Analysis of Multimodal Brain Images

Advances in imaging technologies have enabled superbly fine-resolution in-vivo examination of the human brain. We are developing deep learning, statistical, and optimization frameworks to aid in the interpretability of such images by discovering regions that are preserved across a population or group of interest, and in understanding how the functional and structural networks of the brain interrelate.

Affiliated People

Research interests: 

Data science, network science, machine learning, brain networks

Haraldur recieved a B.Sc in Electrical Engineering and a B.Sc in Computer Science at the University of Iceland in 2013 and 2014, respectively. His Ph.D. research is focused on statistical and machine learning methods to model and understand the human brain from MRI data. Prior to joining Dynamo in 2014 he developed methods to diagnose sleep disorders, and since joining Haraldur has studied deep learning architectures at Google Research and the effect social networks have on physical behavior at Evidation Health, among other projects.

Sikun Lin
Research interests: 

graph mining, brain networks, computer vision


Sikun received her B.Sc degree in Physics and Math from Hong Kong University of Science and Technology in 2015 and her MPhil degree in Computer Science from the same university in 2017.

Research interests: 

Computational Vision, Image Analysis, NLP

Shuyun Tang received his B.S degree in Statistics and Data Science from UCSB in 2021 and he is an avid researcher in machine learning.

Research interests: 

data mining

Hongyuan became a Ph.D. candidate in June 2016. Before coming to UCSB, he received his bachelor of science in EECS from Peking University in 2013.