"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 become the fundamental element and core enabler of the artificial intelligence. Many incredible, bleeding-edge applications, such as community/shared virtual reality experiences and self-driving cars, will […]
Come learn about Nanosurf's DriveAFM, a tip-scanning atomic force microscope used for all areas of applications from materials to life science. An instrument demonstration will follow in the CILS Core […]
A conversational regarding the Master's in ECE and Bioengineering program at the Roux Institute in Portland, Maine.
Interested to learn more about the Graduate School of Engineering on the Northeastern campus? Then we welcome you to sign up for a Graduate School of Engineering campus tour! Led […]
The Roux Institute presents: The Future of Manufacturing Featuring Jack Lesko, Director of Engineering Research
"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, […]
Join the CommLab for a virtual interactive workshop, where we will be discussing best practices for presenting your posters. Bring your poster-even if it is just a draft- and we […]
"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 […]