Sarah Ostadabbas
Associate Professor, Electrical and Computer Engineering
Office
- 520 ISEC
- 617.373.4992
Related Links
Research Focus
Computer Vision; Machine Learning; Artificial Intelligence; Augmented Cognition with Medical Applications; Augmented/Virtual Reality
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.
Education
- Postdoc (2015)—Georgia Tech
- PhD (2014) Electrical & Computer Engineering (Signal Processing)—UT Dallas
- MS (2007) Electrical Engineering (Control)—Sharif University of Tech, Tehran, Iran
- BS (2006) Electrical Engineering (Electronics)—Amirkabir University of Tech, Tehran, Iran
- BS (2005) Electrical Engineering (Biomedical)—Amirkabir University of Tech, Tehran, Iran
Honors & Awards
- SONY Faculty Innovation Award 2023
- Runners-up for the Oracle Eureka Award 2023
- 2022 NSF CAREER Award
Professional Affiliations
- Member of IEEE
- IEEE Women in Engineering
- IEEE Signal Processing Society
- IEEE EMBS
- IEEE Young Professionals
- ACM SIGCHI.
Research Overview
Computer Vision; Machine Learning; Artificial Intelligence; Augmented Cognition with Medical Applications; Augmented/Virtual Reality
Augmented Cognition Laboratory
ACLab works at the intersection of computer vision and machine learning. We are interested in representation learning algorithms for visual perception (object recognition, localization, segmentation, pose estimation, activity tracking, …) with the multidisciplinary goal of understanding, detecting, and predicting human behaviors by estimating their physical, physiological and emotional states. For a robust and efficient state estimation, we represent the state of the world in a low-dimensional embedding, called “pose”, which is a succinct interpretable representation of the important information in the state. At ACLab, we use machine intelligence (mainly Computer Vision and Machine Learning) to solve these pose estimation problems and to give human leverage, not to replace them! At ACLab, we are also working on problems in Small Data domains. To deal with data limitation, we do integrate explicit (structural or data-driven) domain knowledge into the learning process via generative models, while benefiting from the recent advancements in data efficient ML.
Selected Research Projects
- CAREER: Learning Visual Representations of Motor Function in Infants as Prodromal Signs for Autism
- – Principal Investigator, National Science Foundation
- CHS: Small: Collaborative Research: A Graph-Based Data Fusion Framework Towards Guiding A Hybrid Brain-Computer Interface
- – Principal Investigator, National Science Foundation
- CRII: SCH: Semi-Supervised Physics-Based Generative Model for Data Augmentation and Cross-Modality Data Reconstruction
- – Principal Investigator, National Science Foundation
- NCS-FO: Leveraging Deep Probabilistic Models to Understand the Neural Bases of Subjective Experience
- – Co-Principal Investigator, National Science Foundation- Neural and Cognitive Systems
- NRI: EAGER: Teaching Aerial Robots to Perch Like a Bat via AI-Guided Design and Control
- – Principal Investigator, National Science Foundation
- SCH: INT: Collaborative Research: Detection, Assessment and Rehabilitation of Stroke-Induced Visual Neglect Using Augmented Reality (AR) and Electroencephalography (EEG)
- – Principal Investigator, National Science Foundation
Department Research Areas
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.

Sep 15, 2023
Ostadabbas Selected as a Finalist of the Eureka Award of the 2023 Oracle Excellence Award
ECE Associate Professor Sarah Ostadabbas was selected as one of the finalists of the Eureka Award of the 2023 Oracle Excellence Awards. With Oracle for Research, researchers in academic, commercial, and governmental settings, across all disciplines, are exploring novel ways to achieve ground-breaking results to make the world a better place.

Sep 05, 2023
Developing New Technology for Social Anxiety Treatment
ECE Associate Professor Sarah Ostadabbas is leading a $500K NSF grant, in collaboration with the University of Pittsburgh, for the “Development of a precision closed loop BCI for socially fearful teens with depression and anxiety” to introduce a prototype for clinical application of augmented reality (AR)-guided, electroencephalogram (EEG)-based exposure technology for socially fearful teens.

Aug 07, 2023
Young Scholars Program for Local High School Students Finishes Strong
High school seniors engage in diverse engineering research during Northeastern’s Young Scholars Program, mentored by COE faculty and students.

Aug 07, 2023
Research Experience for Undergraduates (REU Pathways) completes 10 week Boston program
MIE Professor Ibrahim Zeid and STEM Executive Director Claire Duggan led a 10-week NSF-funded REU program called “REU Pathways,” which mentors community college students in engineering research.

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.

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.

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.
Oct 07, 2022
Announcing Fall 2022 PEAK Experiences Awardees
Several engineering students and science students mentored by COE faculty are recipients of Northeastern’s Fall 2022 PEAK Experiences Awards. This extraordinary group of students is taking on a range of exciting projects, from exploring axolotl limb regeneration to building a snake-inspired robot to understanding accessibility on Broadway. ASCENT AWARDS Justin Almendral COE’24, “Wearable Art” Mentor: […]

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).
Apr 28, 2022
FY23 TIER 1 Award Recipients
Congratulations to the 15 COE faculty and affiliates who were recipients of FY23 TIER 1 Interdisciplinary Research Seed Grants for 13 different projects.