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X-WR-CALDESC:Events for Northeastern University College of Engineering
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SUMMARY:Batool Salehihikouei PhD Dissertation Defense
DESCRIPTION:Announcing:\nPhD Dissertation Defense \nName:\nBatool Salehihikouei \nTitle:\nLeveraging Deep Learning on Multimodal Sensor Data for Wireless Communication: From mmWave Beamforming to Digital Twins \nDate:\n4/3/2024 \nTime:\n11:00:00 AM \nLocation: EXP-601A \nCommittee Members:\nProf. Kaushik Chowdhury (Advisor)\nProf. Hanumant Singh\nProf. Josep Jornet\nDr. Mark Eisen \nAbstract:\nWith the widespread Internet of Things (IoT) devices\, a wide variety of sensors are now present in different environments. For example\, self-driving vehicles and automated warehouses depend on sensor information for navigation and management of the robots\, respectively. In this dissertation\, we present methods\, where these sensors are re-purposed to assist network management in wireless communication\, especially when classic approaches fall short to provide the required quality of service (QoS). This thesis presents data-driven and AI-based methods\, where the multimodal sensor information is used for two applications: (i) beamforming at the mmWave band and (ii) joint optimization of the navigation and network management in warehouse environments. In the first part\, we study multimodal beamforming methods for mmWave vehicular networks. First\, we present deep learning fusion algorithms\, where the inputs from a multitude of sensor modalities such as GPS (Global Positioning System)\, camera\, and LiDAR (Light Detection and Ranging) are combined towards predicting the optimum beam at the mmWave band. We prove that fusing the multimodal sensor data improves the prediction accuracy\, compared to using single modalities. Second\, we study the trade-off between the accuracy and cost of different learning strategies and demonstrate that federated learning is the most successful learning strategy\, with respect to the communication overhead. Third\, we propose algorithms to further optimize the communication overhead by incorporating a pruning strategy tailored to the disturbed nature of the federated learning systems. Fourth\, we propose a modality-agnostic deep learning paradigm that operates on any possible combination of sensor modalities. In part two\, we propose using digital twins to overcome the challenges of scarcity of data and close-world assumption in deep learning algorithms. A digital twin is a replica of a real world entity\, which is typically used for studying the impact of any configuration settings in a safe\, digital environment. In this dissertation\, we propose a framework that operates by harmonic usage of the DL models and running emulations in the twin. Moreover\, we use digital twins to generate training labels and fine-tune the models for unseen scenarios. Finally\, we study a robotic industrial setting\, where the path planning policy is continuously updated by monitoring the dynamics of the real world\, constructing the digital twin\, and updating the policy. The constructed twin captures the features of both physical and RF environments in the digital world and includes a reinforcement learning algorithm that jointly optimizes navigation and network resource management.
URL:https://coe.northeastern.edu/event/batool-salehihikouei-phd-dissertation-defense/
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DTSTART;TZID=America/New_York:20240403T120000
DTEND;TZID=America/New_York:20240403T130000
DTSTAMP:20260511T160328
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LAST-MODIFIED:20240223T212831Z
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SUMMARY:Learn about engineering program opportunities in Silicon Valley \, CA
DESCRIPTION:The Graduate school of Engineering is proud to offer programs on many of Northeastern University’s multiple global campuses. In this webinar\, we focus on spotlighting the Silicon Valley\, CA campus. You’ll have an opportunity to learn more about the programs and opportunities available on this campus from admissions and campus representatives.
URL:https://coe.northeastern.edu/event/learn-about-engineering-program-opportunities-in-silicon-valley-ca/
ORGANIZER;CN="Graduate School of Engineering":MAILTO:coe-gradadmissions@northeastern.edu
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DTSTART;TZID=America/New_York:20240403T120000
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DTSTAMP:20260511T160328
CREATED:20240326T142857Z
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SUMMARY:Chemical Engineering Spring Seminar Series: Dr. Sindia M. Rivera Jiménez
DESCRIPTION:Professional Organizations and Social Responsibility in Chemical Engineering Education \nProfessional organizations (POs) are established communities that significantly influence the competencies and values of engineers\, but the impact of their interaction with academia on undergraduate education is not fully understood. This study addresses this gap by exploring how engineering faculty in POs strategically incorporate social responsibility into their teaching. Relying on Paulo Freire’s critical consciousness and the Transformational Agency framework\, it examines faculty reflections on societal and power dynamics for curriculum change. \nConducted over eight months\, the study focuses on a Community of Practice (CoP) within the American Institute of Chemical Engineering’s Education Division\, engaging faculty from multiple institutions. We employed qualitative methods\, analyzing interview data through thematic analysis with In-Vivo and Axial coding. Preliminary results highlight how the CoP influences faculty’s reflective practices and understanding of societal structures\, suggesting it enhances educators’ critical awareness and ability to integrate social responsibility into their teaching. \nThe findings deepen our understanding of POs’ role in evolving engineering education. They showcase how educators’ involvement in POs can shape socially responsible engineers\, addressing the complex societal roles engineers face. This seminar aims to inspire educators with strategies for creating transformative learning environments. \n\nDr. Rivera-Jiménez is an Assistant Professor in the Department of Engineering Education at the University of Florida and is affiliated with the Department of Chemical Engineering and the Institute of Higher Education. Her research group focuses on community-driven methods to improve practices and policies that enhance the professional formation of engineers and impact the success of diverse engineering communities\, including faculty\, undergraduate and graduate students\, and transfer students. Current projects include faculty support via professional societies\, student motivation and emotions in blended learning\, and studying diverse transfer student success within organizational contexts. \nAdditionally\, she hosts “The Engineering Professor Speaks Education Podcast\,” a bilingual series exploring the nuances of being an effective engineering educator. Her most recent accolades include the AIChE IDEAL Star Award (2021)\, the AIChE Education Division Service Award (2022)\, and the ASEE Education Research Methods Apprentice Faculty Grantee Award (2023).
URL:https://coe.northeastern.edu/event/chemical-engineering-spring-seminar-series-dr-sindia-m-rivera-jimenez/
LOCATION:103 Churchill\, 103 Churchill Hall\, 360 Huntington Ave\, Boston\, MA\, 02115\, United States
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DTSTART;TZID=America/New_York:20240403T153000
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SUMMARY:Kaustubh Shivdikar PhD Dissertation Defense
DESCRIPTION:Announcing:\nPhD Dissertation Defense \nName:\nKaustubh Shivdikar \nTitle:\nEnabling Accelerators for Graph Computing \nDate:\n4/3/2024 \nTime:\n3:30 PM \nLocation: Zoom \nCommittee Members:\nProf. David Kaeli (Advisor)\nProf. Devesh Tiwari\nProf. Ajay Joshi (Boston University)\nProf. John Kim (KAIST)\nProf. José Luis Abellán (University of Murcia) \nAbstract:\nThe advent of Graph Neural Networks (GNNs) has revolutionized the field of machine learning\, offering a novel paradigm for learning on graph-structured data. Unlike traditional neural networks\, GNNs are capable of capturing complex relationships and dependencies inherent in graph data\, making them particularly suited for a wide range of applications including social network analysis\, molecular chemistry\, and network security. The impact of GNNs in these domains is profound\, enabling more accurate models and predictions\, and thereby contributing significantly to advances in these fields. \nGNNs\, with their unique structure and operation\, present new computational challenges compared to conventional neural networks. This requires comprehensive benchmarking and a thorough characterization of GNNs to obtain insight into their computational requirements and to identify potential performance bottlenecks. In this thesis\, we aim to develop a better understanding of how GNNs interact with the underlying hardware and will leverage this knowledge as we design specialized accelerators and develop new optimizations\, leading to more efficient and faster GNN computations. \nA pivotal component within GNNs is the Sparse General Matrix-Matrix Multiplication (SpGEMM) kernel\, known for its computational intensity and irregular memory access patterns. In this thesis\, we address the challenges posed by SpGEMM by implementing a highly optimized hashing-based SpGEMM kernel tailored for a custom accelerator. This optimization is crucial to enhancing the performance of GNN workloads\, ensuring that the acceleration potential of custom hardware is fully realized. \nSynthesizing these insights and optimizations\, we design state-of-the-art hardware accel-erators capable of efficiently handling various GNN workloads. Our accelerator architectures are built on our characterization of GNN computational demands\, providing clear motivation for our approaches. Furthermore\, we extend our exploration to emerging GNN workloads in the domain of graph neural networks. This exploration into novel models underlines our comprehensive approach\, as we strive to enable accelerators that are not just performant\, but also versatile\, able to adapt to the evolving landscape of graph computing.
URL:https://coe.northeastern.edu/event/kaustubh-shivdikar-phd-dissertation-defense/
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