DO | 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

Advanced Scientific English

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

Course code

DO405 (undergrad) / DO701 (postgrad), under the Department of Oceanography

Semesters taught

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

Course description

This course is designed for graduate students and students preparing to pursue graduate studies who wish to level up their scientific English skills. Students will learn to read and write with intent by adopting the perspectives of reviewers.

In the first part of the course, students will be trained to read research papers not only as readers, but also as reviewers. By learning what reviewers look for when preparing an evaluation report, students will learn to read more efficiently and critically.

In the second part of the course, students will apply this perspective to their own writing by developing a research proposal. They will revise their work through iterative, role-based feedback, alternating between the roles of author and reviewer to make their writing clear and easy to navigate for a potential reviewer. When teaching writing style, I recommend the “Context-Content-Conclusion” approach. It is one of many popular styles that make writing easy to read.

Throughout the course, students will learn to use AI tools to support the processes of reading and writing, while maintaining control over their intent, rather than accepting everything from AI tools uncritically.

Prerequisite

This course is intended for students with at least upper-intermediate English proficiency.

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

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

DO | WONG Jin Yung

Bioinformatics

Course code

Semesters taught

Course description

Prerequisite

Syllabus

Advanced Scientific English

Course code

Semesters taught

Course description

Prerequisite

Machine Learning

Course code

Semesters taught

Course description

Prerequisite

Syllabus

Programming

Course code

Semesters taught

Course description

Prerequisite

Syllabus

Web app examples

Outstanding students’ works