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DTSTART;TZID=America/New_York:20240403T110000
DTEND;TZID=America/New_York:20240403T123000
DTSTAMP:20260516T184306
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:20240401T103000
DTEND;TZID=America/New_York:20240401T113000
DTSTAMP:20260516T184306
CREATED:20240319T140923Z
LAST-MODIFIED:20240319T140923Z
UID:42912-1711967400-1711971000@coe.northeastern.edu
SUMMARY:Reza Vafaee PhD Proposal Review
DESCRIPTION:Announcing:\nPhD Proposal Review \nName:\nReza Vafaee \nTitle:\nEfficient Algorithms for Sparse Sensor Scheduling in Large-Scale Dynamical Systems with Performance Guarantees \nDate:\n4/1/2024 \nTime:\n10:30:00 AM \nLocation: Zoom \nCommittee Members:\nProf. Milad Siami (Advisor)\nProf. Eduardo Sontag\nProf. Laurent Lessard\nProf. Alex Olshevsky (Boston University) \nAbstract:\nThis research proposal introduces innovative frameworks for sparse sensor scheduling in large-scale dynamical networks. The first framework addresses sensor scheduling in discrete-time linear time-invariant dynamical networks\, presenting a novel learning-based rounding method to convert weighted sensor schedules into sparse\, unweighted schedules while maintaining comparable observability performance. The second framework extends the approach to dynamically select sensors for linear time-varying systems\, utilizing an online sparse sensor scheduling framework with randomized algorithms to approximate fully-sensed systems with a constant average number of active sensors at each time step. Finally\, a myopic approach within a Kalman filtering framework is adopted in the third framework\, addressing non-submodular sensor scheduling in large-scale linear time-varying dynamics. A simple greedy algorithm is employed\, providing approximation bounds based on submodularity and curvature concepts. Simulation results validate the theoretical foundations and demonstrate the proposed approach’s superiority over existing methods.
URL:https://coe.northeastern.edu/event/reza-vafaee-phd-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240329T100000
DTEND;TZID=America/New_York:20240329T120000
DTSTAMP:20260516T184306
CREATED:20240319T141258Z
LAST-MODIFIED:20240319T141258Z
UID:42908-1711706400-1711713600@coe.northeastern.edu
SUMMARY:Matthew Wallace MS Thesis Defense
DESCRIPTION:Announcing:\nMS Thesis Defense \nName:\nMatthew Wallace \nTitle:\nModel Predictive Planning \nDate:\n3/29/2024 \nTime:\n10:00:00 AM \nLocation:\nRoom: HS 204.  Link: Teams \nCommittee Members:\nProf. Laurent Lessard (Advisor)\nProf. Michael Everett\nProf. Derya Aksaray \nAbstract:\nThis thesis presents Model Predictive Planning (MPP)\, a trajectory planner for low-agility vehicles such as a fixed-wing aircraft to navigate obstacle-laden environments.  MPP consists of (1) a multi-path planning procedure that identifies candidate paths\, (2) a raytracing procedure that generates linear constraints around these paths that enforce obstacle avoidance\, and (3) a convex quadratic program that finds a feasible trajectory within these constraint if one exists. Low-agility aircraft cannot track arbitrary paths\, so refining a given path into a trajectory that respects the vehicle’s limited maneuverability and avoids obstacles often leads to an infeasible optimization problem. The critical feature of MPP is that it efficiently considers multiple candidate paths during the refinement process\, thereby greatly increasing the chance of finding a feasible and trackable trajectory. I begin by presenting a background on path planning\, trajectory optimization\, and Model Predictive Control.  This is followed by a presentation of the MPP algorithm.  Finally\, I demonstrate the effectiveness of MPP on both a longitudinal and 3D aircraft model.
URL:https://coe.northeastern.edu/event/matthew-wallace-ms-thesis-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240328T110000
DTEND;TZID=America/New_York:20240328T120000
DTSTAMP:20260516T184306
CREATED:20240306T150314Z
LAST-MODIFIED:20240321T190531Z
UID:42671-1711623600-1711627200@coe.northeastern.edu
SUMMARY:Huan Wang PhD Dissertation Defense
DESCRIPTION:Announcing:\nPhD Dissertation Defense \nName:\nHuan Wang \nTitle:\nTowards Efficient Deep Learning in Computer Vision via Network Sparsity and Distillation \nDate:\n3/28/2024 \nTime:\n11:00:00 AM \nZoom \nCommittee Members:\nProf. Yun Fu (Advisor)\nProf. Octavia Camps\nProf. Zhiqiang Tao \nAbstract:\nAI\, 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 commonly consist of millions of parameters\, with foundation models extending to billions. The rapid expansion in model size introduces many challenges including training cost\, sluggish inference speed\, excessive energy consumption\, and negative environmental implications such as increased CO2 emissions. \nAddressing these challenges necessitates the adoption of efficient deep learning. The dissertation focuses on two overarching approaches\, network pruning and knowledge distillation\, to enhance the efficiency of deep learning models in the context of computer vision. Network pruning focuses on eliminating redundant parameters in a model while preserving the performance. Knowledge distillation aims to enhance the performance of the target model\, referred to as the “student\,” by leveraging guidance from a stronger model\, known as the “teacher”. This approach leads to performance improvements in the target model without reducing its size. \nIn this defense presentation\, I will start with the background and major challenges of leveraging these techniques to improve the efficiency of deep neural networks. Then\, I shall present the proposed solutions for various vision tasks\, including image classification\, single-image super-resolution\, novel view synthesis / neural rendering / NeRF / NeLF\, text-to-image generation / diffusion models\, and photorealistic head avatars. Extensive results and analyses will justify the efficacy of the proposed approaches\, demonstrating that pruning and distillation make a generic and complete framework for efficient deep learning in various domains. Finally\, a comprehensive summary (with takeaways) and outlook of the future work will conclude the presentation.
URL:https://coe.northeastern.edu/event/human-wang-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240321T120000
DTEND;TZID=America/New_York:20240321T133000
DTSTAMP:20260516T184306
CREATED:20240319T141109Z
LAST-MODIFIED:20240319T141109Z
UID:42910-1711022400-1711027800@coe.northeastern.edu
SUMMARY:Julian Gutierrez PhD Dissertation Defense
DESCRIPTION:Announcing:\nPhD Dissertation Defense \nName:\nJulian Gutierrez \nTitle:\nTowards Real-Time Safe Flight Paths for Urban Air Mobility \nDate:\n3/21/2024 \nTime:\n12:00:00 PM \nLocation: Zoom \nCommittee Members:\nProf. David Kaeli (Advisor)\nProf. Pau Closas\nDr. Evan Dill (NASA)\nDr. Natasha Neogi (NASA) \nAbstract:\nThe emergence and development of advanced technologies and vehicle types have created a growing demand for introducing new forms of flight operations. These new and increasingly complex operational paradigms\, such as Advanced and Urban Air Mobility (AAM/UAM)\, present regulatory authorities and the aviation community with the challenge of finding methods to integrate these emerging operations without significant additional risk to pedestrians and infrastructure. Predictive and autonomous risk mitigation capabilities become critical to meet this challenge. However\, urban environments experience effects that are computationally expensive to model\, limiting conventional aviation concepts\, policy\, and risk prediction tools from being effectively translated into this space. With the emergence of High-Performance Computing (HPC) ecosystems in the last two decades\, we can use these software and hardware capabilities to help bridge the gap between real-time predictive responses and modeling accuracy. \nIn this dissertation we first present a simulation framework to estimate the quality of Global Navigation Satellite System (GNSS) performance for autonomous aircraft in urban environments. We propose a new algorithm designed for HPC to accelerate modeling the characteristic effects of dense urban canyons on GNSS\, allowing the extension of established GNSS integrity techniques into urban navigation. Additionally\, we provide a thorough validation of the simulator\, which proves high-accuracy modeling when compared to sensors in the real world. Second\, we use this simulation framework to provide situational awareness when processing the raw output of a GNSS sensor. This effort focuses on multipath mitigation\, which reduces the error in the estimated position solution. Third\, we use this simulation framework as the input into a new 4D path-planning algorithm based on an adaptation of the Bellman-Ford algorithm. HPC techniques are employed to accelerate the algorithm to produce flight paths that minimize exposure to GNSS risks. We evaluate the computational cost of satellite availability fluctuations by prioritizing events when satellite availability changes as triggers for these updates.
URL:https://coe.northeastern.edu/event/julian-gutierrez-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20240103
DTEND;VALUE=DATE:20240108
DTSTAMP:20260516T184306
CREATED:20230905T203036Z
LAST-MODIFIED:20230905T203111Z
UID:38060-1704240000-1704671999@coe.northeastern.edu
SUMMARY:Computer Vision with Small Data (CV4Smalls): A Focus on Infants and Endangered Animals
DESCRIPTION:Prof. Sarah Ostadabbas is the general chair of the inaugural workshop on Computer Vision with Small Data (CV4Smalls): A Focus on Infants and Endangered Animals. This workshop is hosted as part of the 2024 Winter Conference on Applications of Computer Vision (WACV) and promises to be a platform for groundbreaking research and insightful discussions. The workshop will take place in person between January 3-7\, 2024 in Waikoloa\, Hawaii\, USA. \nResearchers and practitioners are invited to submit original research papers\, not exceeding 8 pages\, adhering to the WACV template https://lnkd.in/efsp_8qx. Workshop papers will be included in IEEE Xplore\, and will be indexed separately from the main conference papers. Submitted papers will undergo a rigorous peer-review double-blind process. For more information please contact: ostadabbas@ece.neu.edu \nImportant Dates:\n• Paper Submission Deadline: 11th October\, 2023\n• Notification of Acceptance: 9th November\, 2023\n• Camera-Ready Deadline: 19th November\, 2023\n• Workshop Date: 3-7th January\, 2024
URL:https://coe.northeastern.edu/event/computer-vision-with-small-data-cv4smalls-a-focus-on-infants-and-endangered-animals/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231220T140000
DTEND;TZID=America/New_York:20231220T150000
DTSTAMP:20260516T184306
CREATED:20231215T181627Z
LAST-MODIFIED:20231215T181627Z
UID:40911-1703080800-1703084400@coe.northeastern.edu
SUMMARY:Xiang Zhang PhD Proposal Review
DESCRIPTION:Title:Confidentiality and Privacy Preserving:  Intertwining Deep Learning and  Side-channel Analysis \nMeeting ID: 976 4324 8925 Passcode: 779251 \nCommittee Members:\nProf. Yunsi Fei (Advisor)\nProf. Adam Ding\nProf. Lili Su \nAbstract:\nIn the past decade\, deep learning-empowered technologies have significantly permeated our daily lives\, revolutionizing diverse application domains with superb performance.  In hardware security\, deep learning has been employed for power or electromagnetic side-channel analysis (SCA) and protection\, and the security of deep learning implementations starts gaining traction. \nThis dissertation delves into the intertwining deep learning techniques and side-channel analysis.  It addresses two critical questions: how to extend deep learning to other types of SCAs; what confidentiality and privacy vulnerabilities deep learning models have. \nOur research work first explores deep learning-assisted cache side-channel attacks and introduces innovative countermeasures grounded in the principles of adversarial samples against deep learning. We first design a novel high-frequency cache monitor\,  which runs concurrent to the victim execution and collects run-time timing traces\, while previous cache monitors are only able to collect timing samples. Such timing traces facilitate follow-on non-profiled Differential Deep Learning Analysis (DDLA) for secret retrieval. We also propose a novel countermeasure against the new DDLA\, leveraging the concept of adversarial examples\, which deliberately introduces obfuscation operations in the victim program so as to generate ‘adversarial’ timing traces and therefore circumvent the follow-on DDLA. \nThe second part of the dissertation addresses the vulnerability of deep neural network (DNN) implementations and presents novel methodologies for enhancing user privacy. It introduces a technique for extracting deep learning models through software-based power side channels. By manipulating model inputs and leveraging the on-chip Intel Running Average Power Limit (RAPL) sensors reporting\, the entire model parameters can be extracted when the model inference is executed on modern processors. To protect both the model confidentiality and the input privacy\, this dissertation proposes to obfuscate the model inputs while preserving the end-to-end functionality. It introduces an encoder to transform the inputs before feeding the DNN model\, and appends a decoder after the model outputs to recover the intended results. The approach\, compared to traditional encryption or masking techniques\, is more efficient and can effectively protect both user privacy and model confidentiality. \nThe overall goal of the dissertation is to further investigate the power of deep learning in SCA and countermeasure and safeguard secure DNN implementations.
URL:https://coe.northeastern.edu/event/xiang-zhang-phd-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231212T110000
DTEND;TZID=America/New_York:20231212T170000
DTSTAMP:20260516T184306
CREATED:20231208T145540Z
LAST-MODIFIED:20231208T145540Z
UID:40786-1702378800-1702400400@coe.northeastern.edu
SUMMARY:Deepak Prabhala MS Thesis Defense
DESCRIPTION:Title: “Smart Microwave Devices with Programable Printed Circuit Board (PPCB): Design with Liquid Crystal Elastomer Polymers in Transmission Lines and Circulators” \nCommittee Members:\n1) Professor Nian X. Sun (Advisor)\n2) Professor Marvin Onabajo\n3) Professor Yongmin Liu \nAbstract:\nThis study explores the innovative application of liquid crystal elastomer (LCE) polymers in the design and implementation of microwave transmission lines and circulators. Liquid crystal elastomers\, known for their unique combination of liquid crystalline and elastomeric properties\, offer a unique approach to developing flexible and tunable microwave devices. The research focuses on a thorough study of the electro-mechanical properties of LCEs to achieve novel functionalities in the design of transmission lines and circulators for microwave communication systems in HFSS simulations. The first part of the study delves into the characterization of the dielectric and mechanical properties of the chosen LCE polymer. Subsequently\, the design and fabrication of a flexible and tunable transmission line using LCE are discussed. The LCE-based transmission line aims to measure the insertion loss and return loss with different widths\, lengths\, and thicknesses of the LCE polymer. The study investigates the impact of temperature on the transmission line’s performance\, offering insights into potential applications in reconfigurable microwave systems. The second phase of the research explores the utilization of LCE in the development of a microwave circulator\, a vital component in microwave communication networks. The circulator design incorporates the unique properties of LCE by using a stepped dielectric variation approach for broadband isolation. This innovation holds promise for enhancing the efficiency and adaptability of microwave systems in communication and radar applications. The findings of this research contribute to offering a pathway for integrating liquid crystal elastomers into flexible and reconfigurable microwave devices. This thesis aims to advance the understanding of smart microwave devices and inspire further exploration into the application of liquid crystal elastomer polymers in cutting-edge technologies.
URL:https://coe.northeastern.edu/event/deepak-prabhala-ms-thesis-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231212T090000
DTEND;TZID=America/New_York:20231212T100000
DTSTAMP:20260516T184306
CREATED:20231208T145140Z
LAST-MODIFIED:20231208T145212Z
UID:40790-1702371600-1702375200@coe.northeastern.edu
SUMMARY:Durga Suresh PhD Proposal Review
DESCRIPTION:Title: Network Security Management and Threat Mitigation in the Open Cloud\n \nCommittee Members:\nProf. Miriam Leeser (Advisor)\nProf. Michael Zink\nProf. Xiaolin Xu \nAbstract:\nCloud computing and advanced cyberinfrastructures are increasingly vital to the functioning of Internet systems. Every day\, more devices are added to the cloud\, to provide greater resource utilization\, availability\, and scalability. Due to the expanding reliance on cloud computing\, securing the cloud is paramount. Tackling the issue of securing the cloud is crucial not only for preserving the functionality and reliability of cloud-based systems but also for protecting the critical data and services that depend on these platforms. \nCloud computing models include public clouds\, private clouds\, community clouds\, and hybrid clouds. Private\, community\, and hybrid clouds provide security\, but with an important trade-off; namely\, user access restriction in the cloud. The proposed research uses the Open Cloud Testbed (OCT) which is part of the National Science Foundation’s (NSF) Computer and Information Systems Engineering(CISE) Community Research Infrastructure(CRI) program. OCT is an example of a public cloud that allows users two things: 1) an isolated set of nodes to perform experiments with bare metal access\, which can potentially lead to security issues\, and 2) the ability to test out the solutions for both using the cloud and adding security to it. The proposed research aims to target a system like the OCT\, specifically targeting a public cloud environment. \nThis system will be designed to allow access to the switch\, enabling control and management of traffic within the cloud network. This research aims to mitigate network security threats in the public cloud network. The aim of this research is multifold. First\, we identify and classify the behavior of users in the cloud. We then provide an approach to creating a network security management policy that will deal with 1)detecting network intruders that scan the cloud network and remove their access to the network\, and  2) managing heavy hitters that can cause Denial of Service (DOS) and Distributed Denial of Service (DDOS) attacks in the cloud network by using the heavy hitter detection system and prevent them from putting more traffic on the network. Both network intruder detection and heavy hitter management systems use Access Control Lists (ACL)as a means to prevent the user from putting traffic on the cloud network. Lastly\, we perform experiments to handle these threats and measure the success of the experimental setup concerning network attacks. The proposed approach will ensure network security by creating a framework for network security management policy to minimize threats in the cloud network and other resources directly attached to the network. The proposed research aims to enhance cybersecurity by employing network intruder detection techniques to identify potential threats\, implementing heavy hitter management to mitigate threats effectively\, and developing and enforcing a network security management policy to prevent future threats.
URL:https://coe.northeastern.edu/event/durga-suresh-phd-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231208T120000
DTEND;TZID=America/New_York:20231208T123000
DTSTAMP:20260516T184306
CREATED:20231208T145358Z
LAST-MODIFIED:20231208T145358Z
UID:40788-1702036800-1702038600@coe.northeastern.edu
SUMMARY:Ate Darabi PhD Proposal Review
DESCRIPTION:Title:\nComplex Delayed Networks and Their Application in Epidemic Analysis: Modeling\, Analysis\, and Strategic Management \nCommittee Members:\nProf. Milad Siami (Advisor)\nProf. Bahram Shafai\nProf. Rozhin Hajian \nAbstract:\nIn the face of crowd-related disasters like pandemics and mass attacks\, the complex dynamics of human interactions demand comprehensive modeling approaches. This proposal adopts a network-based perspective\, leveraging the delayed Susceptible-Infected-Susceptible (SIS) model for epidemics and the Predator-Swarm-Guide (PSG) model for crowd movement\, to gain insights into the dynamics of these critical situations. \nIn epidemic networks\, time delays and uncertainties can significantly change the epidemic behavior and result in successive echoing waves of the spread between various population clusters. We examine these effects on linear SIS dynamics\, evaluating network stability and performance loss. We prove that network performance loss is correlated with the structure of the underlying graph\, intrinsic time delays\, epidemic characteristics\, and external shocks. This performance measure is then used to develop an optimal traffic restriction algorithm for network performance enhancement\, resulting in reduced infection in the metapopulation.   An epidemic-based centrality index is also proposed to evaluate the impact of every subpopulation on network performance\, and its asymptotic behavior is investigated. This index converges to local or eigenvector centralities under specific parameters. Moreover\, given that epidemic-based centrality depends on the epidemic properties of the disease\, it may yield distinct node rankings as the disease characteristics slowly change over time or as different types of infections spread. This unique characteristic of epidemic-based centrality enables it to adjust to various epidemic features. The derived centrality index is then adopted to improve the network robustness against external shocks on the epidemic network. \nThe PSG model addresses mass attack scenarios\, considering individuals’ efforts to evade adversaries and seek guidance. Environmental factors like impermeable walls and psychological elements are incorporated into this model. The preliminary results highlight the role of coordinated cooperation in minimizing casualties. The objective is to reduce casualties through a hybrid motion optimization approach for individuals and the guiding agent.
URL:https://coe.northeastern.edu/event/ate-darabi-phd-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231207T140000
DTEND;TZID=America/New_York:20231207T150000
DTSTAMP:20260516T184306
CREATED:20231204T190346Z
LAST-MODIFIED:20231204T190346Z
UID:40688-1701957600-1701961200@coe.northeastern.edu
SUMMARY:Yisi Liu MS Thesis Defense
DESCRIPTION:Title: Experimental research on the Nonlinear Magnetoelectric Effect of the VLF ME Antennas \nCommittee Members:\nProf. Nian Sun (Advisor)\nProf. Yongmin Liu\nProf. Xufeng Zhang \nAbstract:\nMagnetoelectric (ME) coupling effects in ferromagnetic and piezoelectric composites involve the control of electric polarization (P) by applying a magnetic field (H) (direct ME effect)\, or the manipulation of magnetization (M) through an electric field (E) (converse ME effect) . These effects are facilitated by the mechanical deformation in the ferroic phases resulting from the combination of magnetostriction and piezoelectricity. In single-phase materials\, the breakthrough in achieving large ME coefficients has further advanced the development of ME materials and devices. Consequently\, numerous multifunctional ME devices\, such as mechanical antennas\, magnetic sensors\, tunable inductors\, and filters\, have been developed. This thesis has provides a summary and categorization of these devices based on their physical mechanism and type of ME effects. The inclusion of mechanical ME antennas based on piezoelectric/magnetostrictive heterostructures with acoustic actuation reflects the significant interest in this topic. Notably\, a maximum communication distance of 120 m for a very low frequency (VLF) communication system has been achieved using a pair of mechanical ME antennas. Subsequently\, we will focus on introducing and reviewing the materials and devices related to the ME effect\, as well as the application of ME mechanical antennas in very low frequency (VLF) communication systems. \nIn addition to that\, we developed a transmitter with a Metglas/PZT/Metglas structure antenna. Our study focuses on investigating the transmission effects of this antenna when employing direct antenna modulation techniques to enhance data transmission. Through our research\, we have introduced a novel modulation method by modulating the antenna. We observed that this modulation method produces a more stable and stronger signal. \n 
URL:https://coe.northeastern.edu/event/yisi-liu-ms-thesis-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231207T100000
DTEND;TZID=America/New_York:20231207T110000
DTSTAMP:20260516T184306
CREATED:20231204T185514Z
LAST-MODIFIED:20231204T185514Z
UID:40692-1701943200-1701946800@coe.northeastern.edu
SUMMARY:Mauro Belgiovine PhD Proposal Review
DESCRIPTION:Title: Wireless Intelligence: A Comprehensive Exploration of AI-Driven Solutions in Channel Estimation\, Beam Refinement\, and Protocol Classification for Next Generation Networks \nCommittee Members:\nProf. Kaushik Chowdhury (advisor)\nProf. Stratis Ioannidis\nDr. Chris Dick \nAbstract:\nhis thesis explores the transformative impact of artificial intelligence (AI) on wireless systems through model-driven simulations and real-world datasets\, with a focus on enhancing both local and cellular wireless networks through the deployment of highly customized deep learning solutions that target specific bottlenecks affecting traditional signal processing based communication. \nThe research delves into three key areas that address critical challenges in the current wireless landscape. The first focal point of the investigation involves channel estimation using deep learning techniques to denoise pilots and expedite the accurate estimation of Channel State Information (CSI). By leveraging deep learning methodologies\, the proposed solution aims to enhance the reliability and computation for MIMO and massive MIMO channel estimation\, thereby contributing to improved communication efficiency and reduced errors. The second major topic encompasses the application of reinforcement learning for 5G New Radio (NR) millimeter-wave (mmWave) beam refinement. The study aims to develop a Deep Reinforcement Learning algorithm capable of adjusting beamsteering angles\, starting from a coarse beam scanning procedure and further refining them for higher transmission efficiency. This innovation is expected to substantially decrease traffic overhead while simultaneously enhancing beam steering precision\, thus optimizing the performance of mmWave communication. The third and final area of focus introduces a transformer-based WiFi multi-protocol classifier\, strategically deployed on a DeepWave Air-T edge device\, which is equipped with Module on Chip (MoC) low power CPU-GPU and programmable Software Defined Radio (SDR). This classifier outperforms existing modulation classification models and legacy methods under lower SNR conditions\, leveraging TensorRT’s model compression capabilities to efficiently process extended sequences of raw IQ samples\, ensuring high performance at a low computational cost. The proposed solution addresses the growing demand for efficient and adaptable wireless communication systems\, paving the way for advancements in edge-based processing and intelligent protocol classification. \nThis work seeks to contribute significantly to the ongoing AI revolution in wireless systems by addressing crucial issues in channel estimation\, beam refinement\, and protocol classification. The outcomes of this research hold the potential to redefine the landscape of wireless communication\, offering enhanced performance\, reduced overhead\, and increased adaptability in both local and cellular networks.
URL:https://coe.northeastern.edu/event/mauro-belgiovine-phd-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231206T150000
DTEND;TZID=America/New_York:20231206T170000
DTSTAMP:20260516T184306
CREATED:20231204T185947Z
LAST-MODIFIED:20231204T185947Z
UID:40690-1701874800-1701882000@coe.northeastern.edu
SUMMARY:Suyash Pradhan MS Thesis Defense
DESCRIPTION:Title: COPILOT: Cooperative Perception using Lidar for Handoffs between Road Side Units \nCommittee Members:\nProf. Kaushik Chowdhury (Advisor)\nProf. Stratis Ioannidis\nProf. Jennifer Dy \nAbstract:\nThis thesis presents COPILOT\, a ML-based approach that allows vehicles requiring ubiquitous high bandwidth connectivity to identify the most suitable road side units (RSUs) through proactive handoffs. By cooperatively exchanging the data obtained from local 3D Lidar point clouds within adjacent vehicles and with coarse knowledge of their relative positions\, COPILOT identifies transient blockages to all candidate RSUs along the path under study. Such cooperative perception is critical for choosing RSUs with highly directional links required for mmWave bands\, which majorly degrade in the absence of LOS. COPILOT proposes three modules that operate in an inter-connected manner: (i) As an alternative to sending raw Lidar point clouds\, it extracts and transmits low-dimensional intermediate features to lower the overhead of inter-vehicle messaging; (ii) It utilizes an attention-mechanism to place greater emphasis on data collected from specific vehicles\, as opposed to nearest neighbor and distance-based selection schemes\, and (iii) it experimentally validates the outcomes using an outdoor testbed composed of an autonomous car and Talon AD7200 60GHz routers emulating the RSUs\, accompanied by the public release of the datasets. Results reveal COPILOT yields upto 69.8% and 20.42% improvement in latency and throughput compared to traditional reactive handoffs for mmWave networks\, respectively
URL:https://coe.northeastern.edu/event/suyash-pradhan-ms-thesis-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231204T103000
DTEND;TZID=America/New_York:20231204T113000
DTSTAMP:20260516T184306
CREATED:20231127T163905Z
LAST-MODIFIED:20231127T163905Z
UID:40523-1701685800-1701689400@coe.northeastern.edu
SUMMARY:Cheng Gongye PhD Dissertation Defense
DESCRIPTION:Title:\nHardware Security Vulnerabilities in Deep Neural Networks and Mitigations \nDate:\n12/4/2023 \nTime:\n10:30:00 AM \nCommittee Members:\nProf. Yunsi Fei (Advisor)\nProf. Aidong Ding\nProf. Xue Lin\nProf. Xiaolin Xu \nAbstract:\nIn the past decade\, Deep Neural Networks (DNNs) have become pivotal in numerous fields\, including security-sensitive autonomous driving and privacy-critical medical diagnosis. This Ph.D. dissertation delves into the hardware security of DNNs\, discovering their vulnerabilities to fault and side-channel attacks and exploring novel countermeasures essential for their safe deployment in critical applications. \nFault attacks disrupt computation or inject faults into parameters\, compromising the integrity of targeted applications. This dissertation demonstrates a power-glitching fault injection attack on FPGA-based DNN accelerators\, common in cloud environments\, which exploits vulnerabilities in the shared power distribution network and results in model misclassification. In response to these threats\, we introduce a novel\, lightweight defense mechanism to protect DNN parameters from adversarial bit-flip attacks. The proposed framework incorporates a dynamic channel-shuffling obfuscation scheme coupled with a logits-based model integrity monitor. The approach effectively safeguards various DNN models against bit-flip attacks\, without necessitating retraining or structural changes to the models. Furthermore\, our research expands the scope of fault analysis beyond just the parameters of DNN models. We thoroughly examine the entire implementation of commercial products\, defying the prevailing assumption that quantized DNNs are inherently resistant to bit-flips. \nSide-channel attacks exploit information leakage of system implementations\, such as power consumption and electromagnetic emanations\, to reveal system secrets and therefore compromise confidentiality. This dissertation makes significant contributions to side-channel assisted model extraction of DNNs. We present a floating-point timing side-channel attack on x86 CPUs that reverse-engineers DNN model parameters in software implementations. For hardware accelerators\, we target the state-of-the-art AMD-Xilinx deep-learning processor unit (DPU)\, a reconfigurable engine dedicated to convolutional neural networks (CNNs) and representing the most complex commercial FPGA accelerator with encrypted IPs. Our work demonstrates that electromagnetic analysis can be leveraged to recover the data flow and scheduling of the DNN accelerators\, facilitating follow-on architecture and parameter extraction attacks. To mitigate EM side-channel model extraction attacks\, we introduce a novel defense mechanism that devises a random importance-aware activation mask on input pixels to disrupt the operation alignment on EM traces\, with minimal performance and efficiency impacts. \nOverall\, this dissertation significantly deepens the understanding of hardware security of DNN models. It makes important contributions in discovering novel and critical vulnerabilities of DNN inference pertaining to system implementations\, and proposing effective and practical solutions for securing DNNs in mission-critical environments. The research work marks a substantial step forward in the development of resilient and secure AI systems.
URL:https://coe.northeastern.edu/event/cheng-gongye-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231129T163000
DTEND;TZID=America/New_York:20231129T170000
DTSTAMP:20260516T184306
CREATED:20231127T164136Z
LAST-MODIFIED:20231127T164136Z
UID:40525-1701275400-1701277200@coe.northeastern.edu
SUMMARY:Aria Masoomi PhD Proposal Review
DESCRIPTION:Title:\nMaking Deep Neural Network Transparent \nDate:\n11/29/2023 \nTime:\n4:30:00 PM \nCommittee Members:-\nProf. Jennifer Dy\nProf. Eduardo Sontag\nProf. Mario Sznaier\nProf. Peter Castaldi \nAbstract:\nAs machine learning algorithms are deployed ubiquitously to a variety of domains\, it is imperative to make these often black-box models transparent. The ability to interpret and comprehend the reasoning behind machine learning models plays a pivotal role in increasing user trust. It not only offers insights into how a model functions but also opens avenues for model enhancements. \nThis research delves into the realm of interpretability\, focusing on the dichotomy between ‘intrinsic’ and ‘post hoc’ interpretability. Intrinsic interpretability involves constraining the complexity of the machine learning model itself\, resulting in models inherently interpretable due to their simplicity\, such as decision trees or sparse linear regression. On the other hand\, post hoc interpretability employs techniques that assess the model’s behavior after training\, offering insights into the model’s outcomes. Examples of post hoc techniques include permutation feature importance and the Shapley value method for feature importance. \nThe core contribution of this Thesis proposal lies in the development of novel methods to enhance both intrinsic and post hoc interpretability. These methods aim to advance the field by offering new perspectives on understanding machine learning models\, thereby contributing to the ongoing discourse on model transparency and user trust.
URL:https://coe.northeastern.edu/event/aria-masoomi-phd-proposal-review-2/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231129T150000
DTEND;TZID=America/New_York:20231129T160000
DTSTAMP:20260516T184306
CREATED:20231127T163756Z
LAST-MODIFIED:20231127T163756Z
UID:40521-1701270000-1701273600@coe.northeastern.edu
SUMMARY:Aria Masoomi PhD Proposal Review
DESCRIPTION:Title:\nMaking Deep Neural Network Transparent \nDate:\n11/29/2023 \nTime:\n3:00:00 pm \nCommittee Members:\nProf. Jennifer Dy (Advisor)\nProf. Mario Sznaier\nProf. Eduardo Sontag\nProf. Peter Castaldi \nAbstract:\nAs machine learning algorithms are deployed ubiquitously to a variety of domains\, it is imperative to make these often black-box models transparent.\nThe ability to interpret and comprehend the reasoning behind machine learning models plays a pivotal role in increasing  user trust. It not only offers insights into how a model functions but also opens avenues for model enhancements. \nThis research delves into the realm of interpretability\, focusing on the dichotomy between ‘intrinsic’ and ‘post hoc’ interpretability. Intrinsic interpretability involves constraining the complexity of the machine learning model itself\, resulting in models inherently interpretable due to their simplicity\, such as decision trees or sparse linear regression. On the other hand\, post hoc interpretability employs techniques that assess the model’s behavior after training\, offering insights into the model’s outcomes. Examples of post hoc techniques include permutation feature importance and the Shapley value method for feature importance. \nThe core contribution of this Thesis proposal lies in the development of novel methods to enhance both intrinsic and post hoc interpretability. These methods aim to advance the field by offering new perspectives on understanding machine learning models\, thereby contributing to the ongoing discourse on model transparency and user trust.
URL:https://coe.northeastern.edu/event/aria-masoomi-phd-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231127T080000
DTEND;TZID=America/New_York:20231127T170000
DTSTAMP:20260516T184306
CREATED:20231127T163640Z
LAST-MODIFIED:20231127T163640Z
UID:40519-1701072000-1701104400@coe.northeastern.edu
SUMMARY:Bruno Souto Maior Muniz Morais PhD Dissertation Defense
DESCRIPTION:Title:\nEnabling Domain Platform Design for Streaming Applications: A Holistic Approach \nCommittee Members:\nGunar Schirner (Advisor)\nProf. David Kaeli\nProf. Hamed Tabkhi (UNCC) \nTime:\n10:00:00 AM \nLocation: ISEC 601 \nAbstract:\nIn recent years\, more demanding streaming applications make striking a balance between high compute performance and efficiency paramount in platforms designs for edge computing. In addition\, designing a platform that is optimized for a single application is costly due to non-recurring engineering (NRE) costs. In contrast\, multiple applications can be grouped in domains\, e.g. computer vision\, software-defined radio. Leveraging shared characteristics of similar applications within a domain\, e.g. structural composition/computation patterns\, a single domain platform that caters to these similarities and accelerates applications can be generated\, thus benefiting multiple applications at once and dramatically improving NRE and time-to-market (TTM). \nThis dissertation introduces methodologies atvarious abstraction levels to enable streamlined domain platform design for streaming applications. Thrust 1 introduces high level DSE methods based on integer linear programming (ILP)\, Tile-based Synchronization Aware ILP (TSAR-ILP). Initially\, single-application platform allocations are considered using TSAR-ILP. While TSAR-ILP only focuses on applications in isolation\, its formulation lays the foundations for DmTSAR-ILP\, a method that performs domain DSE with multiple applications\, obtaining an optimal unified platform allocation that and achieving an increase of 22.5% in throughput\, while being 70x faster when compared to previous methods (MG-DmDSE). However\, DmTSAR-ILP aims to aggregate all applications fairly. This presents a challenge when the designer wishes to focus on a subset of applications. To enable ultimate flexibility in a product-oriented setting\, modeled after a market analysis process\, this dissertation introduces ProdDSE. ProdDSE enables application prioritization while also introducing concurrent application modeling and a multi-objective optimization (area\, performance) approach. This enables up to a 3.4x boost in performance depending on use case\, while also providing gains in DSE runtime (4.3x faster). \nThrust 2 introduces Sedona\, a domain-specific language (DSL) and exploration enviroment that captures parametric dataflow application descriptions with language features dedicated to streaming applications. A design identified by Thrust 1 can be further refined using the tools in Thrust 2\, by capturing the connectivity of a design using Sedona. Then\, automatic wiring is performed for target outputs such as timing-aware simulations or RTL-level code\, enabling structural manipulation at a high-level description without the burden of low-level manual integration. \nFinally\, to better guide the high-level decisions performed in Thrust 1 and further exploration/integration in Thrust 2\, Thrust 3 considers the implications of HWACC topology choices in an HWACC-rich SoC. The ACTAR flow is introduced to explore different topologies in a RISC-V based SoC and the side-effects of topology and memory sizing choices on the system-wide performance and synchronization burdens due computation offloading to HWACCs. This produces valuable and actionable insights for designers to make informed choices on system-level compositions depending on application communication and computation demands.
URL:https://coe.northeastern.edu/event/bruno-souto-maior-muniz-morais-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231117T083000
DTEND;TZID=America/New_York:20231117T093000
DTSTAMP:20260516T184306
CREATED:20231020T143903Z
LAST-MODIFIED:20231020T143903Z
UID:39994-1700209800-1700213400@coe.northeastern.edu
SUMMARY:Mahshid Asri PhD Dissertation Defense
DESCRIPTION:Title:\nDevelopment of Anomaly Detection and Characterization Algorithms Using Wideband Radar Image Processing for Security Applications \nDate:\n11/17/2023 \nTime:\n8:30:00 AM \nLocation: 302 Stearns \nCommittee Members:\nProf. Carey Rappaport (Advisor)\nProf. Charles DiMarzio\nProf. Edwin Marengo \nAbstract:\nDetection and characterization of suspicious body-worn objects is necessary for safe and effective personnel screening. In airports\, developing a precise system that can distinguish threats and explosives from objects like money belt can reduce the pat-down significantly while maintaining effective security. This dissertation proposes two main algorithms which are developed for different millimeter-wave radar systems. The first project is a material characterization algorithm designed for a 30 GHz wideband multi bi-static radar system used for passenger screening in airports. The proposed algorithm can automatically distinguish lossless materials from lossy ones and calculate their thickness and permittivities. Starting from the radar reconstructed image showing a cross-section of the body\, we extract the nominal body contour using Fourier series\, separate body and object responses\, categorize the object as lossy or lossless based on the depression and protrusion of the body contour\, and finally predict possible values for the object’s permittivity and thickness. Our resulting classification is good\, implying fewer nuisance alarms at check points. We have also trained a deep learning model for pixel-wise localization of body worn anomalies. The second project is a metal detection algorithm developed to monitor pedestrians walking along a sidewalk for large\, concealed metallic objects. Finite Difference Frequency Domain and SAR algorithms are used to simulate the images produced by this 6 GHz wideband radar system. A deep learning model has then been used to predict a pixel level mask for the body and anomaly based on the inputted radar image.
URL:https://coe.northeastern.edu/event/mahshid-asri-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231024T130000
DTEND;TZID=America/New_York:20231024T140000
DTSTAMP:20260516T184306
CREATED:20231020T144256Z
LAST-MODIFIED:20231020T144857Z
UID:39990-1698152400-1698156000@coe.northeastern.edu
SUMMARY:Baolin Li PhD Proposal Review
DESCRIPTION:Title:\nMaking Machine Learning on HPC Systems Cost-Effective and Carbon-Friendly \nDate:\n10/24/2023 \nTime:\n1:00:00 PM \nCommittee Members:\nProf. Devesh Tiwari (Advisor)\nProf. Ningfang Mi\nDr. Vijay Gadepally \nAbstract:\nThe end users want the machine learning (ML) training/inference services to be lightning-fast. However\, the cost\, incurred by service providers\, to support these lighting-fast ML services is often prohibitively high. Large-scale HPC and data centers are struggling to keep their cost low as they provide faster ML services\, while the excessive demand for these services is already negatively impacting our environment due to the large carbon footprint of ML services. Therefore\, this dissertation focuses on better understanding the complex trade-off among performance\, cost\, and environmental footprint of ML models. \nThis dissertation asks three simple questions: (1) Is slower hardware always worse? (2) Is more expensive hardware always better? (3) Should we always strive to design and train ML models with the highest possible accuracy? As this dissertation reveals\, the answers to these questions are more complex than what the conventional wisdom suggests. In fact\, simplistic answers — based on first-order intuitions — can lead to missed opportunities in terms of performance efficiency\, cost-effectiveness\, and carbon footprint. \nIn this dissertation\, we build multiple novel frameworks to demonstrate that mixing slower-and-cheaper hardware with faster-and-expensive hardware can unlock much higher performance- and cost-effectiveness than using only faster-and-expensive hardware configurations. But\, unlocking this potential requires a careful design — a design that carefully exploits the diversity in ML inference workload characteristics and adapts to varying ML inference request loads. Next\, this dissertation demonstrates that while the highest-possible-accuracy ML models are desirable\, using such models can have a severe negative environmental impact. To mitigate this challenge\, this dissertation builds an experimental framework to reduce the carbon footprint of ML inference services. The key idea\, behind this framework\, is to mix the lower-quality ML models with higher-quality ML models intelligently and share the hardware resources during inference query execution to reduce the excessive carbon footprint of high-quality ML model inference\, esp. during the periods when a data center’s energy source has high carbon intensity. The extensive experimental evaluation confirms that significant carbon emission reductions can be achieved with transient\, very minimal\, and configurable loss in accuracy. \nAs we make rapid advances in the era of large-language models (LLMs) and foundation models\, the novel methods and open-source tools presented in this dissertation will enable us to build ML services faster but cheaper and in an environmentally sustainable manner
URL:https://coe.northeastern.edu/event/baolin-li-phd-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230919T100000
DTEND;TZID=America/New_York:20230919T113000
DTSTAMP:20260516T184306
CREATED:20230901T134555Z
LAST-MODIFIED:20230901T134555Z
UID:38048-1695117600-1695123000@coe.northeastern.edu
SUMMARY:Zhengnan Li PhD Proposal
DESCRIPTION:Title:\nMulti-user Communications \nDate:\n9/19/2023 \nTime:\n10:00:00 AM \nLocation:\nISEC 432 \nCommittee Members:\nProf. Milica Stojanovic (Advisor)\nDr. Xiaowen Wang (Apple)\nProf. Tommaso Melodia \nAbstract:\nExtensive research has been conducted with respect to underwater acoustic communications and networking\, owing to their profound importance in various applications\, such as fish farming and the oil-and-gas industry. Acoustic networks involve scenarios where multiple users necessitate transmitting data to a central base station. Techniques such as time-division or code-division multiple access are commonly employed in such networks. However\, these techniques entail a trade-off: each user’s transmission rate remains confined to a fraction of the overall available resources. Consequently\, the endeavor to accommodate an increased number of users within the usable resources results in a diminished data rate for each user. Conversely\, if the goal is to maintain a consistent per-user data rate\, the acquisition of additional bandwidth becomes imperative. Yet\, the feasibility of this approach is challenged by the inherent limitations of available bandwidth in underwater acoustic systems. \nIn this proposal\, I will present preliminary findings that involve the utilization of code and space division multiple access systems in conjunction with orthogonal frequency division multiplexing (OFDM)\, which is a key enabler for the current and future generations of wireless systems. Additionally\, the proposal delves into the prospect of establishing an underwater acoustic channel repository—an effort designed to emulate underwater acoustic channels\, thereby alleviating the necessity for extensive real-world underwater experimentation. This proposal also includes several recent long-distance underwater experiments carried out in Japan\, examining various ideas regarding the frequency offset compensation problem in OFDM systems. Furthermore\, beyond the outcomes in underwater acoustic communication\, this proposal encompasses a series of explorations involving micro electromechanical systems (MEMS) and the utilization of terahertz frequencies.
URL:https://coe.northeastern.edu/event/zhengnan-li-phd-proposal/
LOCATION:432 ISEC\, 360 Huntington Ave\, Boston\, MA\, 02115\, United States
GEO:42.3396156;-71.0886534
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=432 ISEC 360 Huntington Ave Boston MA 02115 United States;X-APPLE-RADIUS=500;X-TITLE=360 Huntington Ave:geo:-71.0886534,42.3396156
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230918T083000
DTEND;TZID=America/New_York:20230918T150000
DTSTAMP:20260516T184306
CREATED:20230915T172337Z
LAST-MODIFIED:20230915T172337Z
UID:38782-1695025800-1695049200@coe.northeastern.edu
SUMMARY:Technical Exchange Program between NU and DFG Delegation
DESCRIPTION:8:30-8:50 am: Lab tour of the NU Cleanroom\, labs\, etc. (Meet at the entrance of Egan Building\, 120 Forsyth Street\, Boston\, MA 02115\, Led by Nian Sun)\n8:50-9am Move to McLeod Suites\, Curry Student Center\, Northeastern University\n9:00-9:15: Opening remarks from NU and DFG (VPRD Kim Holloway\, Senator Professor Marion Merklein)\n9:15-10:30: Presentations from NU (9 speakers with 8 minutes each)\n\nRavinder Dahiya\, Electrical and Computer Engineering\nMatteo Rinaldi\, Electrical and Computer Engineering\nYongmin Liu\, Electrical and Computer Engineering\, Mechanical and Industrial Engineering\nXufeng Zhang\, Electrical and Computer Engineering\nBen Davaji\, Electrical and Computer Engineering\nSwastik Kar\, Physics Department\nPaul Stevenson\, Physics Department\nRuobing Bai\, Mechanical and Industrial Engineering\nHongli Zhu\, Mechanical and Industrial Engineering\n\n\n10:30-10:45: Coffee break\n10:45-11:45: Presentations from NU and DFG Delegation (7 speakers with 8 minutes each)\n\nSrinivas Tadigadapa\, Electrical and Computer Engineering\nSrirupa Chakraborty\, Chemical Engineering\nNian Sun\, Electrical and Computer Engineering\nArne Berger\, University of Applied Sciences Anhalt\nRobert Böhm\, Leipzig University of Applied Sciences\nAlexander Prange\, University of Applied Sciences Niederrhein\nJessica Friess\, University of Applied Sciences Niederrhein\n\n\n12-1:30pm: Lunch break (working lunch at McLeod Suites)\n1:30-2:30pm: Presentations from DFG Delegation (7 speakers with 8 minutes each)\n\nMargit Geißler Bonn-Rhein-Seig University of Applied Sciences\nJens Helbig\, Nürnberg Tech\nBenjamin Neding\, University of Applied Sciences Lübeck\nJörn V. Wochnowski University of Applied Sciences Lübeck\nRomana Piat\, University of Applied Sciences Darmstadt\nHolger Saage\, Univ. of Applied Sciences Landshut\, Competence Center for Lightweight Design\nJulian Tornow\, Hochschule Ruhr-West University of Applied Sciences\n\n\n2:30-3pm: DFG / NU discussions on potential collaborations\, programs\, etc.
URL:https://coe.northeastern.edu/event/technical-exchange-program-between-nu-and-dfg-delegation/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230901T130000
DTEND;TZID=America/New_York:20230901T140000
DTSTAMP:20260516T184306
CREATED:20230816T150411Z
LAST-MODIFIED:20230816T150432Z
UID:37858-1693573200-1693576800@coe.northeastern.edu
SUMMARY:Mostafa Abedi PhD Proposal Review
DESCRIPTION:Title: Power-Efficient and Security-Enhancing Techniques for Ultra-low Power IoT  Devices \nCommittee Members: \nProf. Aatmesh Shrivastava (Advisor)\nProf. Marvin Onabajo\nProf. Nian X. Sun \nAbstract:\nInternet-of-things (IoT) devices often rely on ambient energy sources such as photovoltaic (PV) cells and thermoelectric generators (TEGs) for their operation. Minimizing power loss through ambient energy harvesting optimization can significantly extend the battery life or support battery-free sensor nodes in IoT devices. A maximum power point tracking (MPPT) circuit is often used for impedance matching to maximize energy transfer efficiency. This research proposes an ultra-low power\, high-tracking efficiency MPPT circuit based on Hill-Climbing (HC) algorithm suitable for micro-power DC harvesters. The proposed system employs a modified version of the hill-climbing algorithm. In case of input power changes and consequent deviation of the harvester from the MPP\, an integrated Power Change Detector (PCD) is proposed to reactivate the MPPT circuit. The PCD detects changes in input power and activates the MPPT circuit\, enabling automatic activation and resulting in substantial power savings. Furthermore\, due to the proposed power estimation technique\, the MPPT is not dependent on the internal structure of the energy source\, and its tracking efficiency is unrelated to the conversion ratio of the converter. This approach enables us to achieve a peak tracking efficiency of over 99.9\%. To adjust the input power of the harvester to track the maximum power point\, we propose a new\, efficient Pulse Width Modulation (PWM) circuit. This circuit exhibits a wide duty cycle range\, low power consumption\, linearity\, and robustness against variations. \nThis research also focuses on increasing the security of IoT devices. In the past\, chip fabrication was mostly done internally by semiconductor firms. Now\, it is more collaborative\, pulling in designs from various sources and having a few factories produce them. This new way of working means that companies that only handle design might face more challenges like the threat of hardware Trojans (HT) being added either during the design phase or production. With that in mind\, we introduce a different circuit design approach. We aim to find these Trojans\, particularly the newer analog Trojans. The idea is to boost the security of IoT devices by detecting these issues early. In addition\, to improve the security of IoT systems\, we propose an ultra-low power energy monitoring system (EMS) to detect and mitigate denial-of-sleep (DoSL) attacks. In this project\, we explore a new method of defense against DoSL attacks by monitoring energy consumption. We will implement a low-power system to monitor the lifetime of the IoT node by continuously evaluating the harvested\, stored\, and consumed energy in the node.
URL:https://coe.northeastern.edu/event/mostafa-abedi-phd-proposal-review/
LOCATION:532 ISEC\, 360 Huntington Ave\, Boston\, MA\, 02115\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230825T100000
DTEND;TZID=America/New_York:20230825T110000
DTSTAMP:20260516T184306
CREATED:20230816T145957Z
LAST-MODIFIED:20230816T145957Z
UID:37862-1692957600-1692961200@coe.northeastern.edu
SUMMARY:Julian Gutierrez PhD Proposal Review
DESCRIPTION:Title: Towards Real-Time Safe Flight Paths for Urban Air Mobility \nCommittee Members:\nProf. David Kaeli\, (Advisor)\nProf. Pau Closas\nDr. Evan Dill (NASA)\nDr. Natasha Neogi (NASA) \nAbstract:\nThe emergence and development of advanced technologies and vehicle types have created a growing demand for introducing new forms of flight operations. These new and increasingly complex operational paradigms\, such as Advanced and Urban Air Mobility (AAM/UAM)\, present regulatory authorities and the aviation community with the challenge of finding methods to integrate these emerging operations without significant additional risk to pedestrians and infrastructure. Predictive and autonomous risk mitigation capabilities become critical to meet this challenge. However\, urban environments experience effects that are computationally expensive to model\, limiting conventional aviation concepts\, policy\, and risk prediction tools from being effectively translated into this space. With the emergence of High-Performance Computing (HPC) ecosystems in the last two decades\, we can use these software and hardware capabilities to help bridge the gap between real-time predictive responses and modeling accuracy. \nIn this dissertation we first present a simulation framework to estimate the quality of Global Navigation Satellite System (GNSS) performance for autonomous aircraft in urban environments. We propose a new algorithm designed for HPC to accelerate modeling the characteristic effects of dense urban canyons on GNSS\, allowing the extension of established GNSS integrity techniques into urban navigation. Additionally\, we provide a thorough validation of the simulator\, which proves high-accuracy modeling when compared to sensors in the real world. Second\, we use this simulation framework as the input into a new 4D path-planning algorithm based on an adaptation of the Bellman-Ford algorithm. HPC techniques are employed to accelerate the algorithm to produce flight paths that minimize exposure to GNSS risks. We evaluate the computational cost of satellite availability fluctuations by prioritizing events when satellite availability changes as triggers for these updates.
URL:https://coe.northeastern.edu/event/julian-gutierrez-phd-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230818T143000
DTEND;TZID=America/New_York:20230818T153000
DTSTAMP:20260516T184306
CREATED:20230817T142938Z
LAST-MODIFIED:20230817T142938Z
UID:37895-1692369000-1692372600@coe.northeastern.edu
SUMMARY:Xu Yizhe MS Thesis Defense
DESCRIPTION:Title:\nIntegration of Polyimide Flexible PCB Wings in Northeastern’s Aerobat \nLocation:\nRoom: ISEC 532\, Teams link \nCommittee Members:\nProf. Alireza Ramezani(Advisor)\nProf. Rifat Sipahi \nAbstract:\nThe principal aim of this Master’s thesis is to propel the optimization of the membrane wing structure of the Northeastern Aerobat through origami techniques and enhancing its capacity for secure hovering within confined spaces. Bio-inspired drones offer distinctive capabilities that pave the way for innovative applications\, encompassing wildlife monitoring\, precision agriculture\, search and rescue operations\, as well as the augmentation of residential safety. The evolved noise-reduction mechanisms of birds and insects prove advantageous for drones utilized in tasks like surveillance and wildlife observation\, ensuring operation devoid of disturbances. Traditional flying drones equipped with rotary or fixed wings encounter notable constraints when navigating narrow pathways. While rotary and fixed-wing systems are conventionally harnessed for surveillance and reconnaissance\, the integration of onboard sensor suites within micro aerial vehicles (MAVs) has garnered interest in vigilantly monitoring hazardous scenarios in residential settings. Notwithstanding the agility and commendable fault tolerance exhibited by systems such as quadrotors in demanding conditions\, their inflexible body structures impede collision tolerance\, necessitating operational spaces free of collisions. Recent years have witnessed an upsurge in integrating soft and pliable materials into the design of such systems; however\, the pursuit of aerodynamic efficiency curtails the utilization of excessively flexible materials for rotor blades or propellers. This thesis introduces a guard design incorporating feedback-driven stabilizers\, enabling stable hovering flights within Northeastern’s Robotics-Inspired Study and Experimentation (RISE) cage.
URL:https://coe.northeastern.edu/event/xu-yizhe-ms-thesis-defense/
LOCATION:532 ISEC\, 360 Huntington Ave\, Boston\, MA\, 02115\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230818T140000
DTEND;TZID=America/New_York:20230818T150000
DTSTAMP:20260516T184306
CREATED:20230817T143207Z
LAST-MODIFIED:20230817T143207Z
UID:37889-1692367200-1692370800@coe.northeastern.edu
SUMMARY:Haoling Li MS Thesis Defense
DESCRIPTION:Title: Ultracompact and Conformal Magnetodielectric Antennas \nCommittee Members:\nProf. Nian-Xiang Sun (Advisor)\nProf. Xufeng Zhang\nProf. Marvin Onabajo \nAbstract:\nNovel approaches are needed for improving antenna performance\, enhancing efficiency\, and reducing the size\, profile\, number\, and signature of antennas. Efficient conformal antennas are increasingly replacing traditional antennas across platforms such as ships\, aircraft\, and human interfaces. Magnetodielectric antennas made with high-hesistivity magnetic materials are getting more and more attention. Defined as the maximum magnetic conductivity\, hesitivity is directly related to the radiation efficiency of magnetodielectric antennas\, with a higher hesitivity corresponding to higher attainable efficiency. In this study\, new ultra-compact conformal magnetodielectric antennas are demonstrated\, employing commercially available ferrite ceramic substrates. Through rigorous simulation and fabrication\, a comprehensive comparison of our magnetodielectric antennas with reference monopole antennas demonstrated superior efficiency\, enhanced gain\, bandwidth\, and a substantial reduction in antenna size compared to monopole antennas. State-of-the-art hesitivity as high as 6×10^6 Ω/m has been reported in CoZrNb alloy films\, with an expectation of further 10× improvement in thin carrier substrates. This study forecasts the potential development of new magnetic materials with higher hesitivity\, leading to further advancements in magnetodielectric antennas with enhanced radiation efficiency and ground plane immunity.
URL:https://coe.northeastern.edu/event/haoling-li-ms-thesis-defense/
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DTSTART;TZID=America/New_York:20230818T100000
DTEND;TZID=America/New_York:20230818T110000
DTSTAMP:20260516T184306
CREATED:20230817T143431Z
LAST-MODIFIED:20230817T143431Z
UID:37897-1692352800-1692356400@coe.northeastern.edu
SUMMARY:Jaehyeon Ryu PhD Dissertation Defense
DESCRIPTION:Title: Materials Strategies for Scaling Soft Neuroelectrode Arrays \nLocation: Snell 012/Teams \nCommittee Members:\nProf. Hui Fang (Advisor)\nProf. Yongmin Liu\nProf. Ryan Koppes \nAbstract:\nThe evolution of electronics to seamlessly interface with biological tissue hinges on addressing multifaceted material constraints spanning electrochemical\, electrical\, and mechanical domains. Conventional bioelectronic interfaces\, while endowed with established electrochemical functionality\, remain hampered by rigidity that contradicts the pliability of surrounding tissue. While conductive materials exhibiting tissue-like softness and stretchability have been realized\, their potential for electrochemical probing of tissue is impeded by strain-induced performance degradation and an ill-suited integration with the irregular tissue interface. Nevertheless\, a significant challenge in ultrasoft bioelectronics pertains to scalability for achieving cellular resolution\, primarily due to mechanical disparities between conventional microelectronic materials and soft elastomer substrates. In this thesis\, by using a novel approach involving a multifunctional nanomesh\, composed of distinct purposefully designed layers including polymer for mechanical buffering\, metal for electrical conduction\, and low impedance coating for electrochemical interfacing in the same nanomeshed structure\, the resultant microelectrodes\, scalable down to 20μm at cellular resolution\, exhibit comparable performance to rigid devices alongside a stretchablity of approximately 50%\, with potential for future enhancement through in-plane structural optimizations. In addition\, we introduce a high-density neuroelectronic array featuring 256 filamentary neuroelectrodes on a flexible substrate. These electrodes are integrated with a single-transistor multiplexing acquisition circuit\, effectively reducing noise and footprint while potentially extending device lifetime. Remarkably\, the array’s rollable contact pad design allows for minimally invasive delivery through a syringe. Experimental validation demonstrates the array’s capability to record neural signals with high tone decoding accuracy. Utilizing high-density arrays of these microelectrode arrays\, this unique frame works holds significant promise for advancing the field of neural interfacing\, enabling a wide range of application from fundamental neuroscience studies to various biomedical applications.
URL:https://coe.northeastern.edu/event/jaehyeon-ryu-phd-dissertation-defense/
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DTSTART;TZID=America/New_York:20230817T130000
DTEND;TZID=America/New_York:20230817T140000
DTSTAMP:20260516T184306
CREATED:20230816T150620Z
LAST-MODIFIED:20230816T150620Z
UID:37856-1692277200-1692280800@coe.northeastern.edu
SUMMARY:Zhiyong Zhang PhD Proposal
DESCRIPTION:Title: Towards Indoor Mapping and Navigation with Perceptual Aliasing using Visual Semantic SLAM \nCommittee Members:\nProf. Hanumant Singh (Advisor)\nProf. Huaizu Jiang\nProf. David Rosen \nAbstract:\nModern SLAM (Simultaneous Localization And Mapping) techniques allow us to create accurate 3D maps of the environment primarily using visual sensors in GPS-denied regions. In this context\, numerous deep learning-based approaches have emerged\, enabling the extraction of rich semantic information from images\, including shapes\, objects\, and text. \nLeveraging these technologies\, our aim is to construct comprehensive 3D maps of indoor environments\, which could be utilized by robots for path planning and navigation. Additionally\, the solution can be integrated with a large language model\, enabling the robot to interact intuitively with people. \nThis research comprises four main components: Semantic Feature Extraction and Tracking with SLAM: Given that the same semantic features can appear in multiple frames\, some of which may not be conducive to feature detection and recognition (such as blurry images or distant views)\, we are developing a pipeline to ensure the optimal detection and recognition of semantic features within the most suitable frame. The pipeline also involves tracking the same feature across frames while maintaining its 3D location in the global map. \nResolving Perceptual Aliasing: Many indoor places can exhibit high visual similarity\, which confuses the robot when powered up with a prior map in its memory. Semantic features can be used to localize the robot in the map\, determining its specific floor or room. This capability can also aid SLAM in performing loop closure with high-level information. \nCross-Floor Constraints for SLAM Optimization: Most buildings contain a symmetric layout across floors\, which can be exploited to establish constraints between them. For instance\, vertically aligned rooms like 425 and 525\, as well as elevators\, offer opportunities for vertical constraint. Such constraints can enhance SLAM optimization\, resulting in improved map accuracy. \nIndoor Path Planning and Navigation: Once we have a comprehensive 3D map of the indoor environment\, path planning becomes an intuitive way to utilize this map. With semantic features integrated into the map\, the robot can associate 3D point clouds with high-level information\, such as door numbers or office names. Large language models are available to provide a more human-like way to interact with the robot. For example\, a command like “Navigate to Professor Hanumant’s office and locate the book ‘The Hitchhiker’s Guide to the Galaxy'” can be executed by the robot.
URL:https://coe.northeastern.edu/event/zhiyong-zhang-phd-proposal/
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DTSTART;TZID=America/New_York:20230817T090000
DTEND;TZID=America/New_York:20230817T110000
DTSTAMP:20260516T184306
CREATED:20230802T192116Z
LAST-MODIFIED:20230802T192116Z
UID:37698-1692262800-1692270000@coe.northeastern.edu
SUMMARY:Jagatpreet Nir PhD Proposal
DESCRIPTION:Title: Low Contrast Visual Sensing and Inertial Navigation in GPS Denied Environments \nCommittee Members:\nProf. Hanumant Singh\nProf. Martin Ludvigsen\nProf. Pau Closas\nProf. Michael Everrett \nAbstract:\nVisual inertial navigation has shown remarkable performance in publicly available datasets\, assuming certain ideal conditions such as textured scenes\, uniform illumination\, and static environments. However\, real-world scenarios often violate these assumptions\, resulting in significant visual degradation. Consequently\, the classical visual navigation pipelines fail and produce erroneous results\, rendering these systems ineffective for demanding field robotic missions. \nThis research aims to enhance the robustness of visual-inertial systems in visually degraded situations\, taking a comprehensive approach from both systems and algorithm perspectives. The work encompasses two primary objectives. Firstly\, it focuses on refining the characterization of MEMS-based inertial sensors and their error propagation in position\, while proposing improved dead-reckoning algorithms. Secondly\, it explores the performance limits of visual navigation under moderate to extreme visual degradation and investigates novel algorithms that leverage deep learning methods to bolster the visual navigation engine. To validate the efficacy of these advancements\, new datasets comprising drone and underwater robot scenarios are utilized\, demonstrating the applicability of this work in field robotic applications. \nBy addressing the limitations of existing visual-inertial navigation systems and developing robust algorithms\, this research aims to significantly enhance the reliability and performance of such systems in visually degraded environments\, thus expanding their potential for real-world applications in demanding field robotic missions.
URL:https://coe.northeastern.edu/event/jagatpreet-nir-phd-proposal/
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DTSTART;TZID=America/New_York:20230815T153000
DTEND;TZID=America/New_York:20230815T163000
DTSTAMP:20260516T184306
CREATED:20230816T150241Z
LAST-MODIFIED:20230816T150241Z
UID:37860-1692113400-1692117000@coe.northeastern.edu
SUMMARY:Sumegha Singhania MS Thesis Defense
DESCRIPTION:Title: Exploring Log of RGB Space as a Better Input for Computer Vision Tasks \nCommittee Members:\nProf. Bruce Maxwell (Advisor)\nProf. Hanumant Singh\nProf. David Rosen\nProf. Mahdi Imani \nAbstract:\nThere are specific\, physics-based rules that govern the interaction of light and matter. Though studied extensively in the greater computer vision community\, these rules are largely broken by common image processing techniques like JPEG compression and sRGB conversion. While the reliability and usability of color and intensity found in RAW images might better train networks to successfully complete vision-based tasks\, these smaller\, more heavily-processed formats have become the standard input for training sets. As a result\, many of the images used to train neural networks do not retain the inherent structure that would enable neural networks to learn more general rules that exist in the natural world. \nWe hypothesize that using linear RGB or log RGB images\, which preserve the physics of reflection\, can simplify the learning process for certain vision tasks\, enhance overall robustness and performance\, and provide invariance to visual variations that exist in real-world vision applications. Our research demonstrates that employing linear and log RGB images to train deep networks for the task of object detection improves their performance when using the same network architecture and the same set of training images. Additionally\, we also show that the networks trained on linear and log RGB show greater resilience to variations in intensity and color balance. Specifically\, the network trained on linear and log RGB inputs shows invariance to intensity and color balance variations that were not encountered during training\, while the network trained on the same images in sRGB JPEG format experiences significant performance degradation. To understand the reasons behind this disparity\, we analyze and visualize low-level features in log RGB\, linear RGB\, and JPEG data. Our findings reveal that the log space preserves certain relevant features across variations in intensity and color balance.
URL:https://coe.northeastern.edu/event/sumegha-singhania-ms-thesis-defense/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230811T140000
DTEND;TZID=America/New_York:20230811T150000
DTSTAMP:20260516T184306
CREATED:20230731T152837Z
LAST-MODIFIED:20230731T152837Z
UID:37660-1691762400-1691766000@coe.northeastern.edu
SUMMARY:Jinkun Zhang PhD Proposal Review
DESCRIPTION:Location: ISEC305 \nTitle: Low-latency forwarding\, caching and computation placement in data-centric networks \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. These limitations include significant round-trip delays caused by backhaul network capacity bottlenecks and exorbitant costs associated with centralized computing power\, e.g.\, training GPT-4 requires over 16\,000 A100 GPUs.\nTo 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. \nFurthermore\, in data-centric networking\, communication and computation are resolved around data names instead of host addresses.\nThe deployment of network caches\, by enabling data reuse\, offers substantial benefits for data-centric networks.\nFor instance\, consider a scenario where multiple machine learning applications seek to train different models simultaneously. These application could (partially) share data samples and intermediate results\, and carefully designed data-reusing mechanisms become necessary. Optimal caching of data or intermediate results can significantly reduce the overall training cost\, compared to each application independently gathering and transmitting data. \nTo efficiently manage computation and storage resources in heterogeneous data-centric networks\, several frameworks have been proposed with different design objectives\, such as optimizing throughput or incorporating multicast flows. However\, previous approaches have failed to minimize average user delay despite the latency sensitivity of numerous real-world applications. \nThis proposal aims to address this gap by introducing 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 networks with heterogeneous communication\, storage\, and computation capabilities. \nSpecifically\, to minimize user latency in congestible networks\, we model delays caused by link transmissions and CPU computations using\ntraffic-dependent nonlinear functions. We formulate the joint forwarding\, caching\, and computation problem as an NP-hard mixed-integer non-submodular optimization\, for which no constant-factor approximation algorithms are currently known. To make progress\, we approach the joint problem by dividing it into two subproblems: the joint forwarding/computation problem and the joint forwarding/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 show that the objective function is a sum of a convex function and a geodesic convex function\, and 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. Furthermore\, our theoretical bound leads to a constant-factor approximation under additional assumptions. \nAs future work\, we propose to develop a novel in-network large model training framework\, building upon the aforementioned method.\nDue to the substantial model size and extensive data samples required for training\, centralized model storing and training are nearly infeasible for small and intermediate service providers.\nConsequently\, we will adopt horizontal model partitioning and distribute different model layers across the network nodes through caching.\nData samples or batches are input into the network and undergo the forward-backward procedure for training. Our objective is to jointly optimize data forwarding and model/computation placement\, thereby minimizing the total cost of transmission\, computation\, and storage. \nFurthermore\, we introduce several network resource allocation optimization problems related to data-centric networking\, thereby expanding the scope of our proposal.
URL:https://coe.northeastern.edu/event/jinkun-zhang-phd-proposal-review/
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