Advanced Data Science Techniques for Enhancing Biological Dataset Usability
Modern biomedical science has been revolutionized by the advancement of experimental techniques that can collect data at an unprecedented scale and resolution. As a result, biomedical research is increasingly relying on computational methods to extract and analyze these data to reveal the hidden meaning and help make critical healthcare decisions. My lab is dedicated to developing data-analytical methods and tools to make complex biological data more understandable and useful. In this SDS Undergraduate Research Fellowship Program, you will have the opportunity to learn some advanced data science techniques developed in the lab, make improvements, and apply them to some interesting biological applications provided by the instructor (such as cancer outcome prediction and drug resistance prediction), or applying to some applications of your interest.
Example research projects include but not limited to:
Research project 1: Improve the accuracy of cancer outcome prediction via data augmentation and mixture of experts
Research project 2: Improve the knowledge discovery process from single-cell RNAseq data via advanced clustering and visualization techniques
Research project 3: Simultaneously improve the outcome prediction accuracy and systems-level knowledge discovery in cancer via knowledge representation learning
- Faculty: Jianhua Ruan
- Department: Computer Science
- Open Positions: 3
- Mode: Hybrid
- Hours per week: 5-10hrs
Requirements and Responsibilities:
Needed Skills: Ideally the student should have taken or be taking Course-based Undergraduate Research Experience (CURE) course.
Student Responsibilities: Students can work in groups or individually, but each student will be involved in at least two of following four roles.
1. Data integration, cleaning, and summarization
2. Research of existing methods
3. Pipeline implementation, evaluation
4. Written reports
