RESEARCH VISION

Mert R. Sabuncu

Thanks to the remarkable progress in hardware technologies, biomedical data are multiplying rapidly, improving in quality, and offering unprecedented means to examine the underlying biology. Today, biomedical data generation has outstripped the development of sophisticated computational tools that can efficiently and effectively extract information from these data. For example, the biggest challenge we are currently facing in examining potential patterns of associations between genotype, environment, anatomy, behavior, and clinical symptoms, is methodological. We are in need of methods that can unveil true, multivariate, and dynamic relationships of modest effect size, by examining high dimensional variables (e.g., millions of image voxels) and usually with limited sample size (e.g., thousands of subjects).

Broadly, my research focuses on developing novel tools for analyzing neuroimaging data, in conjunction with clinical and other types of biomedical data. My projects are motivated by a range of problems such as mapping and detecting anatomical changes due to pathology; characterizing the temporal dynamics of these alterations; studying pre-symptomatic neuroanatomical abnormalities for early diagnosis; quantifying disease severity; functionally characterizing risk genes associated with neural disorders; and making clinically-useful individual-level predictions, e.g., based on image and genotype data. My research is also concerned with laying out the theoretical underpinnings of the analytic problems we face in these applications.

Statistical analysis, machine learning, signal/image processing, and computer vision have historically evolved separately to tackle different problems, and thus offer complementary viewpoints. Recently, however, the once-sharp boundaries between these fields have begun to blur. It is becoming increasingly clear that the methods that we desperately need to make sense of large-scale biomedical data will have to draw from multiple domains, including these fields. I view my research program as positioned at this intersection, where my central aim is to develop analytic methods that will be instrumental in biomedical research, particularly neurology and neuroscience, where we are faced with challenging clinical and basic science problems. My vision of inter-disciplinary engineering motivated by biomedical applications is reflected in the mission of the Medical Image Computing and Computer Assisted Intervention (MICCAI) society, which I am an active member of. MICCAI is an international, rapidly expanding, premier scientific community that grew out of MIT and HMS. I am committed to being one of the influential leaders in MICCAI. I have been on the program committee of the flagship conference of MICCAI for the last five years. In 2016, I am on the organizing committee, serving as the program co-chair of MICCAI 2016.

As an enthusiastic believer that software dissemination is an integral part of scholarly work, I will continue my commitment to sharing and supporting software implementations of algorithms my group develops. Furthermore, I realize the importance of building successful collaborations with students and faculty members in creating cutting-edge research projects and generating external research funds. In the next step of my career, my goal will be to build a stimulating, cross-disciplinary research group at Cornell that attracts scholars with different backgrounds and interests.