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 Laboratory Sarah Ostadabbas
Biomedical Imaging and Signal Processing Laboratory Dana Brooks
Brain Stimulation & Simulation Laboratory Sumientra Rampersad
Cognitive Systems Laboratory Deniz Erdogmus
Data, Networks, and Algorithms Laboratory Stratis Ioannidis
Efficient and robust distributed machine learning lab Lili Su
Information Processing Laboratory Pau Closas
Machine Learning Laboratory Jennifer 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
Associate 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

Computer Vision; Machine Learning; Artificial Intelligence; Augmented Cognition with Medical Applications; Augmented/Virtual Reality

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.