Sarah Ostadabbas

About

Professor Ostadabbas is an assistant professor in the Electrical and Computer Engineering Department of Northeastern University (NEU), Boston, Massachusetts, USA. Professor Ostadabbas joined NEU in 2016 from Georgia Tech, where she was a post-doctoral researcher following completion of her PhD at the University of Texas at Dallas in 2014. At NEU, Professor Ostadabbas is the director of the Augmented Cognition Laboratory (ACLab) with the goal of enhancing human information-processing capabilities through the design of adaptive interfaces via physical, physiological, and cognitive state estimation. These interfaces are based on rigorous models adaptively parameterized using machine learning and computer vision algorithms. For many of these interfaces, Professor Ostadabbas has developed augmented reality (AR) and virtual reality (VR) tools for both the assessment and enhancement portions of the project. Professor Ostadabbas’ work also expands to the Small Data Domain (e.g. medical or military applications), where data collection and/or labeling is expensive, individualized, and protected by very strong privacy or classification laws. Her solutions include learning frameworks with deep structures that work with limited labeled training samples, integrate domain-knowledge into the model for both prior learning and synthetic data augmentation, and maximize the generalization of learning across domains by learning invariant representations. Professor Ostadabbas is the co-author of more than 70 peer-reviewed journal and conference articles and her research has been awarded by the National Science Foundation (NSF), including Pre-CAREER and CAREER awards, Department of Defense (DoD), Mathworks, Amazon AWS, Verizon, Biogen, and NVIDIA. She co-organized the Multimodal Data Fusion (MMDF2018) workshop, an NSF PI mini-workshop on Deep Learning in Small Data, the CVPR workshop on Analysis and Modeling of Faces and Gestures from 2019 and she was the program chair of the Machine Learning in Signal Processing (MLSP2019). Prof. Ostadabbas is an associate editor of the IEEE Transactions on Biomedical Circuits and Systems, on the Editorial Board of the IEEE Sensors Letters and Digital Biomarkers Journal, and has been serving in several signal processing and machine learning conferences as a technical chair or session chair. She is a member of IEEE, IEEE Computer Society, IEEE Women in Engineering, IEEE Signal Processing Society, IEEE EMBS, IEEE Young Professionals, International Society for Virtual Rehabilitation (ISVR), and ACM SIGCHI.

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.

Faculty

Mar 01, 2023

Celebrating National Engineers Week 2023

The College of Engineering celebrated National Engineers Week with events including a ‘Break the Mold’ Fireside Chat with award-winning engineer and commentator Dr. Shini Somara and Dean Gregory Abowd; Cookies with the Dean event, which included snacks, photo and video booths, COE swag and a lot of COE pride; Breakout sessions with Shini Somara with select students and faculty showing our progressive engineering program, research, and community; and an Engineering for Everyone Expo hosted by the Center for STEM Education.

Faculty

Feb 02, 2023

2023 Acorn Innovation Awards

ECE Assistant Professor Sarah Ostadabbas, Professor Deniz Erdogmus, and MIE Associate Professor Yi Zheng received MassVentures Acorn Innovation Awards to assist them in testing the viability of their technologies and potentially bringing their research to market.

Students

Oct 18, 2022

ECE Student Nominated for Schwarzman Scholarship

Electrical engineering student Alex Marley, E’22, was nominated for the Schwarzman Scholarship, which funds a year of study at China’s Tsinghua University in Beijing.

Faculty

Sep 19, 2022

Ostadabbas Paper Wins Best Scientific Paper Award at the ICPR 2022

The paper entitled “InfAnFace: Bridging the infant-adult domain gap in facial landmark estimation in the wild,” authored by ECE Assistant Professor Sarah Ostadabbas and her team at the Augmented Cognition Lab, received the Best Scientific Paper Award in the Biomedical Track at the 26th International Conference on Pattern Recognition (ICPR 2022).

Faculty

Feb 16, 2022

Ostadabbas Receives NSF CAREER Grant for Early Detection of Autism

ECE Assistant Professor Sarah Ostadabbas was awarded a $600K NSF CAREER grant for “Learning Visual Representations of Motor Function in Infants as Prodromal Signs for Autism.”

Faculty

Jan 11, 2022

Methods for Non-Contact In-Bed Pose Estimation

ECE Assistant Professor Sarah Ostadabbas was awarded a patent for “Methods and systems for in-bed pose estimation.” Abstract Source: USPTO Non-contact methods and systems are disclosed for estimating an in-bed human pose. The method includes the steps of: (a) capturing thermal imaging data of a human subject lying on a bed using a long wavelength […]

Faculty

May 11, 2021

Just What the Doctor Ordered

Human beings are some of the most complex systems in the world, and responses to illness, disease, and impairments manifest in countless different ways. When it comes to making sure that your system stays up and running, healthcare professionals typically have their own deep well of knowledge—but the addition of artificial intelligence tools offers unprecedented […]

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