Title: Effective and Efficient Continual Learning Committee Members: Prof. Jennifer Dy (Advisor) Prof. Stratis Ioannidis Prof. Yanzhi Wang Abstract: Continual Learning (CL) aims to develop models that mimic the human ability to learn continually without forgetting knowledge acquired earlier. While traditional machine learning methods focus on learning with a certain dataset (task), CL methods adapt […]
Title:Decoupling Efficiency-Performance Optimization for Modern Neural Networks Date: 7/20/2023 Committee Members: Yanzhi Wang (Advisor); Prof. David Kaeli; Prof. Sunil Mittal; Prof. Jennifer Dy Abstract: Deep learning has achieved remarkable success in a variety of modern applications, but this success is often accompanied by inefficiency in terms of storage and inference speed, which can hinder their […]
Title: Real-Time Spectrum Sensing for Inference and Control Committee Members: Prof. Tommaso Melodia (Advisor) Prof. Kaushik Choudhury Prof. Francesco Restuccia Abstract: Through growing cellular innovations, the usage and congestion of the wireless spectrum is increasing at incredible speeds. High demand and limited supply pose a resource issue known as the "spectrum crunch". With the high […]
"Synthetic Data Generator: Understanding Human Face & Body via Image Synthesis" Committee Members: Prof. Yun Fu (Advisor) Prof. Sarah Ostadabbas Prof. Ming Shao Abstract: The community has long enjoyed the benefits of synthesizing data, as it provides a reliable and controllable source for training machine learning models while reducing the need for data collection from […]
Title: Enabling Domain Platform Design for Streaming Applications: A Holistic Approach Committee Members: Gunar Schirner (Advisor) Prof. David Kaeli Prof. Hamed Tabkhi (UNCC) Time: 10:00:00 AM Location: ISEC 601 Abstract: In recent years, more demanding streaming applications make striking a balance between high compute performance and efficiency paramount in platforms designs for edge computing. In […]
Title: Making Deep Neural Network Transparent Date: 11/29/2023 Time: 3:00:00 pm Committee Members: Prof. Jennifer Dy (Advisor) Prof. Mario Sznaier Prof. Eduardo Sontag Prof. Peter Castaldi Abstract: As machine learning algorithms are deployed ubiquitously to a variety of domains, it is imperative to make these often black-box models transparent. The ability to interpret and comprehend […]
Title: Making Deep Neural Network Transparent Date: 11/29/2023 Time: 4:30:00 PM Committee Members:- Prof. Jennifer Dy Prof. Eduardo Sontag Prof. Mario Sznaier Prof. Peter Castaldi Abstract: As machine learning algorithms are deployed ubiquitously to a variety of domains, it is imperative to make these often black-box models transparent. The ability to interpret and comprehend the […]
Title: Hardware Security Vulnerabilities in Deep Neural Networks and Mitigations Date: 12/4/2023 Time: 10:30:00 AM Committee Members: Prof. Yunsi Fei (Advisor) Prof. Aidong Ding Prof. Xue Lin Prof. Xiaolin Xu Abstract: In the past decade, Deep Neural Networks (DNNs) have become pivotal in numerous fields, including security-sensitive autonomous driving and privacy-critical medical diagnosis. This Ph.D. […]
Title: COPILOT: Cooperative Perception using Lidar for Handoffs between Road Side Units Committee Members: Prof. Kaushik Chowdhury (Advisor) Prof. Stratis Ioannidis Prof. Jennifer Dy Abstract: This thesis presents COPILOT, a ML-based approach that allows vehicles requiring ubiquitous high bandwidth connectivity to identify the most suitable road side units (RSUs) through proactive handoffs. By cooperatively exchanging […]
Title: Wireless Intelligence: A Comprehensive Exploration of AI-Driven Solutions in Channel Estimation, Beam Refinement, and Protocol Classification for Next Generation Networks Committee Members: Prof. Kaushik Chowdhury (advisor) Prof. Stratis Ioannidis Dr. Chris Dick Abstract: his thesis explores the transformative impact of artificial intelligence (AI) on wireless systems through model-driven simulations and real-world datasets, with a […]