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 […]

COE graduate students are invited to join the CommLab’s interactive LinkedIn workshop where we’ll be optimizing your profile and learning how to effectively connect with people on the platform to build a strong professional network.  This workshop meets every other Monday on Zoom or in 306 Egan.  Drop-in’s welcome!

Looking to create a standout CV or resume that showcases your skills and experience in a professional way? COE graduate students are invited to join the CommLab’s LaTeX workshop! Our workshop provides support in CV/resume building using LaTeX, and will give you a better and clearer understanding of different aspects of the LaTeX code. You’ll […]

Learn how you can earn a Graduate Certificate in Engineering Leadership as a stand-alone certificate or in combination with one of twenty plus Master of Science degrees offered through Northeastern’s College of Engineering, College of Science, or Khoury College of Computer Sciences.  The National Academy of Engineering recognized The Gordon Institute of Engineering Leadership (GIEL) […]

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 […]

Title: Leveraging Deep Learning on Multimodal Sensor Data for Wireless Communication: From mmWave Beamforming to Digital Twins Committee Members: Prof. Kaushik Chowdhury (Advisor) Prof. Hanumant Singh Prof. Josep Jornet Dr. Mark Eisen Abstract: With the widespread Internet of Things (IoT) devices, a wide variety of sensors are now present in different environments. For example, self-driving […]

Title: Towards Efficient Deep Learning in Computer Vision via Sparsity and Distillation Committee Members: Prof. Yun Fu (Advisor) Prof. Octavia Camps Prof. Zhiqiang Tao Abstract: AI, empowered by deep learning, has been profoundly transforming the world. However, the excessive size of these models remains a central obstacle that limits their broader utility. Modern neural networks […]

Learn how you can earn a Graduate Certificate in Engineering Leadership as a stand-alone certificate or in combination with one of twenty-three Master of Science degrees offered through Northeastern’s College of Engineering, College of Science, or Khoury College of Computer Sciences. The National Academy of Engineering recognized The Gordon Institute of Engineering Leadership (GIEL) for […]

"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 […]