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Seong Jae Hwang, PhD

  • Assistant Professor, School of Computing and Information

Seong Jae Hwang received his B.S. in Computer Science from the University of Illinois at Urbana-Champaign in 2011, M.S.E. in Robotics from the University of Pennsylvania in 2013, and Ph.D. in Computer Sciences from the University of Wisconsin-Madison in 2019. He joined the Department of Computer Science in the School of Computing and Information at the University of Pittsburgh in 2019. He is also affiliated with MOMACS (Modeling and Managing Complicated Systems) and the Intelligent Systems Program.

His research is focused on developing statistical machine learning and deep neural network methods for analyzing imaging modalities in computer vision, machine learning, and medical imaging. On the technical side, he develops algorithms for cross-sectional and sequential data from small to large scales with statistical machine learning and deep learning models. On the application side, his interests range from neuroscientific discoveries including understanding the pathological progression of Alzheimer’s disease to machine learning/computer vision applications.

Representative Publications

  1. Seong Jae Hwang, Zirui Tao, Won Hwa Kim, Vikas Singh, "Conditional Recurrent Flow: Conditional Generation of Longitudinal Samples with Applications to Neuroimaging", International Conference on Computer Vision (ICCV), 2019.
  2. Seong Jae Hwang, Nagesh Adluru, Won Hwa Kim, Sterling C. Johnson, Barbara B. Bendlin, Vikas Singh, "Associations between PET Amyloid Pathology and DTI Brain Connectivity in Preclinical Alzheimer's Disease", Brain Connectivity, 2018.
  3. Seong Jae Hwang, Sathya N. Ravi, Zirui Tao, Hyunwoo J. Kim, Maxwell D. Collins, Vikas Singh, "Tensorize, Factorize and Regularize: Robust Visual Relationship Learning", Computer Vision and Pattern Recognition (CVPR), 2018.
  4. Seong Jae Hwang, Nagesh Adluru, Maxwell D. Collins, Sathya N. Ravi, Barbara B. Bendlin, Sterling C. Johnson, Vikas Singh, "Coupled Harmonic Bases for Longitudinal Characterization of Brain Networks", Computer Vision and Pattern Recognition (CVPR), 2016.
  5. Seong Jae Hwang, Nagesh Adluru, Maxwell D. Collins, Sathya N. Ravi, Barbara B. Bendlin, Sterling C. Johnson, Vikas Singh, "Coupled Harmonic Bases for Longitudinal Characterization of Brain Networks", Computer Vision and Pattern Recognition (CVPR), 2016.

Research Interests

  • Computer Vision / Machine Learning / Deep Learning for Neuroimaging (MRI, dMRI, PET)
  • Preclinical / longitudinal Alzheimer’s disease analysis
  • Brain connectivity network analysis
  • Brain lesion segmentation
  • Data harmonization (e.g., multi-site, multi-scanner)