Computational BioMedicine & Intelligence System Lab
Research Field
Prof. Yang's research addresses topics on Systems and Computational Biology and Bioinformatics, in which researchers develop algorithms and tools based on electrical engineering or computer science techniques to integrate different genome-wide high-throughput data for constructing and analyzing biological systems.
This is now the most popular emerging integrated research domain in biology, electrical engineering, and computer science. Interests include systems and computational biology, bioinformatics, deep learning, deep reinforcement learning, and synthetic biology. Other interests also include computer vision, machine learning, data mining, and big data analysis.
The following categories outlined our current research focuses:
(1) Biomedical Informatics and Big Data Analysis
(2) Application of Deep Learning and Artificial Intelligence in Molecular Biology
(3) Biomedical Database and Web Application Service Construction
(4) Design of Deep Learning Pipelines in Biomedical Image Processing
Our research is focused on Computational Biology, Systems Biology, and Bioinformatics. The central theme of these cross-domain research fields is integrating different genome-wide high-throughput data using electrical engineering and computer science techniques to construct and analyze biological systems. Mastering both information engineering techniques and biological knowledge is required to tackle these types of problems. The results combine biological knowledge (molecular biology and cell biology) and information engineering techniques (network analysis, Digital Signal Processing, algorithm design, dynamic system modeling, machine learning, and big data analysis)
Research
- 2020
Top 5 Best Poster Presentations (InCoB 2020: Y.-H. Yu, J.-X. Xu, C.-F. Liao, and T.-H. Yang*, “Automatic transcriptional factor-gene interaction literature evidence extraction via temporal convolutional neural networks.”) - 2020
Top 5 Best Poster Presentations (InCoB 2020: Y.-C. Lin and T.-H. Yang*, “Novel biological metrics for evaluating the functional significance of RNA secondary structure predictions.) - 2014
Honorary Member of Phi Tau Phi Scholastic Honor Society - 2013
Best Oral Presentation Award @ International Symposium on Evolutionary Genomics and Bioinformatics, Taiwan
Teaching
- 2022
National University of Kaohsiung "Outstanding Courses": [1] Deep Neural Network II [2] Deep Neural Network Research - 2021
National University of Kaohsiung "Outstanding Courses": [1] Introduction to Deep Learning, [2] Database Management, [3] Object Oriented Programming I, [4] Deep Neural Network I, [5] Advanced Java Theory and Practice - 2020
National University of Kaohsiung "Outstanding Courses": [1] Application of Deep Learning Techniques to Real World Problems, [2] Application of algorithms and large-scale software design, [3] Introduction to Artificial Intelligence, [4] Advanced Database Management, [5] Object Oriented Programming II - 2019
National University of Kaohsiung "Outstanding Courses": [1] Introduction to Deep Learning, [2] Advanced Database Management
PhD. of Electrical Engineering, National Cheng Kung University, Taiwan
Job Description
Responsibilities:
- Assist in the design, development, and testing of AI models and algorithms.
- Analyze large datasets to derive meaningful insights.
- Implement machine learning techniques to solve complex problems.
- Optimize code for performance and scalability.
- Collaborate with cross-functional teams to integrate AI solutions into research projects.
Preferred Intern Education Level
Currently pursuing or recently completed a bachelor's/master's degree in Computer Science, Data Science, Artificial Intelligence, biomedical engineering, or a related field.
Skill sets or Qualities
Applicants should be familiar with the following:
- Python
- Keras or Pytorch
- Numpy and Pandas
- Interest in biomedical applications of deep learning
- Strong analytical and problem-solving skills.