Sarah Ostadabbas
Assistant Professor,
Electrical and Computer Engineering
Office
- 520 ISEC
- 617.373.4992
Research Focus
Machine learning/pattern recognition; computer vision, affective computing, human-machine interaction
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
Professional Affiliations
Member of IEEE, IEEE Women in Engineering, IEEE Signal Processing Society, IEEE EMBS, IEEE Young Professionals, and ACM SIGCHI.
Research Overview
Machine learning/pattern recognition; computer vision, affective computing, human-machine interaction
Department Research Areas
Selected Publications
- B. Rezaei, S. Ostadabbas, Moving Object Detection through Robust Matrix Completion Augmented with Objectness, IEEE Journal of Selected Topics in Signal Processing (J-STSP), 2018
- S. Liu, S. Ostadabbas, Inner Space Preserving Generative Pose Machine, European Conference on Computer Vision (ECCV’18), 2018, Munich, Germany
- Y. Yin, M. Nabian, M. Fan, C. Chou, M. Gendron, S. Ostadabbas, Facial Expression and Peripheral Physiology Fusion to Decode Individualized Affective Experience, Affective Computing Workshop of the 27th International Joint Conference on Artificial Intelligence (IJCAI-2018)
- A. Farnoosh, M. Nabian, P. Closas, S. Ostadabbas, First-Person Indoor Navigation via Vision-Inertial Data Fusion, IEEE/ION PLANS Conference, 2018, 1213-1222
- S. Liu, S. Ostadabbas, A Vision-Based System for In-Bed Posture Tracking, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (ICCV/ACVR’17) 1373-1382
- B. Rezaei, S. Ostadabbas, Background Subtraction via Fast Robust Matrix Completion, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (ICCV/RSL-CV’17), 2017, 871-1879
- A. Farnoosh, M. Nourani, S. Ostadabbas, Spatially-Continuous Plantar Pressure Reconstruction Using Compressive Sensing, Proceedings of the 2nd Machine Learning for Healthcare Conference (MLHC’17), in Journal of Machine Learning Research (JMLR) Track, 68, 2017
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.
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.
Jan 30, 2020
Translating the ‘Mastermind’ Concept from Business to Academia
Assistant Professors Danielle Levac (Bouvé), Emily Zimmerman (Bouvé), Kristen Allison (Bouvé), Abigail Koppes (ChE), Sarah Ostadabbas (ECE), Jessica Oakes (BioE), and Associate Professor Eno Ebong (ChE) were awarded a 2020 ADVANCE Mutual Mentoring Advancement Program (M2AP) Grant for “Translating the ‘Mastermind’ Concept from Business to Academia: Facilitating Peer mentorship among female PIs leading active research labs”.
Sep 09, 2019
Ostadabbas and Ramezani awarded an NSF NRI EAGER
ECE Assistant Professors Sarah Ostadabbas (PI) and Alireza Ramezani (co-PI) received a $100K EAGER grant from NSF National Robotics Initiative (NRI) on teaching aerial robots to perch like a bat via AI-guided design and control.
Aug 20, 2019
Studying Sleep Positions
Assistant Professor Sarah Ostadabbas is creating a database of sleeping positions to learn how it affects a person’s health.
Jul 31, 2019
Ostadabbas Awarded NSF Grant to combined Augmented Reality (AR) and EEG for Stroke-Induced Visual Neglect Rehabilitation
ECE Assistant Professor Sarah Ostadabbas is the Northeastern PI of a collaborative $1.2M NSF grant with the University of Pittsburgh on “SCH: INT: Collaborative Research: Detection, Assessment and Rehabilitation of Stroke-Induced Visual Neglect Using Augmented Reality (AR) and Electroencephalography (EEG).”
Aug 21, 2018
$1M NSF Grant to Understand Subjective Experiences
CCIS Assistant Professor Jan-Willem van de Meent (PI) and co-PIs ECE Professor Jennifer Dy, ECE Assistant Professor Sarah Ostadabbas, and COS Assistant Professor Ajay Satpute were awarded a $1M NSF grant “Leveraging Deep Probabilistic Models to Understand the Neural Bases of Subjective Experience”.
Aug 07, 2018
2018 MMDF Workshop Report is Out
The very productive 2018 Multimodal Data Fusion (MMDF) workshop hosted in March 2018 at Northeastern resulted in insightful discussion and productive suggestions for moving the field of multimodal data fusion forward in the coming years.
Jul 18, 2018
Shuangjun Liu from ACLab Awarded $30K AWS Amazon Cloud Credits
Shuangjun Liu, ECE PhD student working at the Augmented Cognition Lab (ACLab) directed by Assistant Professor Sarah Ostadabbas, received $30K Amazon Web Service (AWS) credit for the implementation of his PhD research on “A Semi-Supervised Data Augmentation Approach using 3D Graphical Engines”.
Jun 07, 2018
Ostadabbas Awarded NSF CRII Grant
ECE Assistant Professor Sarah Ostadabbas has been awarded a $170K NSF grant to develop a “Semi-Supervised Physics-Based Generative Model for Data Augmentation and Cross-Modality Data Reconstruction” to bridge the gap between state-of-the-art deep learning techniques and the small data problem common in personalized healthcare and other data-limited domains.