The Signal Processing, Imaging, Reasoning, and Learning (SPIRAL) Group is federation of labs in the Electrical and Computer Engineering Department of Northeastern University. The lab comprises 6 faculty and more than 30 graduate students and postdocs, working in diverse research areas such as statistical signal processing, machine learning, distributed computing, and optimization.

Research in SPIRAL is generously supported by the National Science Foundation, the National Institutes of Health, the Office of Naval Research, the Defense Advanced Research Projects Agency, the Intelligence Advanced Research Projects Activity, the U.S. Army Research Laboratory, Google Research, MathWorks, Amazon Cloud Services, and NVIDIA

NIHNSF

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Our Research

Securing GNSS-based Infrastructures

This project develops novel anti-jamming techniques for Global Navigation Satellite Systems (GNSS) that are effective, yet computationally affordable. GNSS is ubiquitous in civilian, security and defense applications, causing a growing dependence on such technology for Read more…

Skin Cancer Diagnosis

Melanoma is diagnosed in approximately 124,000 people and is responsible for about 10,000 deaths every year, in the USA. Dermatologists rely on visual and dermatoscopic examination to discriminate benign melanocytic lesions from malignant, resulting in Read more…

Brain Computer Interfaces

People with severe speech and physical impairments can benefit from a direct brain computer interface for their communication needs. This project aims to develop an AAC interface using noninvasive EEG sensors to infer the user’s Read more…

ASSIST/iROP

Retinopathy of prematurity (ROP) is a leading cause of childhood visual loss worldwide, and the social burdens of infancy-acquired blindness are enormous. Early diagnosis is critically important for successful treatment, and can prevent most cases Read more…

Scalable Graph Distances

Representations of real-world phenomena as graphs are ubiquitous, ranging from social and information networks, to technological, biological, chemical, and brain networks. Many graph mining tasks — including clustering, anomaly detection, nearest neighbor, similarity search, pattern Read more…