"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 […]
"Softwarized Approaches for the Open RAN of NextG Cellular Networks" Abstract: The 5th and 6th generations of cellular networks (5G and 6G), also known as NextG, will bring unprecedented flexibility […]
Location: 532 ISEC "Wake-up Radio-enabled Wireless Networking: Measurements and Evaluation of Data Collection Techniques in Static and Mobile Scenarios" Abstract: Multi-hop wireless networks such as Wireless Sensor Networks and in general, […]
"Towards Robust and Secure Deep Learning Models and Beyond" Abstract: Modern science and technology witness the breakthroughs of deep learning during the past decades. Fueled by the rapid improvements of […]
"Permutation Invariant Graph Learning" Abstract: Graphs are widely used in many areas such as biology, engineering, and social sciences to model sets of objects and their interactions and relationships. Tasks […]