Sarah Ostadabbas

Assistant Professor,  Electrical and Computer Engineering

Contact

Social Media

Office

  • 520 ISEC
  • 617.373.4992

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

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.

Augmented Cognition Laboratory

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

Selected Publications

Faculty

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.

Sarah Ostadabbas

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.”

Sarah Ostadabbas

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 […]

abstract photo of women doctor pointing to tablet with medical imagery around it

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 […]

Faculty

Oct 16, 2020

COE Faculty Awarded Seed Funding in Collaboration with University of Maine

BioE Assistant Professors Jiahe Li and Mingyang Lu, ECE Professor & Chair Srinivas Tadigadapa, ECE Assistant Professors Sarah Ostadabbas and Xue “Shelley” Lin, and ECE Associate Research Scientist Ataur Katebi were among the faculty chosen for five competitive collaborative research projects with the University of Maine in the areas of artificial intelligence, earth and climate sciences, health and life sciences, manufacturing, and marine sciences.

steering wheel and dashboard of self-driving car

Faculty

Oct 01, 2020

Assuring that Self-Driving Cars are Accessible to Those with Disabilities

ECE Assistant Professor Xue “Shelley” Lin is working with the algorithms in self-driving vehicles to ensure that they will be accessible to those with disabilities.

Sarah Ostadabbas

Faculty

Sep 04, 2020

Innovative Approaches to a Hybrid Non-Invasive BCI System

ECE Assistant Professor Sarah Ostadabbas, in collaboration with the University of Rhode Island, was awarded a $500K NSF grant for “A Graph-Based Data Fusion Framework Towards Guiding A Hybrid Brain-Computer Interface.”

Faculty

Apr 11, 2020

FY21 TIER 1 Award Recipients

Congratulations to the 19 COE faculty and affiliates who were recipients of FY21 TIER 1 Interdisciplinary Research Seed Grants for 13 different projects.

In the Media

Mar 18, 2020

Invasion of the Bias Snatchers

ECE Assistant Professor Sarah Ostadabbas was featured in the latest issue of Northeastern’s Litmus podcast “Invasion of the Bias Snatchers,” about how her research is using computers to simulate how people sleep.

gapfund logo

Faculty

Jan 30, 2020

Eight COE Projects Selected for GapFund360

Northeastern’s GapFund360 program helps Northeastern’s researchers bridge the gap between promising lab results and demonstrating a commercially viable prototype. Awards range from $50K -$100K. Nine projects were selected from a pool of 39 applications from across the university; COE contributed 25 of the applications and seven projects were selected for funding. Congratulations to the following COE researchers whose projects were selected for Phase I or Phase II GapFund360 funding: ChE Assistant Professor Sidi Bencherif, MIE Assistant Professor Safa Jamali, ECE Assistant Professor Sarah Ostadabbas, ChE/COS Associate Professor Carolyn Lee-Parsons, ECE Professor Tommaso Melodia, ECE Associate Research Scientist Salvatore D’Oro, ECE Associate Professor Kaushik Chowdhury, ECE Principal Research Scientist Yousof Naderi, ECE Postdoc Ufuk Muncuk, ECE Professor Vincent Harris, ECE Associate Research Scientist Parisa Andalib, ECE Associate Professor Matteo Rinaldi, and ECE Research Assistant Professor Zhenyun Qian.

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