Dr Raymond Louie
Lecturer
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PhD. in Electrical Engineering, University of Sydney, Australia
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B.E/B. Sc. in Electrical Engineering/Computer Science (1st Class Honours), University of New South Wales, Australia
Engineering
Computer Science and Engineering
I am a computational biologist and bioinformatician, with a current focus on applying machine learning and computational tools to analyze immune cells in patients with HCV, cealiac disease or blood malignancies treated with CAR T therapy.
I received a PhD in Electrical Engineering at the University of Sydney, after which I was an ARC Postdoctoral Fellow focusing on wireless communications. I then moved to the Hong Kong University of Science and Technology, where I focused on applying statistical and machine learning tools to viral sequence data, to aid in the design of effective vaccines. My background allows for a unique multi-disciplinary approach to solving biological problems.
I am currently recruiting PhD students. Please contact me if interested.
E-mail
- Publications
- Media
- Grants
- Awards
- Research Activities
- Engagement
- Teaching and Supervision
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NHMRC Ideas Grant CIB (2024-2027)
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"Harnessing Pathogenic Immune Cells to Develop a New Diagnostic for Non-Responders and Refractory Conditions in Celiac Disease", AUD $1,094,640.50.
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Hong Kong Research Grants Council General Research Fund, Chief (Primary) Investigator (2017-2019)
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“Statistical inference of the fitness parameters driving viral evolution using time series data”, HKD 482,605 (AUD 77,168.32)
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Hong Kong Research Grants Council General Research Fund, Chief (Primary) Investigator (2016-2018),
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“A novel statistical inference framework for big data applications: Identifying hidden multi-variate non-linear associations”, HKD 696,029 (AUD 111,294.72)
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Australian Research Council Discovery Project grant, Chief (and sole) Investigator (2011-2013).
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“Design of future cognitive radio relay networks”. AUD 255,000
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Rational vaccine design using computational models: The past few years has seen an abundance in sequence data for viruses such as SARS-CoV-2, HIV and HCV. This information can be exploited for the rational design of vaccines, by building models which capture the patterns in the viral sequences. These patterns can be exploited to find weaknesses in the virus, which can then be used to design effective vaccines.
Developing a machine learning model to predict disease outcome: Accurately predicting disease outcomes can have a significant impact on patient care, leading to early detection, personalized treatment plans, and improved clinical outcomes. Machine learning algorithms provide a powerful tool to achieve this goal by identifying novel biomarkers and drug targets for various diseases. By integrating machine learning algorithms with biological data, you will have the opportunity to push the boundaries of precision medicine and contribute to algorithms that can revolutionize the field. Such biological data includes single-cell datasets, where we have considerable experience.
My Teaching
- BINF3010 Applied Bioinformatics (UNSW)
- T2 2023 (lecturer in charge)
- BINF3020 Computational Bioinformatics (UNSW)
- T3 2023 (guest lecturer)
- ENG1811 Computing for Engineers (UNSW)
- T2 2023 (co-teaching)
- ELEC2100H Honors – Signals and Systems (HKUST)
- Fall 2015, 2016, 2017 (full course)
- ELEC5450 – Random Matrix Theory and Applications (HKUST)
- 8/3/2017, 15/3/2017, 16/3/2018 (guest lecturer)
- BIEN5040 – Introduction to Bioengineering Research (HKUST)
- 23/9/2015, 30/9/2015, 7/10/2016 (guest lecturer)
- ELEC2600 - Probability and Random Processes in Engineering (HKUST)
- Fall 2014 (full course)
- 8/11/2013 (guest lecturer)
- ELEC5507 – Error Control Coding (University of Sydney)
- 2010-2012 (full course)