"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 […]
ADDITIVELY MANUFACTURED ELECTRONICS (AME) - THE NEXT GENERATION OF ELECTRONIC DEVICES AND CIRCUITS FROM 2D TO 3D AME Academy will be delivering a 1 day event at Northeastern University, Boston MA on July 29, 2022. Join the team of experts led by electronics industry veteran Mr. Gene Weiner, member of the IPC’s Raymond E. Pritchard Hall […]
Want to learn Python, but don’t know where to start? We’ll walk you through everything you need to start using Python, beginning with writing code for the very first time. In this interactive workshop, you will learn about and practice core Python skills such as writing functions, troubleshooting errors, and installing packages. We will also cover tips […]
"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 […]
Learn how you can earn a Graduate Certificate in Engineering Leadership as a stand-alone certificate or in combination with one of twenty 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 its […]
"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. […]
Who is your audience, what do you want to say, and what do they want to hear? Many graduate students serve in roles of leadership, teaching and mentoring. This free workshop will give you the tools you need to be an effective leader, teacher and mentor. We will practice using these tools by role playing […]
"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 […]