Center for Signal Processing, Imaging, Reasoning, and Learning (SPIRAL)

SPIRAL conducts fundamental and interdisciplinary applied research in the areas of computational modeling, computer vision, distributed computing, image analysis, machine learning, optimization, and signal processing, with application areas spanning affective computing, biomedical imaging, modeling, and simulation, cyber-human systems including brain interfaces and human robot collaboration, geolocalization and positioning systems, neuroscience, wireless communications and networks. SPIRAL is a federation of collaborating research laboratories that jointly strive to conduct rigorous application-driven computational modeling and inference research in diverse contexts, and are committed to providing an inclusive research, academic, and personal development environment for a diverse group of talented individuals at undergraduate, graduate, and postdoctoral training stages.

The following laboratories currently participate in SPIRAL:

Augmented Cognition LaboratorySarah Ostadabbas
Biomedical Imaging and Signal Processing LaboratoryDana Brooks
Brain Stimulation & Simulation LaboratorySumientra Rampersad
Cognitive Systems LaboratoryDeniz Erdogmus
Data, Networks, and Algorithms LaboratoryStratis Ioannidis
Efficient and robust distributed machine learning labLili Su
Information Processing LaboratoryPau Closas
Machine Learning LaboratoryJennifer Dy

SPIRAL has been generously funded by the following government agencies, industry, and foundations:

Advanced Robotics for Manufacturing Institute, ARL, DARPA, DHHS, DHS, IARPA, NIH, NSF, ONR, Amazon Cloud Services, Analog Devices, Biogen, Google Research, Honeywell Labs, Intel Labs, Mathworks, NVIDIA, Unilever Corporation, Brigham & Womens’ Hospital, Boston Children’s Hospital, Massachusetts General Hospital, Nancy Lurie Marks Family Foundation, Simons Foundation, and Northeastern University.

For more information please contact the center co-directors:


Pau Closas
Assistant Professor,  Electrical and Computer Engineering

Statistical and array signal processing; estimation and detection theory; Bayesian inference; stochastic filtering; robust statistics; and game theory, with applications to positioning systems (GNSS and indoor technologies); wireless communications, and mathematical biology


Sarah Ostadabbas
Assistant Professor,  Electrical and Computer Engineering

Machine learning/pattern recognition; computer vision, affective computing, human-machine interaction

Recent SPIRAL News

SPIRAL Receives 2 Best Paper Awards at PETRA

Two papers from SPIRAL researchers received best paper awards at PErvasive Technologies Related to Assistive Environments (PETRA) conference.