I am Le Nguyen Quoc Khanh, also known as Khanh Lee. I am an associate professor with the Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University (TMU). Prior to joining TMU, I completed a postdoctoral research program at Nanyang Technological University in Singapore. I earned my master’s and Ph.D. degrees from the Department of Computer Science and Engineering, Yuan Ze University, Taiwan. As a computer scientist and data scientist specializing in medical applications, my primary goal is to contribute to the field of artificial intelligence (AI) and its applications in medicine. My research interests are focused on AI applied to radiomics, bioinformatics, and biomedical informatics, with the goal of advancing medical imaging and genomics, using AI to predict and diagnose diseases, and improve patient outcomes.
We are AIBioMed team, a diverse group of experts from various fields, including AI, medicine, and technology. Our team is passionate about using an interdisciplinary approach to tackle complex problems from different perspectives. With my leadership and the AIBioMed team’s expertise, we have already made significant contributions to the field, publishing numerous papers in top-tier journals and presenting a variety of research at international conferences. Additionally, we have been awarded several research grants from the National Science and Technology Council (NSTC) and the Ministry of Education (MOE) in Taiwan. Recently, we are at the forefront of developing innovative solutions that have the potential to revolutionize healthcare. Our commitment to advancing the use of AI in medicine underscores our dedication to improving patient outcomes and advancing medical knowledge. Further information about our work can be found at https://khanhlee.github.io/publications/.
Our research is centered around two primary applications: radiomics and genomics. Radiomics is an exciting and rapidly evolving field of medical research that involves extracting and analyzing large amounts of quantitative data from medical images, such as CT scans, MRIs, and PET scans. The goal of radiomics research is to use this data to improve the diagnosis, prognosis, and treatment of diseases, such as cancer, by identifying patterns and features that are not visible to the naked eye.
We are focused on developing new algorithms and models for image analysis that can identify relevant features in medical images that are associated with specific diseases or outcomes. Additionally, we aim to integrate radiomics data with other types of data, such as genomics, clinical, and demographic data, to improve disease progression and treatment outcomes. As a result, radiomics has the potential to revolutionize the field of medicine by improving the accuracy and effectiveness of disease diagnosis and treatment.
In genomics and bioinformatics, we use AI techniques, such as machine learning, deep learning, and natural language processing (NLP), to help researchers extract meaningful insights from vast amounts of genomic and biological data. In genomics, AI is used to analyze and interpret large datasets of genetic information, identifying patterns and correlations in genomic data that are associated with specific diseases, such as cancer. In bioinformatics, AI techniques can be used to analyze and interpret large datasets of biological information, such as protein structures, gene expression levels, and metabolic pathways. We are focused on integrating and analyzing these different types of data to provide a more comprehensive understanding of biological systems and disease mechanisms.
Moving forward, the AIBioMed team will continue to drive our research in AI-based radiomics and genomics models to high-level tasks. Radiomics and genomics will play an increasingly important role in precision medicine, where treatments are tailored to individual patients based on their unique characteristics. As more data becomes available, and AI techniques continue to evolve, radiomics and genomics will provide increasingly powerful tools to support precision medicine and improve patient outcomes. The future of AI in genomics and bioinformatics is still exciting, and there is enormous potential for AI to transform the way we understand and treat diseases. To the end, we expect to see more accurate predictions, more personalized treatments, and better outcomes for patients.
- Best Oral Presenter Award (20th Asia Pacific Bioinformatics Conference - APBC2022)
- TMU Excellent Research Paper Award 2021 (Taipei Medical University)
- Future Tech Awards (Taiwan FUTEX 2021)
- The World’s Top 2% Scientists 2020-2021-2022 (by Standford University)
- Top 1% of reviewers in Cross-Field 2019 (powered by Publons)
- Ph.D. in Computer Science & Engineering, Yuan Ze University, Taiwan, 2018
- M.S. in Computer Science & Engineering, Yuan Ze University, Taiwan, 2014
- B.Eng. in Information Technology, Da Lat University, 2010
1 Vacancy
Job Description
Key Responsibilities:
- Collaborate with a team of researchers to analyze genomic sequencing data to identify potential biomarkers related to human diseases.
- Utilize machine learning techniques to uncover relationships between genotype and phenotype.
- Process and analyze large-scale genomics datasets using state-of-the-art bioinformatics tools.
- Stay informed about the latest advancements in the fields of bioinformatics and genomics analysis.
- Implement algorithms and conduct tests using programming tools, primarily in R and Python.
- Document and share research findings with the team and contribute to collaborative discussions for further advancements.
Benefits:
- Immersive experience in the burgeoning field of bioinformatics and genomics.
- Access to groundbreaking datasets and advanced analysis tools.
- Collaborative and intellectually stimulating environment.
- Other benefits like stipends, networking opportunities, certificates, etc.
Preferred Intern Education Level
Pursuing or completed a Bachelor’s/Master’s degree in Bioinformatics, Genetics, Computational Biology, or a related field.
Skill sets or Qualities
- Solid understanding of bioinformatics and genomics concepts.
- Experience with programming languages, particularly R and Python.
- Knowledge of machine learning techniques and their application in genomics.
- Familiarity with genomics data formats, sequencing platforms, and related challenges.
- Strong analytical and data interpretation skills.
- Effective communication skills, both written and verbal.
1 Vacancy
Job Description
Key Responsibilities:
- Collaborate with a team of researchers to develop, implement, and optimize deep learning models for medical imaging applications.
- Analyze and process complex medical imaging data to improve model accuracy and reliability.
Stay updated with the latest advancements in the fields of computer vision, deep learning, and medical imaging. - Implement algorithms and conduct tests using programming tools such as Python and OpenCV.
Document and present findings to the research team, offering suggestions for improvements.
Benefits:
- Opportunity to work on cutting-edge research with real-world implications.
- Access to state-of-the-art equipment and software.
- Collaborative work environment with experts in the field.
- Other benefits like stipends, networking opportunities, certificates, etc.
Preferred Intern Education Level
Pursuing or completed a bachelor’s/master’s degree in Computer Science, Biomedical Engineering, or a related field.
Skill sets or Qualities
- Solid understanding of computer vision and deep learning concepts.
- Familiarity with deep learning frameworks such as TensorFlow or PyTorch.
- Familiarity with medical imaging data formats and challenges is a plus.
- Strong analytical and problem-solving skills.
- Effective communication skills, both written and verbal.
- Prior research or project experience in related fields.