Ekaterina R
- Research Program Mentor
PhD candidate at University of California Los Angeles (UCLA)
Expertise
deep learning, machine learning, linear algebra, computer vision, probability theory, medical
Bio
I have a strong academic background in the medical computer science domain, specializing in the integration and analysis of various modalities, including images, electronic health records, and genomics, to enhance early disease detection and improve prognostic outcomes. My research is focused on leveraging advanced computational techniques such as machine learning and deep learning to address complex challenges in healthcare, aiming to develop innovative solutions that can significantly impact patient care. While much of my research focuses on healthcare, my expertise extends beyond the medical domain. I am deeply involved in method development, creating advanced computational techniques that are not only applicable to biomedical AI but can also be adapted to address challenges across multiple fields. Outside of the research, I enjoy spending my free time engaging in various hobbies. I am a board games enthusiast, often diving into complex strategies and enjoying the social interaction that these games bring. I also love spending time with my dog Phoebe, who is always up for an adventure or just some relaxing downtime. Additionally, I have a passion for backpacking, which allows me to explore the great outdoors getting inspired by nature.Project ideas
Skin cancer detection from smartphone quality images
The goal of this project is to develop image-based algorithm to accurately identify skin cancer cases confirmed through histological analysis. The algorithm will be developed based on single-lesion crops extracted from 3D total body photos (TBP), providing a non-invasive and efficient method for early detection of skin cancer. Students participating in this project will gain understanding of medical imaging, will acquire skills in developing, training, and evaluating image-based algorithms using machine learning and deep learning techniques. Students will gain experience in processing and analyzing large-scale medical datasets. Students will learn to develop research skills, including hypothesis testing, data analysis, and scientific writing. The outcome can be a scientific research paper submitted to a conference, journal or a science fair.