"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 […]
"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 be generated within future autonomous devices require collecting and processing large amounts of data, generally transmitted over the wireless channel. Rigid infrastructure deployment that does […]
"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, etc.), terahertz emitters, to spin-orbit-torque driven magnetic memories. Here we focus on two subdisciplines within magnetics – magnetoelectrics and spintronics heterostructures. The first aspect of […]
"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 techniques. Using human operators to identify these vast datasets would be too expensive, hence the use of unsupervised learning has grown more common. Piecewise linear […]
"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 affective psychophysiology and neuroscience. Additionally, considering the increased use of ML as a toolbox, we highlight underlying assumptions and limitations of basic ML methods to […]
"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 to the wireless cellular ecosystem. Because of a typically closed and rigid market, the telco industry has incurred high costs and non-trivial obstacles for delivering […]
"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 computational resources, learning algorithms, and massive amounts of data, deep neural networks (DNNs) have played a dominant role in many real world applications. Nonetheless, there […]
"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 addressed by applying machine learning to graphs, known as graph learning, include node and graph classification, edge prediction, transfer learning, and generative modeling/distribution sampling, to […]
"Robust Sparsified Deep Learning" Abstract: This dissertation studies robustness issues around DNN deployments on resource constrained systems, under both environmental and adversarial input adaptation. We propose a means of compressing a Radio Frequency deep neural network architecture through weight pruning, and provide a systems-level analysis of the implementation of such a pruned architecture at resource-constrained […]
"Model-based decision making in life sciences" Abstract: Mathematical models are key tools in rational decision-making processes. A ``good" model is expected to reproduce experimental observations, which enables predictions outside the previous experimental settings. The accuracy of predictions depends on the assumptions used to model the system. The objective of this study is to explore possible […]