Join MathWorks engineers and Northeastern faculty for these insightful seminars in Climate Change, Quantum Computing, and AI. AI for Humans Friday, March 4, 11 am - 12 pm Register: MathWorksNUSymposiumAI.eventbrite.com […]
Honoring Diversity in ECE - a celebration of Electrical and Computer Engineering students, faculty, and staff with marginalized identities! Register as an honoree to be awarded a certificate, or as […]
Professor Edmund Yeh will give an invited seminar titled "Data-Centric Networking: Theory, Algorithms and Applications" in the Electrical Engineering Seminar Series at Harvard University John A. Paulson School of Engineering […]
"High Efficiency RF Energy Harvesting and Power Management Circuits Techniques for IoT Application" Abstract: As the number of Internet of Things (IoT) devices is continuing to grow, there is a […]
"Parametric Circuits for Enhanced Sensing and RF Signal Processing" Abstract: Massive deployments of wireless sensor nodes (WSNs) that continuously detect physical, biological or chemical parameters are needed to truly benefit […]
"Towards Efficient Deep Neural Network Execution with Model Compression and Platform-specific Optimization" Abstract: Deep learning or deep neural network (DNN), as one of the most powerful machine learning techniques, has […]
"Learning and Shaping the Wireless Environment: An Integrated View of Sensing, Computing and Communication" Abstract: The explosive growth in Internet of Things (IoT) deployments and anticipated data volumes that will […]
"Morphology, Magnetism, and Transport in Nanomaterials and Nanocomposites" Abstract: Magnetic thin film materials and bilayer composites enable unprecedented new applications, ranging from magnetic-based microelectromechanical systems (magnetoelectric sensors, ultracompact magnetoelectric antennas, […]
"Fast Identification via Subspace Clustering and Applications to Dynamic and Geometric Scene Understanding" Abstract: More and more data is needed in order to build new machine learning and computer vision […]
"Interpretable Machine Learning for Affective Psychophysiology and Neuroscience" Abstract: In this thesis, we leverage existing Machine Learning (ML) models where appropriate and develop novel models to advance the understanding of […]