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), and the Director of Women in Engineering (WIE). 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 140 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), winner of Cade Prize for Inventivity in the Technology category (2024), 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 is also elected as a Faculty Fellow and the recipient of the Constantinos Mavroidis Translational Research Award from the NU’s College of Engineering in 2025. She served in the organization committees of many workshops and 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

• B. Galoaa, X. Bai, S. Moezzi, U. Nandi, D. R. Sai Siddhartha Vivek, S. Amraee, and S. Ostadabbas, “Look around and pay attention: Multi-camera point tracking reimagined with transformers,” in International Conference on 3D Vision (3DV), Mar. 2026.

• L. Jiang, S. Zhu, S. Moezzi, Y. Luo, and S. Ostadabbas, “Broadening view synthesis of dynamic scenes from constrained monocular videos,” in International Conference on 3D Vision (3DV), Mar. 2026.

• L. Song, H. Bishnoi, S. K. R. Manne, S. Ostadabbas, B. J. Taylor, and M. Wan, “Overcoming small data limitations in video-based infant respiration estimation,” in IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Mar. 2026.

• X. Bai, S. A. Sreeramagiri⋆, D. R. Sai Siddhartha Vivek, B. Galoaa, E. Mortin, and S. Ostadabbas, “Spartan: Spatiotemporal pose-aware retrieval for text-guided autonomous navigation,” in British Machine Vision Conference (BMVC), Nov. 2025.

• B. Galoaa, S. Amraee, and S. Ostadabbas, “More than meets the eye: Enhancing multi-object tracking with softmax splatting and optical flow,” in International Conference on Machine Learning (ICML), Jul. 2025.

• X. Bai, L. Jiang, Y. Luo, and S. Ostadabbas, “Dual-conditioned temporal diffusion modeling for driving scene generation,” in International Conference on Robotics and Automation (ICRA), May 2025.

• B. Galoaa, S. Amraee, and S. Ostadabbas, “Dragontrack: Transformer-enhanced graphical multi-person tracking in complex scenarios,” in IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Mar. 2025.

• X. Huang, E. Hatamimajoumerd, A. Mathew, and S. Ostadabbas, “Infant action generative modeling,” in

  IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Mar. 2025.

• Y. Luo, X. Bai, L. Jiang, A. Gupta, E. Mortin, H. Singh, and S. Ostadabbas, “Temporal-controlled frame swap for generating high-fidelity stereo driving data for autonomy analysis,” in The British Machine Vision Conference (BMVC), Nov. 2023

• 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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