"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 […]
"Ultra-Low Power and Robust Analog Computing Circuits and System Design Framework for Machine Learning Applications" Abstract: As the scaling of CMOS transistors has almost halted, performance gains of digital systems have also started to stagnate. There is a renewed interest in alternate computing techniques such as in-memory computing, hybrid computing, approximate computing, and analog computing. […]
"Multiple UAVs for Synchronous - Shared Tasks and Long-term Autonomy" Abstract: This thesis focuses on the use of multiple unmanned aerial vehicles(UAVs) in a distributed framework from a systems perspective to synchronously perform shared tasks such as aerial beamforming and coordinated mapping and to enhance the reliability of performing periodic (mapping) tasks at remote locations […]
"Deep Learning Acceleration on Edge Devices with Algorithm/Hardware Co-Design" Abstract: As deep learning has succeeded in a broad range of applications in recent years, there is an increasing trend towards deploying deep neural networks (DNNs) on edge devices such as FPGAs and mobile phones. However, there exists a significant gap between the extraordinary accuracy of […]
"Machine learning approaches for classification of myriad underwater acoustic events over continental-shelf scale regions with passive ocean acoustic waveguide remote sensing" Abstract: Underwater acoustic data contain a myriad of sound sources. Among underwater acoustic events, marine mammal vocalization classification is one of the most challenging problems due to their transient broadband calls, high variation in […]
Location: ISEC 532 "Combining Classical and Learning-based Methods for Visual and Tactile Manipulation" Abstract: Robots that operate in dynamic and ever-changing environments need to make sense of their surroundings and act in them safely and efficiently. This requires the integration of multiple sensory modalities such as visual and tactile. Humans can naturally fuse different feedbacks from […]
"Multiphysics Simulation of Graphene Transistors for On-Chip Plasmonic THz Signal Generation and Modulation" Abstract: Terahertz communication is envisioned as a key technology not only for the next generation of macro-scale networks (e.g., 6G+), but also for transformative networking applications at the nanoscale (e.g., wireless nanosensor networks and wireless networks on chip). This proposal focuses on […]