"Unleashing the Potential of Transfer Learning for Visual Applications" Committee Members: Prof. Raymond Fu (Advisor) Prof. Sarah Ostadabbas Prof. Zhiqiang Tao Abstract: The recent flourish of deep learning in various tasks is largely accredited to the rich and accessible labeled data. Nonetheless, massive supervision remains a luxury for many real-world applications. Further, the domain shift […]
"Deep Generative Models for High Dimensional Spatial and Temporal Data Analysis" Committee Members: Prof. Vinay Ingle (Advisor) Dr. Dimitris Manolakis Prof. Purnima Ratilal-Makris Abstract: Data analysis and exploitation in practical applications is challenging when observations are the result of many interacting natural and man-made phenomena. We address two important problems for which traditional methods of […]
Title: Software-Defined Underwater Acoustic Networks Committee Members: Prof. Tommaso Melodia (Advisor) Prof. Stefano Basagni Prof. Kaushik Chowdhury Dr. Emrecan Demirors Abstract: The exploration, monitoring, and understanding of oceans play a crucial role in addressing climate change, overseeing underwater pipelines, and preventing maritime warfare attacks. To achieve these significant objectives, it is vital to utilize networks […]
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 […]