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DTSTART;TZID=America/New_York:20240403T110000
DTEND;TZID=America/New_York:20240403T123000
DTSTAMP:20260424T231511
CREATED:20240403T182458Z
LAST-MODIFIED:20240403T182458Z
UID:43174-1712142000-1712147400@coe.northeastern.edu
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/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240403T120000
DTEND;TZID=America/New_York:20240403T130000
DTSTAMP:20260424T231511
CREATED:20240223T212831Z
LAST-MODIFIED:20240223T212831Z
UID:42524-1712145600-1712149200@coe.northeastern.edu
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240403T120000
DTEND;TZID=America/New_York:20240403T130000
DTSTAMP:20260424T231511
CREATED:20240326T142857Z
LAST-MODIFIED:20240326T142857Z
UID:43035-1712145600-1712149200@coe.northeastern.edu
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
GEO:42.3387735;-71.0889235
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240403T153000
DTEND;TZID=America/New_York:20240403T170000
DTSTAMP:20260424T231511
CREATED:20240319T141441Z
LAST-MODIFIED:20240319T141441Z
UID:42906-1712158200-1712163600@coe.northeastern.edu
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/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240404T120000
DTEND;TZID=America/New_York:20240404T130000
DTSTAMP:20260424T231511
CREATED:20240322T144254Z
LAST-MODIFIED:20240322T144254Z
UID:43007-1712232000-1712235600@coe.northeastern.edu
SUMMARY:Conflict Resolution and Effective Communication Skills
DESCRIPTION:Join us for our upcoming Graduate Greatness webinar on “Conflict Resolution and Effective Communication” presented by Kimberly Wong. \n📅 Date: Thursday\, April 4th \n🕒 Time: 12:00 – 1:00 p.m. EDT \n📍 Location: Zoom Webinar \nConflict is an inevitable part of any academic journey\, but with effective communication skills\, challenges can be transformed into opportunities for growth. In this virtual workshop\, we’ll dive into strategies for navigating conflict during graduate school. \nParticipants will have the opportunity to examine their own approaches to conflict\, identifying strengths and barriers along the way. Together\, we’ll explore methods to foster trust and understanding in professional relationships\, providing you with concrete strategies for improving dialogue with faculty\, staff\, and classmates. \nRegister for the event: http://tinyurl.com/mw79jbhz
URL:https://coe.northeastern.edu/event/conflict-resolution-and-effective-communication-skills/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240404T130000
DTEND;TZID=America/New_York:20240404T140000
DTSTAMP:20260424T231511
CREATED:20240403T182208Z
LAST-MODIFIED:20240403T182208Z
UID:43178-1712235600-1712239200@coe.northeastern.edu
SUMMARY:Anu Jagannath PhD Dissertation Defense
DESCRIPTION:Announcing:\nPhD Dissertation Defense \nName:\nAnu Jagannath \nTitle:\nDeep Learning at the Edge for Future G Networks: RF Signal Intelligence for Comprehensive Spectrum Awareness \nDate:\n4/4/2024 \nTime:\n1:00:00 PM \nCommittee Members:\nProf. Tommaso Melodia (Advisor)\nProf. Kaushik Chowdhury\nProf. Yanzhi Wang \nAbstract:\nFuture communication networks must address the scarce spectrum to accommodate extensive growth of heterogeneous wireless devices. Efforts are underway to address spectrum coexistence\, enhance spectrum awareness\, and bolster authentication schemes. Wireless signal recognition is becoming increasingly more significant for spectrum monitoring\, spectrum management\, secure communications\, among others. Consequently\, comprehensive spectrum awareness at the edge has the potential to serve as a key enabler for the emerging beyond 5G (fifth generation) networks. State-of-the-art studies in this domain have (i) only focused on a single task – modulation or signal (protocol) classification or radio frequency fingerprinting – which in many cases is insufficient information for a system to act on\, (ii) consider either radar or communication waveforms (homogeneous waveform category)\, and (iii) does not address edge deployment during neural network design phase. In this dissertation\, deep learning is applied to the various signal recognition problems from  a multi-task perspective with an emphasis on edge deployment. To address edge deployment\, various techniques are applied to solve the signal recognition problem under consideration (modulation\, wireless protocol\, emitter fingerprint recognition) to design scalable and computationally efficient framework. While designing the edge deployable architectures\, the generalization capability of the architectures are evaluated under various circumstances to quantify their performance under real-world settings such as emissions from actual emitters (commercial emissions wherever applicable)\, training with a different propagation scenario and testing under a never-before-seen setting. \nThe study was sectioned into different stages where multi-task learning is first applied to solving wireless standard and modulation recognition\, followed by applying deep compression for CBRS radar waveform classification\, next radio frequency fingerprinting for commercial WiFi and Bluetooth emissions were studied utilizing novel multi-task attentional architectures\, and finally the multi-task learning together with deep compression was employed to deploy the architectures in a real-time streaming radio testbed for real-time inferencing of wireless standard and modulation recognition. The feasibility of employing deep compression techniques are carefully evaluated in a real-world deployment setting to quantify the performance from a computational and inference capacity perspective.
URL:https://coe.northeastern.edu/event/anu-jagannath-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240404T153000
DTEND;TZID=America/New_York:20240404T170000
DTSTAMP:20260424T231511
CREATED:20240319T141622Z
LAST-MODIFIED:20240319T141622Z
UID:42904-1712244600-1712250000@coe.northeastern.edu
SUMMARY:Nicolas Bohm Agostini PhD Proposal Review
DESCRIPTION:Announcing:\nPhD Proposal Review \nName:\nNicolas Bohm Agostini \nTitle:\nHardware/Software Codesign and Compiler Techniques for Efficient Hardware Acceleration of Dense Linear Algebra Kernels and Machine Learning Applications \nDate:\n4/4/2024 \nTime:\n3:30:00 PM \nLocation: Zoom \nCommittee Members:\nProf. David Kaeli (Advisor)\nProf. Gunar Schirner\nProf. José Luis Abellán (University of Murcia)\nAntonino Tumeo (PNNL) \nAbstract:\nToday’s linear algebra and machine learning applications (ML) continue to grow in size and complexity\, placing rapidly increasing demands on the underlying hardware and software systems. To address these issues\, hardware designers have proposed using custom accelerators explicitly designed for accelerating these demanding workloads. What needs to be improved is the ability to perform efficient hardware/software (HW/SW) co-design in order to reap the full benefits from these platforms. This thesis presents an integrated solution to facilitate HW/SW accelerator design. We also address issues in accelerator deployment\, enabling rapid prototyping\, integrated benchmarking\, and comprehensive performance analysis of custom accelerators. \nIn this thesis\, we demonstrate the value of a lightweight system modeling library integrated into the build/execution environment\, leveraging TensorFlow~Lite for deployment. We also explore efficient design space exploration of different classes of accelerators while considering the impact of parameters. Secondly\, we employ the Multi-Level Intermediate Representation (MLIR) compiler framework to automatically partition host code from accelerator code\, pre-optimizing the latter for improved high-level synthesis designs and high-quality accelerated kernels. Lastly\, we propose compiler extensions to automate the generation and optimization of communication between the host CPU and AXI-based accelerators. \nWe present novel solutions that enable more efficient and effective design space exploration\, optimization\, and deployment of custom accelerators. The utility of these approaches is demonstrated through experiments with specific accelerator designs and key linear algebra and ML workloads. Most importantly\, these solutions empower high-level language users\, such as domain scientists\, to participate in the design of new accelerator features.
URL:https://coe.northeastern.edu/event/nicolas-bohm-agostini-phd-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240408T141500
DTEND;TZID=America/New_York:20240408T143000
DTSTAMP:20260424T231511
CREATED:20240405T203435Z
LAST-MODIFIED:20240405T212740Z
UID:43245-1712585700-1712586600@coe.northeastern.edu
SUMMARY:Soft Matter Days
DESCRIPTION:Soft Matter Days: April 8-17\, will feature invited guest speakers discussing a variety of interdisciplinary topics in soft matter and complex fluids.  These topics sit at the interface of chemical & mechanical engineering\, materials science\, physics\, chemistry\, and biology.  Guest speakers will discuss real-world phenomena found in food\, blood flow\, and granular materials.  Two talks are guest lectures in CHME5179: RSVP required for those not in the class. \nMonday\, April 8\, 2:15pm\, Curry 340\nCapillary Rise and Thin Films Near Edges: New Insights from Self-similarity\nHoward Stone\, Princeton University\nHost: Xiaoyu Tang x.tang@northeastern.edu \nTuesday\, April 9\, 9:50am\, Zoom (Guest Lecture for CHME 5179)\n“Complex Fluids & Soft Matter in Food”\nDave Weitz\, Harvard University\nRSVP: Sara Hashmi s.hashmi@northeastern.edu \nThursday\, April 11\, 1:30pm\, HS 210\nDynamics of blood flow at the cellular level in health and disease\nMichael Graham\, University of Wisconsin\nHost: Sara Hashmi s.hashmi@northeastern.edu \nFriday\, April 12\, 9:50am\, Zoom (Guest Lecture for CHME 5179)\nNonlinear Rheology of Complex Fluids: Exploring Microstructure\nKate Honda\, Northeastern University\nRSVP: Sara Hashmi s.hashmi@northeastern.edu \nWednesday\,  April 17\, 1:30pm\, HS 210\nUniversality and scaling in shear thickening suspensions\nBulbul Chakraborty\, Brandeis University\nHost: Sara Hashmi s.hashmi@northeastern.edu
URL:https://coe.northeastern.edu/event/soft-matter-days/2024-04-08/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240408T153000
DTEND;TZID=America/New_York:20240408T170000
DTSTAMP:20260424T231511
CREATED:20240403T182632Z
LAST-MODIFIED:20240403T182632Z
UID:43172-1712590200-1712595600@coe.northeastern.edu
SUMMARY:Jinkun Zhang PhD Dissertation Defense
DESCRIPTION:Announcing:\nPhD Dissertation Defense \nName:\nJinkun Zhang \nTitle:\nLow-latency Forwarding\, Caching and Computation Placement in Data-centric Networks \nDate:\n4/8/2024 \nTime:\n3:30:00 PM \nLocation:\nEXP-459\, \nCommittee Members:\nProf. Edmund Yeh (Advisor)\nProf. Stratis Ioannidis\nProf. Kaushik Chowdhury \nAbstract:\nWith the exponential growth of data- and computation-intensive network applications\, such as real-time augmented reality/virtual reality rendering and large-scale language model training\, traditional cloud computing frameworks exhibit inherent limitations. To address these challenges\, dispersed computing has emerged as a promising next-generation networking paradigm. By enabling geographically distributed nodes with heterogeneous computation capabilities to collaborate\, dispersed computing overcomes the bottlenecks of traditional cloud computing and facilitates in-network computation tasks\, including the training of large models. In data-centric networks\, communication and computation are resolved around data names instead of host addresses. The deployment of network caches\, by enabling data reuse\, offers substantial benefits for data-centric networks. For instance\, consider a scenario where multiple machine learning applications seek to train different models simultaneously. This application could (partially) share data samples and/or computational results. Optimal caching of data and/or results can significantly reduce the overall training cost\, compared to each application independently gathering and transmitting data. \nThis dissertation aims to minimize average user delay in a general cache-enabled computing network. We introduce a low-latency framework that jointly optimizes packet forwarding\, storage deployment\, and computation placement. The proposed framework effectively supports data-intensive and latency-sensitive computation applications in data-centric computing networks with heterogeneous communication\, storage\, and computation capabilities. To minimize user latency in congestible networks\, we model delays caused by link transmissions and CPU computations using traffic-dependent nonlinear functions. We consider a series of related network resource allocation problems in a unified network model. \nWe first investigate the joint forwarding and computation placement problem\, then the joint forwarding and elastic caching problem. Despite the non-convexity of the former subproblem\, we provide a set of sufficient optimality conditions that lead to a distributed algorithm with polynomial-time convergence to the global optimum. For the latter subproblem\, we demonstrate its NP-hardness and non-submodularity\, even after continuous relaxation. We propose a set of conditions that provide a finite bound from the optimum. To the best of our knowledge\, our method represents the first analytical progress in addressing the joint caching and forwarding problem with arbitrary topology and non-linear costs. Upon solving the above two subproblems\, we formally propose the low-latency joint forwarding\, caching\, and computation placement framework. We formulate the mixed-integer NP-hard total cost minimization problem jointly over forwarding\, caching\, and computation offloading variables. Developing on the established result for both subproblems\, we propose two methods\, each with an analytical guarantee. The first method achieves a 1/2 approximation guarantee by exploiting the “submodular + concave” structure of the problem\, leading to an offline distributed algorithm. In real scenarios\, however\, request patterns and network status are not known prior and can be time-varying. To this end\, our second method leads to an online adaptive algorithm exploiting its “convex + geodesic-convex” nature\, with a proven bounded gap from the optimum. \nThe proposed solutions are followed by a few extension problems. Specifically\, we generalize the computation from “single-step” to “service chain” applications. We also generalize the solution to incorporate congestion control by considering an “extended graph”. Furthermore\, several network resource allocation optimization problems related to data-centric networking are introduced\, expanding the scope of this dissertation. For example\, we investigate joint caching and transmission power allocation in wireless heterogeneous networks\, where the total transmission energy is minimized subject to constraints for SINR lower bounds\, cache capacities\, and total power budget at each node. We also study the optimal multi-commodity pricing with finite menu length\, where novel asymptotic bounds on quantization errors are devised.
URL:https://coe.northeastern.edu/event/jinkun-zhang-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240409T090000
DTEND;TZID=America/New_York:20240409T100000
DTSTAMP:20260424T231511
CREATED:20240223T212744Z
LAST-MODIFIED:20240223T212744Z
UID:42522-1712653200-1712656800@coe.northeastern.edu
SUMMARY:Learn about engineering program opportunities in Miami\, FL
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 Miami\, FL 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-miami-fl/
ORGANIZER;CN="Graduate School of Engineering":MAILTO:coe-gradadmissions@northeastern.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240410T120000
DTEND;TZID=America/New_York:20240410T130000
DTSTAMP:20260424T231511
CREATED:20240328T155531Z
LAST-MODIFIED:20240328T155531Z
UID:43110-1712750400-1712754000@coe.northeastern.edu
SUMMARY:Chemical Engineering Spring Seminar Series: Dr. Jodie Lutkenhaus
DESCRIPTION:Organic Batteries for a More Sustainable Future \nCobalt\, nickel\, and lithium are essential ingredients in today’s lithium-ion batteries (LIBs)\, but their continued use presents economic\, ethical\, and environmental challenges. Society must now begin to consider the implications of a LIB’s full life cycle\, including the carbon footprint\, the economic and environmental costs\, and material access. These challenges motivate the case for degradable or recyclable batteries sourced from earth-abundant materials whose life cycle bears minimal impact on the environment. This presentation considers organic polymer-based batteries\, which have the potential to address many of these issues. Redox-active polymers form the positive and negative electrodes\, storing charge through a reversible redox mechanism. We demonstrate polypeptide radical batteries that degrade on command into amino acids and by-products as a first step toward circular organic batteries. Further\, we show the recycling of redox-active polymer electrodes using a solvent-based approach. Polymer-air batteries are examined as high-capacity alternatives to metal-air batteries. The molecular mechanism for each case is investigated\, revealing pathways forward for improving each polymer’s performance. Taken together\, organic batteries offer the promise of a circular platform free of critical elements. \n\nJodie L. Lutkenhaus is a Professor\, Associated Department Head\, and holder of the Axalta Chair in the Artie McFerrin Department of Chemical Engineering at Texas A&M University. Lutkenhaus received her B.S. in 2002 from The University of Texas at Austin and her Ph.D in 2007 from the Massachusetts Institute of Technology. Current research areas include polyelectrolytes\, redox-active polymers\, energy storage\, and composites. She has received recognitions including World Economic Forum Young Scientist\, Kavli Fellow\, NSF CAREER\, AFOSR Young Investigator\, and the 3M Non-tenured Faculty Award. She is the past-Chair of the AICHE Materials Engineering & Sciences Division. Lutkenhaus is the Deputy Editor of ACS Applied Polymer Materials and a member of the U.S. National Academies Board of Chemical Sciences & Technology.
URL:https://coe.northeastern.edu/event/chemical-engineering-spring-seminar-series-dr-jodie-lutkenhaus/
LOCATION:103 Churchill\, 103 Churchill Hall\, 360 Huntington Ave\, Boston\, MA\, 02115\, United States
GEO:42.3387735;-71.0889235
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=103 Churchill 103 Churchill Hall 360 Huntington Ave Boston MA 02115 United States;X-APPLE-RADIUS=500;X-TITLE=103 Churchill Hall\, 360 Huntington Ave:geo:-71.0889235,42.3387735
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240411T090000
DTEND;TZID=America/New_York:20240411T100000
DTSTAMP:20260424T231511
CREATED:20240408T134711Z
LAST-MODIFIED:20240410T141025Z
UID:43283-1712826000-1712829600@coe.northeastern.edu
SUMMARY:Giving Day Donuts with the Dean
DESCRIPTION:All faculty\, staff\, and students are invited to have donuts with Dean Gregory Abowd and kick off an exciting day of activities and college challenges. We need your support — a gift to the College of Engineering is an investment in our students\, faculty\, and programs! Make your gift today. \nLocation: outside of Snell Engineering\, by the entryway facing Egan. If raining\, inside SN Lobby
URL:https://coe.northeastern.edu/event/giving-day-donuts-with-the-dean/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240411T120000
DTEND;TZID=America/New_York:20240411T133000
DTSTAMP:20260424T231511
CREATED:20240410T210113Z
LAST-MODIFIED:20240410T210113Z
UID:43325-1712836800-1712842200@coe.northeastern.edu
SUMMARY:Gerald LaMountain PhD Proposal Review
DESCRIPTION:Name:\nGerald LaMountain \nTitle:\nOn the Performance of Classical Estimation Under Adverse\nConditions \nDate:\n4/11/2024 \nTime:\n12:00:00 PM \nLocation:\nEXP 459 \nCommittee Members:\nProf. Pau Closas (Advisor)\nProf. Deniz Erdogmus\nProf. Aanjhan Ranganathan \nAbstract:\nSystem designers across all disciplines of technology face the need to develop machines capable of independently processing and analyzing data and\, in many cases\, subsequently predicting future data. Over the past century\, numerous approaches have been developed to perform this task\, including those that fall under the umbrella of “classical statistics;” that is\, those that employ probabilistic analyses to isolate relationships between variables and\, in particular\, “statistical estimation” wherein those variables are used to make inferences about real-world quantities. To fully leverage the bevy of established estimation algorithms\, it is necessary to be able to evaluate the performance of a given estimator and\, where possible\, make changes to the methodology to improve its performance according to pertinent metrics. In the presence of ground-truth information\, the accuracy of estimations can be evaluated a posteriori for a specific set of data. But what of future data which may not be associated with the same set of ground-truth information? In these cases\, we require statistical generalizations about estimator behavior based on models of observed reality. In reality\, these models are rarely fully representative of the reality of the observed and latent variables of interest. In such cases\, there exists a “model misspecification\,” and estimators which are designed based on such an imprecise model will produce results which differ from both properly specified estimators and the truth. \nThe overall objective of this thesis is to evaluate and expand upon state-of-the-art approaches to estimation and estimator analysis under various types of misspecification\, including modeling errors that naturally occur as a result of the sensory environment\, for example\, unknown or variable observation noise. We contribute a method of Bayesian covariance estimation which\, when embedded within the Kalman filter architecture\, may be used to adapt to real-time changes in sensor performance while maintaining the recursive structure that allows the Kalman filter to be implemented in so many different applications. Furthermore\, we investigated the efficacy of signal subspace algorithms (e.g. MUSIC) for performing multi-antenna radio direction finding\, again in the presence of modelling errors. Although these algorithms are considered suboptimal (in the sense of the minimum mean squared error—MMSE) in finite time\, their computational efficiency motivates their use in many different applications. Our analysis shows that under certain classes of model misspecification\, the candidate algorithm for misspecified multiple signal classification (MMUSIC) performs asymptotically as well as the “gold standard” maximum likelihood estimator (MMLE) under the same misspecification. The final objective of this thesis is to combine the estimation bounds analysis we have applied to static estimation and extend it to dynamical systems. Although there exist established methods for evaluating and bounding the performance of estimators on misspecified models and dynamic models\, there has been limited progress in establishing a standard for performing misspecified estimator analysis under dynamic conditions. Although this work is still ongoing\, preliminary results are encouraging\, suggesting that there are likely multiple approaches to this bounded analysis based around different objectives. Further results will be included in the final version of our work.
URL:https://coe.northeastern.edu/event/gerald-lamountain-phd-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240412T120000
DTEND;TZID=America/New_York:20240412T130000
DTSTAMP:20260424T231511
CREATED:20240403T182325Z
LAST-MODIFIED:20240403T182325Z
UID:43176-1712923200-1712926800@coe.northeastern.edu
SUMMARY:Baolin Li PhD Dissertation Defense
DESCRIPTION:Announcing:\nPhD Dissertation Defense \nName:\nBaolin Li \nTitle:\nMaking Machine Learning on HPC Systems Cost-Effective and Carbon-Friendly \nDate:\n4/12/2024 \nTime:\n12:00:00 PM
URL:https://coe.northeastern.edu/event/baolin-li-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240412T123000
DTEND;TZID=America/New_York:20240412T140000
DTSTAMP:20260424T231511
CREATED:20240403T181805Z
LAST-MODIFIED:20240403T181805Z
UID:43184-1712925000-1712930400@coe.northeastern.edu
SUMMARY:Peng Wu PhD Dissertation Defense
DESCRIPTION:Announcing:\nPhD Dissertation Defense \nName:\nPeng Wu \nTitle:\nBayesian Data Fusion for Distributed Learning \nDate:\n4/12/2024 \nTime:\n12:30:00 PM \nLocation:\nISEC 532 \nCommittee Members:\nProf. Pau Closas (Advisor)\nProf. Deniz Erdogmus\nProf. Lili Su \nAbstract:\nThe necessity for distributed data fusion arises from the increasing demand to integrate diverse and voluminous data sources\, especially in applications where large numbers of users are collaborating to perform inference and learning tasks. This integration is crucial when data is available in a distributed manner or originates from various sensor types\, aiming to deduce specific quantities of interest accurately. Moreover\, the importance of privacy cannot be overstated\, particularly in scenarios where sensitive information\, such as location data\, is involved. Federated learning emerges as a pivotal solution in this context\, enabling model training on local datasets without the need to exchange the data itself\, thus preserving user privacy. However\, the deployment of these technologies encounters significant challenges\, including the multiple counting problem in data fusion\, where data may be redundantly used across different estimations without user awareness\, and the non-IID problem in federated learning\, where the non-identically distributed nature of data across clients can severely hamper the model’s performance. \nTo address these challenges\, this dissertation explores the intersection of data fusion\, federated learning\, and Bayesian methods\, with a focus on applied problems in indoor localization\, satellite-based navigation\, and image processing that spans both theoretical analysis and practical application. In the realm of data fusion\, we delve into the Bayesian framework to offer a solution that not only facilitates the optimal integration of sensor data with prior knowledge but also navigates the intricacies of feature fusion effectively. This approach mitigates the multiple counting issue by ensuring that the fusion of local estimates accounts for the overuse of prior knowledge. In tackling the problems inherent to federated learning\, particularly the non-IID issue\, we introduce novel frameworks and algorithms designed to enhance model training and performance in a privacy-preserving manner. We explore personalized and clustered federated learning as methods to customize the learning process to individual client characteristics and to group clients with similar data traits\, respectively. A number of practical problems are explored using those federated methodologies\, including indoor fingerprinting\, jamming interference classification\, or image classification tasks. Noticeably\, this thesis proposes a novel Bayesian clustered federated learning framework that generalizes existing clustered federated learning schemes by leveraging Bayesian data association modeling. By implementing a Bayesian perspective within these frameworks\, the dissertation proposes practical algorithms that achieve a balance between performance and computational efficiency\, ultimately advancing the application of distributed data fusion and federated learning in privacy-sensitive fields.
URL:https://coe.northeastern.edu/event/peng-wu-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240415T133000
DTEND;TZID=America/New_York:20240415T143000
DTSTAMP:20260424T231511
CREATED:20240410T210005Z
LAST-MODIFIED:20240410T210005Z
UID:43327-1713187800-1713191400@coe.northeastern.edu
SUMMARY:Lin Deng PhD Dissertation Defense
DESCRIPTION:Name:\nLin Deng \nTitle:\nFunction Capacity Expansion of Nano-Optics via Multiplexed Metasurfaces \nDate:\n4/15/2024 \nTime:\n1:30:00 PM \nLocation:\nSL 011 \nCommittee Members:\nProf. Yongmin Liu (advisor)\nProf. Hossein Mosallaei\nProf. Sunil Mittal \nAbstract:\nThroughout history\, the exploration of light has been fundamental to our understanding of the world and has driven advancements in technology and communication. Metasurfaces\, composed of rationally designed nanostructures\, offer a revolutionary means to control light in a prescribed manner. Metasurfaces can operate in conventional free space\, and the emerging integrated photonics domain. Maximizing functionality and degrees of freedom (DOFs) in both arenas is paramount. My thesis aims to push the limit of metasurface capabilities by leveraging multiplexing strategies across input/output parameters such as polarization\, incidence angle\, and waveguide mode. I will present three novel metasurfaces as follows. \n(1) We aim to expand nano-printing multiplexing capacity using the Polarization-Encoded Lenticular Nano-Printing (Pollen) method. When employing three input/output polarization pairs and varying detection angles\, a single metasurface device enables the observation of up to 49 high-resolution nano-printing images. \n(2) By integrating metasurfaces with waveguides\, we can couple guided modes to free space while controlling wavefront and polarization. Our research exploits the multiplexed on-chip metasurface\, which could generate multiple functions depending on the polarization states and waveguide mode propagation directions. \n(3) We investigated mode division multiplexing (MDM) for high-volume optical transmission\, enabling multiple waveguide modes to coexist without interference. By manipulating the orientations of individual nanoantennas\, we have achieved on-demand mode conversion and focusing effects\, demonstrating promising results in various scenarios.  \nIn conclusion\, my research seeks to push the boundaries of metasurface functionalities through innovative multiplexing approaches. The research findings allow us to unlock new possibilities in optical display\, communication\, manipulation\, and beyond by integrating multiple functionalities into single free-space and on-chip metasurfaces.
URL:https://coe.northeastern.edu/event/lin-deng-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240415T140000
DTEND;TZID=America/New_York:20240415T153000
DTSTAMP:20260424T231511
CREATED:20240410T205619Z
LAST-MODIFIED:20240410T210310Z
UID:43334-1713189600-1713195000@coe.northeastern.edu
SUMMARY:David Femi Lamptey MS Thesis Defense
DESCRIPTION:Name:\nDavid Femi Lamptey \nTitle:\nCoordinating Camera and Millimeter-Wave Imaging Systems to Detect Concealed Threats in Public Spaces \nDate:\n4/15/2024 \nTime:\n2:00:00 PM \nLocation:\nSnell Library CoLab J \nCommittee Members:\nProf. Carey Rappaport (Advisor)\nProf. Octavia Camps\nProf. Sarah Ostadabbas \nAbstract:\nThis paper tackles the problem of uniquely identifying and tracking targets for the purposes of concealed threat detection in public spaces. Cameras\, computer vision techniques\, and deep neural networks have made the task of detecting and tracking people in videos almost trivial but provide no means for the detection of otherwise concealed threats a target may be carrying\, while millimeter-wave radars provide a means to perform accurate scanning for concealed objects on a target\, but do not provide enough information for tracking and unique identification of a target\, particularly one with a concealed threat or contraband. This paper proposes a method utilizing a video camera stream and millimeter-wave multi-beam radar fusion in order to identify people in a public space\, track them\, and identify the best beam in a multi-beam radar to refer to at any given point in time in order to obtain the best scan of a particular target from the millimeter-wave radar\, which will then enable an effective determination of a concealed threat. We focus on the computer vision aspects of this challenge\, implementing a tracker and an algorithm to look up the best beam in the radar to associate with a target at a point in time. This algorithm uses the properties of the camera\, such as the video resolution\, field of view of the camera\, internal parameters of the camera\, and elevation of the camera\, in order to perform an estimation of the distance of a person from the camera and perform a determination of the optimal beam to look at for a clear view of the target. This approach was optimized using an efficient spatial indexing lookup technique based on the R-tree data structure. The results from this paper show that this technique is robust\, accurate\, and versatile for a wide variety of scenarios and that the real-time tracking and association between targets and millimeter-wave beams can be performed accurately. We conclude that this technique is a fitting solution to the problem of camera and millimeter-wave multi-beam radar fusion in order to identify concealed threats on targets in public spaces.
URL:https://coe.northeastern.edu/event/david-femi-lamptey-ms-thesis-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240416T140000
DTEND;TZID=America/New_York:20240416T150000
DTSTAMP:20260424T231511
CREATED:20240416T134623Z
LAST-MODIFIED:20240416T134623Z
UID:43398-1713276000-1713279600@coe.northeastern.edu
SUMMARY:LeetCode Mock Interviews - A CommLab Workshop
DESCRIPTION:Join the CommLab any Tuesday from 2-3 PM for our weekly LeetCode Mock Interview Workshop via Zoom. This workshop is tailored towards programming jobs and prior coding knowledge is expected. Boost your LeetCode problem-solving confidence for interviews by building your speaking skills while solving programming problems.
URL:https://coe.northeastern.edu/event/leetcode-mock-interviews-a-commlab-workshop/2024-04-16/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240416T140000
DTEND;TZID=America/New_York:20240416T153000
DTSTAMP:20260424T231511
CREATED:20240410T205747Z
LAST-MODIFIED:20240410T205814Z
UID:43330-1713276000-1713281400@coe.northeastern.edu
SUMMARY:Nolan Pearce MS Thesis Defense
DESCRIPTION:Name:\nNolan Pearce \nTitle:\nDownlink Transmit Beamforming: Single-Carrier Acoustic Communication in a Noisy Environment \nDate:\n4/16/2024 \nTime:\n2:00:00 PM \nLocation:\nEXP-601A: \nCommittee Members:\n1. Prof. MIlica Stojanovic (Advisor)\n2. Prof. Stefano Basagni\n3. Prof. Josep Jornet\n4. Dr. Dimitrios Koutsonikolas \nAbstract:\nNoisy wireless acoustic channels produce intersymbol interference (ISI) from multipath propagation. This interference may be reduced by equalization techniques but require computationally intensive receiver algorithms. Typically\, beamforming methods are implemented at the uplink receiver to reduce complexity of equalization methods. However\, these methods require a receiver array. Using angle estimation of a transmitter\, beamforming techniques can be applied in downlink signal transmission to reduce equalizer complexity. This hypothesis is supported through simulation and applied to an open-air acoustic channel for significant performance improvement. Improving downlink signals through beamforming enables less complex user design suitable for single-carrier communications.
URL:https://coe.northeastern.edu/event/nolan-pearce-ms-thesis-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240416T150000
DTEND;TZID=America/New_York:20240416T163000
DTSTAMP:20260424T231511
CREATED:20240410T205310Z
LAST-MODIFIED:20240410T210425Z
UID:43338-1713279600-1713285000@coe.northeastern.edu
SUMMARY:Zihan Wei MS Thesis Defense
DESCRIPTION:Name:\nZihan Wei \nTitle:\nSpatial Correlation Based Broadband Acoustic Beamforming \nDate:\n4/16/2024 \nTime:\n3:00:00 PM \nLocation:\nEXP-601A \nCommittee Members:\n1. Prof. Milica Stojanovic (Advisor)\n2. Prof. Stefano Basagni\n3. Prof. Josep Jornet \nAbstract:\nThis thesis presents a spatial correlation based broadband acoustic beamforming approach\, addressing significant challenges pertains to acoustic communication channels\, such as time-varying multipath propagation and volatile phase fluctuations due to surface reflections. The proposed beamforming approach utilizes the synchronization preamble\, a high-resolution pseudo-random sequence\, to estimate the spatial correlation matrices for each frequency bin and decompose these spatial correlation matrices using singular vector decomposition. The singular vectors are then applied to each carrier of the orthogonal frequency division multiplexing signals as beamforming weights. The proposed algorithm is demonstrated using simulations and an in-air acoustic communications testbed. Performance metrics such as the mean squared error and bit error rate are presented\, demonstrating excellent performance improvement over the angle-based beamforming approach.
URL:https://coe.northeastern.edu/event/zihan-wei-ms-thesis-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240417T120000
DTEND;TZID=America/New_York:20240417T133000
DTSTAMP:20260424T231511
CREATED:20240410T205702Z
LAST-MODIFIED:20240410T210230Z
UID:43332-1713355200-1713360600@coe.northeastern.edu
SUMMARY:Raana Sabri Khiavi PhD Dissertation Defense
DESCRIPTION:Name:\nRaana Sabri Khiavi \nTitle:\nTheory and Design of Spatiotemporal Metasurfaces for Comprehensive Control of Light \nDate:\n4/17/2024 \nTime:\n12:00:00 PM \nLocation:\nIn person: Exp 311 \nCommittee Members:\nProf. Hossein Mosallaei (Advisor)\nProf. Josep Jornet (Co-advisor)\nProf. Charles Dimarzio\nProf. Siddhartha Ghosh \nAbstract:\nPhotonic metasurfaces are key components for manipulating almost all properties of light such as amplitude\, phase\, polarization\, wave vector\, pulse shape and orbital angular momentum at subwavelength scale. They are capable of sculpting the wavefront of the scattered light through imparting spatial or temporal modulation. Recently\, considerable efforts have been devoted to design active metasurfaces that enable real-time tuning and post-fabrication control of the optical response. Toward achieving this goal\, electro-optically tunable materials such as doped semiconductors\, MQWs\, and atomically thin sheets are incorporated into the building blocks of the geometrically-fixed metasurfaces. Despite the significant progress in this field\, there have been several limitations imparted to the optical response of such so-called quasi-static metasurfaces. Remarkably\, the strong resonant dispersion in such metasurfaces leads to narrow spectral and angular bandwidths. In addition\, the co-varying amplitude and phase response as well as the limited phase modulation give rise to undesired artefacts manifested on their output profiles. The slow response time to the external stimuli is another drawback that restricts the performance of the metasurfaces. Introducing time into the external stimulus of the metasurfaces\, as an additional degree of freedom\, offers a way out to surmount the obstacles facing the quasi-static metasurfaces. Modulation in time enables myriad of exotic space-time scattering phenomena\, where possibility to break the reciprocity and generation/manipulation of the sideband scattered signals offer the most appealing functionalities. In a space-time device\, the reciprocity constraint is lifted\, and time-reversal symmetry is broken. This effect can enable optical isolation and circulation\, while allowing for attaining full-duplex communication by rejecting the interference between up and down communication links. In addition\, sideband generation/manipulation provides access to the dispersionless modulation-induced phase shift with full 2pi span as well as a constant amplitude. The objective of this dissertation is to investigate the mechanisms for yielding reconfigurable plasmonic/all-dielectric metasurfaces in both space and time. Several realizations of quasi-static and time-modulated devices integrated with the electro-optical materials such as  ITO and InAs with the potential for high reflection and wide phase modulation are presented. It has been shown that time-modulated metasurfaces are superior to their quasi-static counterparts. Novel and unique applications of space-time photonic metasurfaces by spatiotemporal manipulation of light for all-angle\, broadband beam steering\, suppressing the undesired sidelobes\, high speed continuous beam scanning\, single sideband suppressed carrier modulation\, dispersionless dynamic wavefront engineering\, and magnetless power isolation at free space have been studied.
URL:https://coe.northeastern.edu/event/raana-sabri-khiavi-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240417T120000
DTEND;TZID=America/New_York:20240417T140000
DTSTAMP:20260424T231511
CREATED:20240410T205431Z
LAST-MODIFIED:20240410T210347Z
UID:43336-1713355200-1713362400@coe.northeastern.edu
SUMMARY:Bengisu Ozbay PhD Dissertation Defense
DESCRIPTION:Name:\nBengisu Ozbay \nTitle:\nFast Semi-Algebraic Clustering for Efficient System Identification and \nGeometric Scene Understanding \nDate:\n4/17/2024 \nTime:\n12:00:00 PM \nLocation:(EV) 102 \nCommittee Members:\nProf. Mario Sznaier (Advisor)\nProf. Octavia Camps\nProf. Taskin Padir\nProf. Rifat Sipahi \nAbstract:\nAs the demand for data-driven techniques in machine learning and computer vision continues to rise\, the reliance on unsupervised learning methods becomes increasingly prevalent. Piecewise linear or affine models offer versatile solutions across various domains\, including system identification and computer vision tasks. \nThis dissertation introduces an efficient methodology that relies solely on singular value decomposition of matrices\, maintaining a fixed size independent of the total number of data points. Remarkably\, this method only requires execution a number of times equivalent to the number of clusters. Through singular value decomposition (SVD) of the empirical moments matrix containing the data\, we demonstrate the feasibility of identifying the polynomials representing hyperplanes. Central to this approach is the utilization of polynomials and Christoffel functions\, facilitating the partitioning of data into distinct clusters\, each with its own set of parameters extracted using application-specific techniques. \nThe dissertation explores various challenges\, including semi-algebraic clustering\, identification of switching auto-regressive models with exogenous inputs (SARX)\, affine linear subspace clustering\, two-view motion segmentation\, identification of Wiener systems\, and switched nonlinear system identification using block-oriented models. The proposed semi-algebraic clustering framework identifies reliable subsets from data\, sequentially segments data using Christoffel polynomials\, and extends the approach beyond linear affine arrangements to address challenges involving quadratic surfaces in two-view motion segmentation and higher order algebraic varieties in switched-Wiener system identification. \n 
URL:https://coe.northeastern.edu/event/bengisu-ozbay-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20240418
DTEND;VALUE=DATE:20240420
DTSTAMP:20260424T231511
CREATED:20240321T142725Z
LAST-MODIFIED:20240321T142725Z
UID:42974-1713398400-1713571199@coe.northeastern.edu
SUMMARY:eMerge Americas - The Premier Global Tech Conference
+ Expo
DESCRIPTION:Join the COE Graduate Admissions team at the eMerge Americas Conference in Miami\, FL! We’ll be at the Expo on both April 18 & 19 with our biggest booth yet! Come say hi and ask your questions about graduate school at Northeastern\, including learning more about our newest campus in Miami!
URL:https://coe.northeastern.edu/event/emerge-americas-the-premier-global-tech-conference-expo/
ORGANIZER;CN="Graduate School of Engineering":MAILTO:coe-gradadmissions@northeastern.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240418T170000
DTEND;TZID=America/New_York:20240418T190000
DTSTAMP:20260424T231511
CREATED:20240328T140257Z
LAST-MODIFIED:20240408T135507Z
UID:42947-1713459600-1713466800@coe.northeastern.edu
SUMMARY:Northeastern National Academy of Inventors Spring 2024 Meeting
DESCRIPTION:Please join us for the National Academy of Inventors Northeastern Chapter Spring 2024 meeting.\n\nWith two dynamic speakers headlining the event\, attendees will gain invaluable insights into the latest advancements shaping various industries. \nOur first speaker\, Shawn P. Williams\, is a materials technologists specializing in Battery Thesis. He will unveil groundbreaking strategies aimed at revolutionizing technology and industry. Discover how the development of cutting-edge materials is driving enhanced performance\, durability\, and functionality across a multitude of applications\, from electronics and aerospace to healthcare and construction. \nNext\, dive into the world of 3D printing with the Fortify general counsel\, David R. Widom\, as he discusses the innovative DCM platform. With its patented technology\, this platform is transforming the additive manufacturing landscape by enhancing the performance of components. Learn how the seamless integration of aligned reinforcing fibers\, magnetics\, and DLP is paving the way for the creation of customized microstructures in high-resolution 3D printed composite parts.
URL:https://coe.northeastern.edu/event/northeastern-national-academy-of-inventors-spring-2024-meeting/
LOCATION:Raytheon Amphitheater (240 Egan)\, 360 Huntington Ave\, 240 Egan\, Boston\, MA\, 02115\, United States
ORGANIZER;CN="Center for Research Innovation":MAILTO:cri@northeastern.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240418T200000
DTEND;TZID=America/New_York:20240418T230000
DTSTAMP:20260424T231511
CREATED:20240408T135147Z
LAST-MODIFIED:20240412T173051Z
UID:43285-1713470400-1713481200@coe.northeastern.edu
SUMMARY:Late Night Breakfast
DESCRIPTION:Late Night Breakfast is back! Hosted by NUCOE in partnership with Northeastern Dining\, Northeastern Housing and Residential Life\, and NU Resident Student Association\, this one-night-only event is the perfect opportunity to take a break from studying and enjoy a crave-worthy and comforting meal. Complimentary tickets will be available for pick-up beginning April 10 (see details below) at Campus Crossroads in Curry Student Center—be sure to BRING YOUR HUSKY CARD! At that time\, you can select which location you’d prefer to visit for Late Night Breakfast\, while supplies last. Tickets are distributed ONE PER PERSON and are only available for a limited time. On the night of the event\, show your ticket and Husky Card to the cashier upon entry to your specified location. Entry is not permitted without your ticket AND Husky Card. \nLocation: United Table at International Village and The Eatery at Stetson East. You may only visit one location (The Eatery OR United Table) for the event. No swipe will be taken for entry. \nTicket Distribution (Campus Crossroads\, CSC):\nApril 10 — 11:00 am – 3:00 pm\nApril 11 — 5:00 pm – 7:00 pm\nApril 12\, — 11:00 am – 1:00 pm\n(Tickets are now sold out).
URL:https://coe.northeastern.edu/event/late-night-breakfast-2/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240419T140000
DTEND;TZID=America/New_York:20240419T170000
DTSTAMP:20260424T231511
CREATED:20240403T181541Z
LAST-MODIFIED:20240403T181541Z
UID:43186-1713535200-1713546000@coe.northeastern.edu
SUMMARY:Ruopeng Jia MS Thesis Defense
DESCRIPTION:Announcing:\nMS Thesis Defense \nName:\nRuopeng Jia \nTitle:\nEngineering Super-modes of Coupled Ring Resonator Arrays \nDate:\n4/19/2024 \nTime:\n2:00:00 PM \nCommittee Members:\n1) Sunil Mittal (Advisor)\n2) Prof. Yongmin Liu\n3) Prof. Ghosh Siddhartha \nAbstract:\nIn the past year of learning and research\, we have mastered the ability to analyze and modulate various resonant ring structures using Hamiltonian operators. Using genetic optimization algorithms\, we have achieved a precisely controllable four wave mixing process under simulated conditions.
URL:https://coe.northeastern.edu/event/ruopeng-jia-ms-thesis-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240422T090000
DTEND;TZID=America/New_York:20240422T094500
DTSTAMP:20260424T231511
CREATED:20240326T153230Z
LAST-MODIFIED:20240326T153230Z
UID:43047-1713776400-1713779100@coe.northeastern.edu
SUMMARY:GSE Spring 2024 Wonder Week: Bioengineering
DESCRIPTION:In this webinar you’ll learn about bioengineering and how this field strives to create an atmosphere of innovation and creativity that fosters excellence in instruction and research and provides a foundation for programs that drive forward the cutting edge of knowledge while establishing translational collaborations with clinical and industrial researchers.
URL:https://coe.northeastern.edu/event/gse-spring-2024-wonder-week-bioengineering/
ORGANIZER;CN="Graduate School of Engineering":MAILTO:coe-gradadmissions@northeastern.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240422T100000
DTEND;TZID=America/New_York:20240422T104500
DTSTAMP:20260424T231511
CREATED:20240326T153205Z
LAST-MODIFIED:20240326T153205Z
UID:43049-1713780000-1713782700@coe.northeastern.edu
SUMMARY:GSE Spring 2024 Wonder Week: Mechanical & Industrial Engineering
DESCRIPTION:In this webinar you’ll learn about the mechanical and industrial engineering graduate programs at Northeastern University. You’ll learn about our experiential graduate programs including interdisciplinary research opportunities and world-renown co-op.
URL:https://coe.northeastern.edu/event/gse-spring-2024-wonder-week-mechanical-industrial-engineering/
ORGANIZER;CN="Graduate School of Engineering":MAILTO:coe-gradadmissions@northeastern.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20240423
DTEND;VALUE=DATE:20240426
DTSTAMP:20260424T231511
CREATED:20240108T150008Z
LAST-MODIFIED:20240108T150008Z
UID:41155-1713830400-1714089599@coe.northeastern.edu
SUMMARY:ODSC East 2024
DESCRIPTION:Join COE Graduate Admissions at the Open Data Science Conference in Boston\, MA! Ask your questions about our graduate engineering programs across the U.S. and Canada during the Expo Hall on April 25th. We look forward to meeting you there!
URL:https://coe.northeastern.edu/event/odsc-east-2024/
ORGANIZER;CN="Graduate School of Engineering":MAILTO:coe-gradadmissions@northeastern.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240423T080000
DTEND;TZID=America/New_York:20240423T084500
DTSTAMP:20260424T231511
CREATED:20240326T153133Z
LAST-MODIFIED:20240326T153133Z
UID:43052-1713859200-1713861900@coe.northeastern.edu
SUMMARY:GSE Spring 2024 Wonder Week: Telecommunications Networks & Cyber Physical Systems
DESCRIPTION:In this webinar\, you will learn about the Master of Science in Telecommunication Networks and the Master of Science Cyber-Physical Systems graduate programs and how you’ll become a prepared professional to address complex\, and ever-evolving engineering challenges.
URL:https://coe.northeastern.edu/event/gse-spring-2024-wonder-week-telecommunications-networks-cyber-physical-systems/
ORGANIZER;CN="Graduate School of Engineering":MAILTO:coe-gradadmissions@northeastern.edu
END:VEVENT
END:VCALENDAR