Sarah Ostadabbas

About

Professor Ostadabbas is an associate professor in the Electrical and Computer Engineering Department at Northeastern University (NU) in Boston, Massachusetts, USA. She joined NU in 2016 after completing her post-doctoral research at Georgia Tech, following the achievement of her PhD at the University of Texas at Dallas in 2014. At NU, Professor Ostadabbas holds the roles of Director at the Augmented Cognition Laboratory (ACLab), Director of Women in Engineering (WIE), and Co-Director at The Center for Signal Processing, Imaging, Reasoning, and Learning (SPIRAL). Her research focuses on the convergence of computer vision and machine learning, particularly emphasizing representation learning in visual perception problems. In her applied research, she has significantly contributed to the understanding, detection, and prediction of human and animal behaviors through the modeling of visual motion, considering various biomechanical factors. Professor Ostadabbas also extends her work to the Small Data Domain, including applications in medical and military fields, where data collection and labeling are costly and protected by strict privacy laws. Her solutions involve deep learning frameworks that operate effectively with limited labeled training data, incorporate domain knowledge for prior learning and synthetic data augmentation, and enhance the generalization of learning across domains by acquiring invariant representations. Professor Ostadabbas has co-authored over 130 peer-reviewed journal and conference articles and received research awards from prestigious institutions such as the National Science Foundation (NSF), Department of Defense (DoD), Sony, Mathworks, Amazon AWS, Verizon, Oracle, Biogen, and NVIDIA. She has been honored with the NSF CAREER Award (2022), Sony Faculty Innovation Award (2023), was the runner-up for the Oracle Excellence Award (2023), and One of the 120+ Women Spearheading Advances in Visual Tech and AI Recognized by LDV Capital (2024). She served in the organization committees of many workshops in renowned conferences (such as CVPR, ECCV, ICCV, ICIP, ICCASP, BioCAS, CHASE, ICHI) in various roles including Lead/Co-Lead Organizer, Program Chair, Board Member, Publicity Co-Chair, Session Chair, Technical Committee, and Mentor.

Selected Publications

• D. Teotia, A. Lapedriza, and S. Ostadabbas, “Interpreting face inference models using hierarchical network dis-section,” International Journal of Computer Vision (IJCV), 2022.
• S. Liu, X. Huang⋆, L. Marcenaro, and S. Ostadabbas, “Privacy-preserving in-bed human pose estimation: High- lights from the ieee video and image processing cup 2021 student competition,” IEEE Signal Processing Magazine, 2022.
• A. Farnoosh and S. Ostadabbas, “Deep Markov Factor Analysis: Towards concurrent temporal and spatial analysis of fMRI data,” in Thirty-fifth Annual Conference on Neural Information Processing Systems (NeurIPS), 2021.
• A. Farnoosh, B. Azari, S. Ostadabbas, “Deep Switching Auto-Regressive Factorization: Application to Time Series Forecasting,” The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI’21). February 2-9, 2021.
• B. Rezaei, A. Farnoosh, and S. Ostadabbas, “G-LBM: Generative Low-dimensional Background Model Estimation from Video Sequences,” 16th European Conference on Computer Vision (ECCV’20), August 23-28,  2020.
• S. Liu, S. Ostadabbas, Seeing Under the Cover: A Physics Guided Learning Approach for In-Bed Pose Estimation, 22nd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI’19), October 13-17, 2019, Shenzhen, China
• S. Liu, S. Ostadabbas, “Inner Space Preserving Generative Pose Machine,” 15th European Conference on Computer Vision (ECCV’18), September 8-14, 2018, Munich, Germany.

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