of possibilities is too large, how can we find an approximate best solution?

Specifically, I will focus on the concepts (or their simplifications) of Dynamic Programming (DP), Maximum Likelihood Estimation (MLE), and Expectation Maximization (EM), etc. I will spend a significant amount of time on DP, building from the basic concepts of recursion and graph representation.

In the last part of the course, I will briefly introduce students to the concept of deep learning (DL), as it has become increasingly important in bioinformatics. The goal here is to connect the optimization methods students learned in the previous part to how DL models works with gradient descent.

Prerequisite

Biological knowledge: Students only need to know that DNA is the genetic material of living organisms and that it consists of four letters: A, G, T, and C. That is all.
Programming experience: Prior programming experience is NOT required, but it will be helpful. In the first part of the course, I will teach the necessary computational skills from scratch. In the second part, the focus will shift toward problem-solving by hand. Coding is less central in the age of AI; problem formulation and understanding algorithms are more important.

Syllabus

to be updated

Life sciences

Sat, 10 Jan 2026 00:00:00 +0000

Course code

GEAE2514A/GEAE2514B (undergrad), under general education courses

Semesters taught

113-1, 113-2, 114-1, 114-2, 115-1 (offered regularly every semester)

Course description

I cover half of this general education course, focusing on the evolution of organisms and their interactions with each other and with the environments they inhabit.

The class is designed for non-biology majors and introduces students to the fascinating world of living organisms. I aim to show that life sciences are highly relatable and touch on fundamental questions related to our own existence and daily life. I also connect life sciences to other disciplines, so students from different majors can see how these ideas connect with their fields.

Here are some of the topics I will cover:

Evolution by natural selection is a big idea. Where did it come from? Influences on Charles Darwin, including economic theories of resource competition.
Why Charles Darwin’s theory of evolution is not just about “survival of the fittest”.
Mutation can cause cancer, yet without it long term evolution is impossible.
Survival of the luckiest: not all evolutionary changes are adaptations; some are simply the outcome of random sampling.
Nature vs. Nurture: If evolution acts only on genes, does choice still matter?
To fight or not to fight? Peace benefits everyone, but why it is so hard to maintain and why doesn’t aggression take over entirely in nature?
If evolution acts on individual rather than group survival, how can cooperation and even self-sacrificial behavior evolve?
Why have peacocks evolved a large, colorful tail that handicaps them? How sexual selection explains “illogical” mate choice in animals.
Will the human population continue to grow? What limits it, and how does it compare to other organisms?
Why do animal populations cycle or fluctuate? The link to the “the butterfly effect”
If extinction is natural, why should we save species? If survival of the fittest rules nature, why save the losers?
The race to resource exploitation: are we doomed, or can we escape “the tragedy of the commons”? Is reducing consumption the answer?
How close are we to climate tipping points? Why didn’t we notice them coming earlier?

Prerequisite

None. My classes are quite different from high school biology. It doesn’t matter much whether you have studied biology before.

Syllabus

The origin of species and the tree of life
Principles of evolution
Animal behavior, conflicts and cooperation
Sexual selection and evolution summary
Population and community ecology
The Anthropocene and conservation biology
Exam

Machine Learning

Sat, 10 Jan 2026 00:00:00 +0000

Course code

DO354 (undergrad)/ DO720 (postgrad), under the Department of Oceanography

Semesters taught

113-1, 114-1, 115-1 (offered every year in the first semester)

Course description

This course introduces students to machine learning and its applications. Upon completion, students will be able to implement various ML models, from simple linear models to deep learning models, and apply them to problems in marine sciences.

The first part of the course focuses on the basic machine learning concepts and implementation with scikit-learn. More time is spent on coding than on theory in this part to help students become confident with coding and familiar with the general workflow of model training.

The second part focuses on the basics of deep learning and implementation using Keras/TensorFlow. More time is spent on conceptual understanding in this part, i.e., heavy use of blackboard explanations, to help students become confident in explaining how each type of neural network model works.

We will use examples in marine sciences throughout the course.

Prerequisite

We will use Python for implementation (using Google Colab), so students are required to have some programming experience in Python.

Syllabus

Introduction to machine learning
Python crash course and data preprocessing
Regression
Classification
Dimension reduction
Clustering
Part 1 summary
Mid-term exam / Final project proposal
Introduction to deep learning
Multilayer perceptron (Part 1: forward propogation and loss function)
Multilayer perceptron (Part 2: back propagation and gradient descent)
Autoencoder
Recurrent neural network
Convolutional neural network
Transfer learning
Part 2 summary
Final project presentation

Marine life in moving fluids

Sat, 10 Jan 2026 00:00:00 +0000

Course code

IGPM709 (postgrad), under the International graduate program of marine sciences and technology

Semesters taught

113-2, 114-1 (offered non-regularly)

Course description

This is an interdisciplinary graduate course based heavily on the book Life in moving fluids: the physical biology of flow, and supplemented with a list of research papers for recent progress of the field.

I aim to show that many aspects of the evolution and plasticity of marine organisms are shaped by interactions with their surrounding fluid environment, demonstrated by surprising adaptations and counterintuitive insights into how animals swim and feed.

Students will learn key concepts in biological flows through discussion of the book chapters, as well as advanced methods and experimental designs used in biological flow research through discussion of the selected papers.

By the end of the course, students are expected to be able to formulate biological questions from the perspective of flow interactions and to critically evaluate research papers in the field.

Prerequisite

Students with backgrounds in either biology or physics/ engineering are welcome.

Syllabus

Introduction: what is fluid?
Drag, scale, and Reynolds number
Flow visualization methods
Flow field analysis
Motion analysis
Sessile systems
Moving animals
Midterm exam
Life in velocity gradients and boundary layers
Making and using vortices
Lift and hydrofoils
Thrust of swimming
Life at low Reynolds number
Advanced topics
Final presentation

Programming

Sat, 10 Jan 2026 00:00:00 +0000

Course code

DO243A (undergrad), under the Department of Oceanography

Semesters taught

113-2, 114-2 (offered every year in the second semester)

Course description

This course introduces students to basic programming concepts using Python. Upon completion, students will be able to write their own programs in Python.

The first part of the course focuses on the basics of programming and the Python language. Students will learn built-in data types and use conditionals and iterations to write their first programs. We will use mostly “vanilla” Python in this part within the command line interface to build a strong foundation.

The second part focuses on using popular external libraries. This part is useful for students learning programming for data analysis. We will use Google Colab to demonstrate how data can be conveniently processed, analyzed and visualized in a notebook format.

Finally, students will be introduced to web app development to present their ideas and let users to interact with their programs easily online. We will use Streamlit to demonstrate. For students planning to also take Machine learning later, this will equip them with the skills to build an interactive web app that allows users to use their model online.

Prerequisite

None.

Syllabus

Introduction to programming and Python basics
Data types
Logical operators and conditionals (if-else)
Iterations (for loop, while loop, comprehensions, recursion)
Function (arguments, variable scopes, testing)
Randomization (sampling, simulation)
Optional topics depending on progress: parallel processing, intro to programming paradigms (procedural, functional, object-oriented), regular expression
Midterm exam and final project proposal
Introduction to data analysis libraries and web app development
Data frames processing with Pandas
Data visualization
Numerical analysis with NumPy
Building interactive web app I
Building interactive web app II
Final project presentation
Final project presentation

Web app examples

Hand written digit predictor (embedded below)
Typhoon track viewer
K-means clustering visualization
Ocean current viewer

main | WONG Jin Yung

Bioinformatics

Course code

Semesters taught

Course description

Prerequisite

Syllabus

Life sciences

Course code

Semesters taught

Course description

Prerequisite

Syllabus

Machine Learning

Course code

Semesters taught

Course description

Prerequisite

Syllabus

Marine life in moving fluids

Course code

Semesters taught

Course description

Prerequisite

Syllabus

Programming

Course code

Semesters taught

Course description

Prerequisite

Syllabus

Web app examples

Outstanding students’ works