Student Profile in Cancer, Imaging, Deep Learning
Aleksandra Nielsen
Biomedical Engineering Graduate Program
Mentor: Satwik Rajaram, Ph.D.
Undergraduate Degree: Biotechnology
Undergraduate Institution: Wroclaw University of Science and Technology
Country You Grew Up In: Poland
Awards/Fellowships: Undergraduate/Graduate: Rector's Scholarship for the Best Students, BioLAB Fellowship
How did you become interested in science and/or research?
I first became interested in computational biology during an undergraduate research work. During that time, I moved my experimental design to in silico setup, which greatly changed the course of my thesis. Since then, the goal to bridge experimental observations and theoretical solutions became the main drive of my research.
Please describe your research.
Intrigued by the possibility to study cancer and the fundamental nature of evolution in an unconventional manner (by using both histopathological images and sequencing data), I have decided to join the Rajaram lab. Our lab focuses on morphology and spatial organization of tissues and their relationship to underlying mechanisms of disease progression and response. More specifically, my graduate project involves development of novel deep learning algorithms to study morphology and genetics guided tumor evolution in kidney cancer.
Why did you choose UT Southwestern?
I chose UTSW for graduate school after my positive experience here as a Visiting Junior Researcher. During my fellowship, I realized that the diverse faculty, program curriculum, and research done at our university all greatly help with development towards our individual future goals, be it in academia or industry.
What do you think makes your program one of the best? What do you love about your Program?
The Computation Biology track at UTSW gives us a unique opportunity to tackle relevant biomedical problems. As a medical center, we have access to clinical data and diverse research collaborations. We are also provided with high-performance cluster computing infrastructure that allows for efficient computational work. All of that allows us to be innovative in asking and answering questions that would be difficult to address in other settings.
– Aleksandra Nielsen
