"Hardware Security Vulnerabilities in Deep Neural Networks and Mitigations" Committee Members: Prof. Yunsi Fei (Advisor) Prof. Xue Lin Prof. Xiaolin Xu Abstract: Over the past decade, Deep Neural Networks (DNNs) have revolutionized numerous fields. With the increasing deployment of DNN models in security-sensitive and mission-critical applications, such as autonomous driving, ensuring the security and privacy […]
"Unveiling the Power of Transfer Learning in Data-Driven AI" Committee Members: Prof. Raymond Fu (Advisor) Prof. Octavia Camps Prof. Huaizu Jiang Abstract: The big data stands as a cornerstone of deep learning, which has significantly improved a wide range of machine learning and computer vision tasks. Despite such a great success, data collection is time-consuming […]
Dean Gregory D. Abowd will present his SIGCHI Lifetime Research Award Acceptance Lecture Date: Tues., June 13, 2023 Time: 5:30 to 7:15 PM, reception following Dean Abowd’s talk Place: In-Person and Livestream BostonCHI meeting at Northeastern University in ISEC Auditorium (102 ISEC), and reception in ISEC Atrium Registration is appreciated but not required. View […]
Title: Enabling Anomaly Detection in Complex Chemical Mixtures Through Multimodal Data Fusion Date: 6/26/2023 Time: 10:00:00 AM Location: SH 210, Committee Members: Prof. Mueller (Advisor) Prof. Erdogmus Prof. Ioannidis Prof. Onnis-Hayden Abstract: Recently innovations in machine learning and data processing are increasingly tied to ensuring useability and interpretability when these methods are applied within end-user […]
"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 […]