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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240807T110000
DTEND;TZID=America/New_York:20240807T120000
DTSTAMP:20240820T220332Z
CREATED:20240820T220332Z
LAST-MODIFIED:20240820T220332Z
UID:7188-1723028400-1723032000@ece.northeastern.edu
SUMMARY:Cobra Alemdar PhD Dissertation Defense
DESCRIPTION:Name:\nKubra Alemdar \nTitle:\nOvercoming and Engineering Wireless Signals for Communication and  Computation \nDate:\n8/7/2024 \nTime:\n11:00:00 AM \nCommittee Members:\nProf. Kaushik Chowdhury (Advisor)\nProf. Josep Jornet\nProf. Marvin Onabajo \nAbstract:\nThe phenomenal growth of connected devices\, especially rapid expansion of IoT networks and the increasing demand for wireless services are the main driving forces for the evolution of wireless technologies. However\, the realization of such technologies requires a radical transformation of existing infrastructures to satisfy the needs of changing wireless environments. The main limitation in delivering these systems stems from a vast diversity in their demands and constraints. To address this limitation\, this dissertation shows how wireless signals and their interaction with and within the wireless propagation domain can be used as communication or computational tools that enable us to achieve certain novel tasks. Specifically\, we build i) cross-functionality architectures to engineer the wireless channel to a) enable the operation of emerging technologies\, and b) demonstrate a new paradigm for computing with wireless signals\, and ii) intelligently shape the wireless channel to create reliable communication links. This dissertation presents an experimentally validated software-hardware systems with thorough analysis\, delivering the following key advancements with distinct contributions: \nFirst\, We present an innovative physical layer solution for distributed networks that provides over-the-air (OTA) clock synchronization\, known as RFCLOCK\, to overcome the hurdle of implementing fine-grained synchronization for emerging technologies. We first develop the theory for such precision synchronization\, and second implement it in a custom-design\, compatible with commercial-off-the-shelf (COTS) software-defined radios (SDRs). We compare the performance of RFClock with popular wired and GPS-based hardware solutions\, both in terms of clock performance as well as impact on distributed beamforming. \nNext\, we propose two novel approaches\, utilizing reconfigurable intelligent surfaces (RISs) to ensure reliable connectivity in wireless networks by controlling the propagation environment: i) we present RIS-based spatio-temporal approach to enhance the link reliability for IoTs where sensors are small-factor designs with single-antenna in a rich multipath environment. We demonstrate the design of RIS and how it can effectively perturb the environment\, generating multiple wireless propagation channels and achieving the performance of a multi-antenna receiver in a Single-Input Single-Output (SISO) link. We compare the performance of the system with a multi-antenna receiver in terms of channel hardening and outage probability. ii) We introduce REMARKABLE\, an online learning based adaptive beam selection strategy for robot connectivity that trains kernelized multi-armed bandit (MAB) model directly in real-world settings of a factory floor. We show how RISs with passive reflective elements can create beamforming towards target robots\, and provide a solution to the problem of adaptive beam selection in dynamic channel conditions. We experimentally demonstrate that REMARKABLE can achieve a significant reduction in beam selection time compared to classical approaches and adaptive beam selection in mobility settings. \nFinally\, we introduce AirFC\, a system harnessing the capability of OTA computation to run inference on a neural network (NN) consisting of a set of fully connected layers (FC) by leveraging multi-antenna systems. We experimentally demonstrate and validate that such computation is accurate enough when compared to its digital counterpart. \n 
URL:https://ece.northeastern.edu/event/cobra-alemdar-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240808T150000
DTEND;TZID=America/New_York:20240808T160000
DTSTAMP:20240820T221121Z
CREATED:20240820T221121Z
LAST-MODIFIED:20240820T221121Z
UID:7190-1723129200-1723132800@ece.northeastern.edu
SUMMARY:Peiyan Dong PhD Dissertation Defense
DESCRIPTION:Name:\nPeiyan Dong \nTitle:\nSoftware-Hardware Co-Design: Towards Ultimate Efficiency in Deep Learning Acceleration \nDate:\n8/8/2024 \nTime:\n3:00:00 PM \nCommittee Members:\nProf. Yanzhi Wang (Advisor) \nProf. David R. Kaeli \nProf. Devesh Tiwari\nProf. Cheng Tan \nAbstract:\nAs AI techniques continue to advance\, the efficient deployment of deep neural networks on resource-constrained devices becomes increasingly appealing yet challenging. Simultaneously\, the proliferation of powerful AI technologies has raised significant concerns about sustainability and fairness\, demanding increased attention from the community. This talk presents two novel software-hardware co-designs for improving the efficiency and sustainability of deep learning models. The first part introduces a hardware-efficient adaptive token pruning framework for Vision Transformers (ViTs) on embedded FPGA\, HeatViT\, which achieves significant speedup under similar model accuracy compared to the state-of-the-art. HeatViT is the first end-to-end accelerator for ViT on embedded FPGA and also achieve practical speedup by data-level compression for the first time. The second presents PackQViT and Agile-Quant\, a paradigm of the efficient implementation for transformer-based models by sub-8-bit packed quantization and SIMD-based optimization for computing kernels. Our framework can achieve better task performance than state-of-the-art ViTs and LLMs with significant acceleration on edge processors\, such as mobile CPU\, Raspberry Pi and RISC-V. This work not only marks the first successful implementation of the LLM on the edge but also addresses the previous limitation where edge processors struggled to efficiently handle sub-8-bit computations. At the conclusion of the presentation\, the speaker will discuss today’s challenges related to AI sustainability and fairness and outline her research plans aimed at addressing these issues. \n 
URL:https://ece.northeastern.edu/event/peiyan-dong-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240809T110000
DTEND;TZID=America/New_York:20240809T120000
DTSTAMP:20240820T221215Z
CREATED:20240820T221215Z
LAST-MODIFIED:20240820T221215Z
UID:7192-1723201200-1723204800@ece.northeastern.edu
SUMMARY:Yifan Gong PhD Dissertation Defense
DESCRIPTION:Name:\nYifan Gong \nTitle:\nTowards Energy-Efficient Deep Learning for Sustainable AI \nDate:\n8/9/2024 \nTime:\n11:00:00 AM \nCommittee Members:\nProf. Yanzhi Wang (Advisor) \nProf. David R. Kaeli \nProf. Xue Lin \nProf.  Huaizu Jiang\nProf. Stratis Ioannidis \nAbstract:\nThe rapid advancements in deep learning (DL) and artificial intelligence (AI) have led to transformative applications across various domains\, such as community virtual reality experiences\, autonomous systems\, and climate change prediction. Edge devices including mobile and embedded systems play a vital role in carrying these applications\, facilitating the widespread adoption of machine intelligence. Along with the great success of DL and AI is the huge energy consumption for both training and inference. With the breakthrough of large-scale models for AI-generated content (AIGC) such as large language models and diffusion models\, the energy consumption issue intensifies\, causing the urgent need for sustainable AI solutions. In this talk\, I will talk about how to facilitate deep learning on various edge devices in an energy-efficient manner for the goal of sustainable AI. Specifically\, I will start by introducing my two system-level approaches to tackling the challenge. The first approach is named bottom-up\, which conducts AI algorithm-aware efficient system design. The second approach is a top-down approach that achieves hardware-driven efficient AI algorithm design. Then\, I will share my recent works addressing the efficiency issues for large-scale models. Finally\, I will show the applications of my methods and pointers to the future direction. \n 
URL:https://ece.northeastern.edu/event/yifan-gong-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240812T100000
DTEND;TZID=America/New_York:20240812T110000
DTSTAMP:20240820T220016Z
CREATED:20240820T220016Z
LAST-MODIFIED:20240820T220016Z
UID:7184-1723456800-1723460400@ece.northeastern.edu
SUMMARY:Gözde Özcan PhD Dissertation Defense
DESCRIPTION:Name:\nGözde Özcan \nTitle:\nLearning and Optimizing Set Functions \nDate:\n8/12/2024 \nTime:\n10:00:00 AM \nLocation:\nEXP 601\nCommittee Members:\nProf. Stratis Ioannidis (Advisor)\nProf. Jennifer Dy\nProf. Evimaria Terzi \nAbstract:\nLearning and optimizing set functions play a crucial role in the artificial intelligence research as various problems of interest can be characterized with set inputs and/or outputs. Submodular functions\, i.e.\, set functions with a diminishing returns property\, are an important subcategory of such functions. They naturally present themselves in applications such as sensor placement\, data summarization\, feature selection\, influence maximization\, hyper-parameter optimization\, and facility location\, to name a few. In a lot of these compelling problems\, the objective is to maximize a submodular function subject to matroid constraints\, which is known to be NP-hard. For problems of this nature\, the continuous greedy algorithm provides a (1 − 1/e)-approximation guarantee in polynomial-time. It does so by estimating the gradient of the so-called multilinear relaxation of the objective function via sampling. However\, for the general class of submodular functions\, the number of samples required to achieve this theoretical guarantee can be computationally prohibitive. \nIn this dissertation\, we address deterministic submodular maximization problems with matroid constraints\, specifically those with objectives expressed through compositions of analytic and multilinear functions. We introduce a novel polynomial series estimator to approximate the multilinear relaxation of such functions and demonstrate that the sub-optimality introduced by our polynomial expansion can be minimized by increasing the polynomial order. By utilizing this estimator\, a variant of the continuous greedy algorithm achieves an approximation ratio close to (1 − 1/e) ≈ 0.63 through deterministic gradient estimation. In numerical experiments\, our polynomial estimator outperforms the sampling estimator\, offering reduced errors in less time. \nWe extend our study to the stochastic submodular maximization setting with general matroid constraints\, where objectives are defined as expectations over submodular functions with an unknown distribution. Adapting polynomial estimators to this context reduces the variance of the gradient estimation while introducing a controlled bias term. For several notable stochastic submodular maximization problems\, we demonstrate that this bias decays exponentially with the degree of our polynomial approximators. Furthermore\, for monotone functions\, a stochastic variant of the continuous greedy algorithm attains an approximation ratio (in expectation) close to (1 − 1/e) ≈ 0.63 using these polynomial estimators. Our experimental results validate the advantages of our approach across synthetic and real-life datasets. \nFinally\, we turn our attention to the learning set functions under a so-called optimal subset oracle setting. A recent approach approximates the underlying utility function with an energy-based model. Approximating this energy-based model yields iterations of fixed-point update steps during mean-field variational inference. However\, these fixed-point iterations are not guaranteed to converge and as the number of iterations increases\, automatic differentiation quickly becomes computationally prohibitive due to the size of the Jacobians that are stacked during backpropagation. We address these challenges by examining the convergence conditions for the fixed-point iterations and utilizing implicit differentiation over automatic differentiation. We empirically demonstrate the efficiency of our method on synthetic and real-world subset selection applications.
URL:https://ece.northeastern.edu/event/gozde-ozcan-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240813T140000
DTEND;TZID=America/New_York:20240813T150000
DTSTAMP:20240820T215923Z
CREATED:20240820T215923Z
LAST-MODIFIED:20240820T215923Z
UID:7182-1723557600-1723561200@ece.northeastern.edu
SUMMARY:Yufei Feng MS Thesis Defense
DESCRIPTION:Name:\nYufei Feng \nTitle:\nBeam Management in Operational 5G mmWave Networks \nDate:\n8/13/2024 \nTime:\n2:00:00 PM \nCommittee Members:\nProf. Dimitrios Koutsonikolas (Advisor)\nProf. Josep Jornet\nProf. Mallesham Dasari \nAbstract:\nDue to the directional nature of mmWave signal propagation\, beam management plays a critical role in the performance of 5G mmWave deployments. However\, the details of beam management in commercial deployments and its performance in real-world scenarios remain largely unknown. In this paper\, we fill this gap by performing a comparative measurement study of the beam management procedure of two major US operator in Boston\, MA. We study a number of beamforming parameters including beamwidth\, number of beams\, beam switching delay\, and their impact on performance\, and we explore the interplay between beam management and rate adaptation. We also investigate for first time Rx beam management on the UE side. Finally\, we study the beam tracking performance and the quality of the selected beams for two operators.
URL:https://ece.northeastern.edu/event/yufei-feng-ms-thesis-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240816T110000
DTEND;TZID=America/New_York:20240816T130000
DTSTAMP:20240820T215800Z
CREATED:20240820T215800Z
LAST-MODIFIED:20240820T215800Z
UID:7180-1723806000-1723813200@ece.northeastern.edu
SUMMARY:Yanyu Li PhD Dissertation Defense
DESCRIPTION:Name:\nYanyu Li \nTitle:\nAccelerating Large Scale Generative AI: a Comprehensive Study \nDate:\n8/16/2024 \nTime:\n11:00:00 AM \nCommittee Members:\nProf. Yanzhi Wang (Advisor)\nProf. David Kaeli\nProf. Kaushik Chowdhury \nAbstract:\nWe have witnessed the great success of deep learning in various domains\, such as the emerging large language models (LLMs) and Artificial General Intelligence (AGI)\, diffusion models for image and video generation\, and classic vision tasks including classification\, segmentation\, detection\, etc. Built with linear\, convolution\, and attention blocks\, Deep Neural Networks (DNNs) play a vital role in the performance revolution. However\, powerful DNNs often call for tremendous computation and storage size\, which hinders their wide adoption. For instance\, LLMs and diffusion models generally have billions of parameters and hundreds of GMACs\, which is prohibitive for edge deployment. As a result\, Efficient AI has become a hot research area. In this work\, with algorithm optimizations and co-designs with hardware platform\, we pursue the appealing features of edge or user-end AI\, where we cut down energy consumption\, shorten response latency\, shrink model storage size\, eliminate the need for cloud server access and protect user privacy. Firstly\, we systematically investigate quantization\, pruning\, and architecture search techniques for efficient vision backbones. We do a comprehensive study on quantization number system and precision\, and propose a novel mix-scheme mix-precision quantization technique to maximize hardware utilization and minimize performance loss. Regarding network pruning\, we propose a novel indicator-based approach\, named Pruning-as-Search\, that is fully differentiable and automatically decides pruning policies\, outperforming human tuning methods in terms of performance and efficiency. Further\, we address the long-existing issue of rigid network width design\, proposing a family of flexible-width pruned networks with minimal per-layer redundancy. As for architecture search\, we formulate a joint optimization objective of both size and latency\, releasing a series of efficient Vision Transformers\, named EfficientFormer (V1 and V2)\, to serve as strong vision backbones with MobileNet-level size and millisecond-level latency on mobile phones. \nSecondly\, we make dedicated optimizations for large-scale generative tasks\, i.e.\, Stable Diffusion (SD) for text-to-image generation\, which serves as pioneer work to enable their mobile deployment. With the proposed efficient architecture design and novel step distillation\, we shrink the generation latency of SD by a magnitude\, from more than 1 minute to generate a 512$\times$512 image to 1~2 seconds\, while preserving the stunning generative quality. We extend our work to the even more challenging video generation task\, enabling 2-bit inference and single step adversarial distillation to speedup video diffusion models by a magnitude.
URL:https://ece.northeastern.edu/event/yanyu-li-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240816T140000
DTEND;TZID=America/New_York:20240816T160000
DTSTAMP:20240820T222221Z
CREATED:20240820T222221Z
LAST-MODIFIED:20240820T222221Z
UID:7207-1723816800-1723824000@ece.northeastern.edu
SUMMARY:Shuo Jiang PhD Dissertation Defense
DESCRIPTION:Name:\nShuo Jiang \nTitle:\nTactile Intelligence in Robotics \nDate:\n8/16/2024 \nTime:\n2:00:00 PM \nLocation:\nEXP-701A \nCommittee Members:\nProf. Lawson Wong (Advisor)\nProf. Robert Platt\nProf. Alireza Ramezani\nProf. Taskin Padir \nAbstract:\nIn recent years\, the evolution of robot electronic skin technology has introduced a novel avenue for robots to perceive their external environment and internal state. In contrast to conventional visual perception methods\, tactile perception enables the discernment of additional physical properties of objects\, such as friction and mass distribution\, or even observes contact with higher resolution. Importantly\, tactile perception is resilient to challenges posed by inadequate illumination or environmental occlusion. However\, it presents inherent challenges\, including a limited sensing range\, compulsory physical interaction with the environment\, and intricate coupling with robot control\, rendering data collection and utilization challenging. Addressing these challenges and devising effective\, efficient\, and interpretable methods for processing tactile signals have emerged as pivotal issues in robot tactile perception. \nWith the development of artificial intelligence technology\, we are now able to interpret tactile information from a new perspective beyond traditional sensor technology and signal processing methods\, thereby expanding a wider range of robotic applications. With our continuous efforts over the past few years\, we have comprehensively addressed the following challenges in enhancing robot tactile perception through the application of advanced artificial intelligence and control methods: enabling robots to explore object shapes through tactile feedback; developing tactile-based safety mechanisms for human-robot collaboration; enhancing the locomotion adaptability of snake robots on irregular terrains through tactile perception; utilizing whole-body exteroceptors and proprioceptors for accurate body schema estimation; and implementing tactile gesture recognition in human-robot interactions. At the same time\, we developed a modular full-body electronic skin system for robots and its accompanying software\, which can accurately detect forces applied to the robot’s entire body and perform high-speed tracking of the real-time kinematics of the robot’s sensor array. \nIn conclusion\, this dissertation explores how robot tactile perception can accomplish complex tasks in various scenarios or achieve performance improvements in traditional tasks through the integration of sensor technology\, machine learning\, control theory\, and robotics. Through extensive theoretical and experimental analysis\, we have demonstrated the critical role of tactile perception in embodied intelligence for robots and established a fundamental knowledge framework for future academic research in this field.
URL:https://ece.northeastern.edu/event/shuo-jiang-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240819T100000
DTEND;TZID=America/New_York:20240819T110000
DTSTAMP:20240820T181408Z
CREATED:20240820T181408Z
LAST-MODIFIED:20240820T181408Z
UID:7172-1724061600-1724065200@ece.northeastern.edu
SUMMARY:Yuexi Zhang PhD Dissertation Defense
DESCRIPTION:Name:\nYuexi Zhang \nTitle:\nHuman Action and Event Detection by Leveraging Multi-modality Techniques \nDate:\n8/19/2024 \nTime:\n10:00:00 AM \nCommittee Members:\nProf. Octavia Camps (Advisor) \nProf. Mario Sznaier \nProf. Sarah Ostadabbas \nAbstract:\nHuman Action and Event Analysis with multi-modalities has emerged as a critical area of research in computer vision and machine learning\, driven by the need to understand complex human behaviors in diverse environments. \nA significant advantage of multi-modal analysis is its application in cross-view action recognition\, where activities are observed from different viewpoints. To tackle such a problem\, we propose a flexible frame which is able to integrate diverse modalities(RGB pixels\, 2D/3D key points\, etc.) to overcome the limitations of single-modal approaches. It consists of two branches where a Dynamic Invariant Representation branch (DIR) concentrates on identifying view-invariant properties through key points trajectories while Context Invariant Representation branch(CIR) is to capture the pixel-level view-invariant features. In the meantime\, our approach leverages contrastive learning techniques to enhance the effectiveness of recognition accuracy\, where it enables the model to learn more discriminative and view-invariant features by contrastive positive pairs against negative pairs. The fusion of multi-modal data\, coupled with contrastive learning\, leads to improved accuracy in recognizing actions across various views and environments. Extensive experiments demonstrate the effectiveness of our approach on diverse modalities. Furthermore\, another promising application with multi-modal techniques is zero-shot action detection\, which aims to recognize actions that the model has not been explicitly trained on. Recently\, with language models are quickly developed\, leveraging LLMs in this context has shown significant potentials\, as these models can bridge the gap between seen and unseen actions by understanding and generalizing from textual descriptions. To further explore the problem\, we propose a transformer encoder-decoder architecture with global and local text prompt\, which allowing the model to infer the characteristics of unseen actions based on different textual attributes. We evaluate our approach on different benchmarks to demonstrate advantages.
URL:https://ece.northeastern.edu/event/yuexi-zhang-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240822T130000
DTEND;TZID=America/New_York:20240822T140000
DTSTAMP:20240820T215132Z
CREATED:20240820T215132Z
LAST-MODIFIED:20240820T215132Z
UID:7176-1724331600-1724335200@ece.northeastern.edu
SUMMARY:Zohreh Azizi PhD Proposal Review
DESCRIPTION:Name:\nZohreh Azizi \nTitle:\nExploring SIM for monomer orientation \nDate:\n8/22/2024 \nTime:\n1:00:00 PM \nLocation: https://northeastern.zoom.us/j/7318775019\nMeeting ID: 731 877 5019 \nCommittee Members:\n1. Prof. Charles DiMarzio (Advisor)\n2. Prof. Carey Rappaport\n3. Dr. Sangyeon (Fred) Cho \nAbstract:\nCollagen fibrils\, the most abundant protein polymers in animals\, protect cells from mechanical forces such as stress\, tension\, compression\, and shear. Each collagen molecule\, approximately 300 nm long and 1.5 nm in diameter\, consists of three polypeptide chains forming a supercoiled triple helix. These fibrils self-assemble\, with diameters ranging from 20 nm to several hundred nanometers. Large collagen fibrils are visible with scanning electron microscopy (SEM) and optical microscopy. Electron microscopy damages samples during preparation\, limiting observations to static\, non-living conditions. The natural self-assembly behavior\, spatial arrangement\, and dimensions of collagen fibrils play a vital role in shaping the structure\, strength\, and function of tissues. These factors are essential in determining how tissues are organized\, how they withstand physical forces\, and how effectively they perform their biological functions. \nDetecting the orientation and location of collagen monomers is essential for understanding their role in collagen spontaneous formation and their interactions with fibril surfaces. To study collagen monomers in a dynamic\, living state\, high-resolution optical microscopy is preferred\, as it allows for detailed imaging beyond the diffraction limit of light.We introduce a new technique using structured illumination to determine the spatial separation of punctuate objects to super–resolution limits that is amenable to both scattering and fluorescent objects. We call the technique Structured–Illumination Point–Separation (SIPS) Microscopy. We apply it to determine the orientation of a collagen monomer by imaging two fluorescent tags at different locations on the monomer. Experimentally\, we show that our approach effectively resolves the orientation of collagen monomers with a resolution surpassing the diffraction limit. \nIn this illumination technique we are employing time-multiplexed binary patterns with a DMD based SIM while the camera shutter is open\, mitigating undesired diffractions from the DMD. Three different phases of sinusoidal patterns generated by the DMD 3000 DLP series are projected onto the fluorescent sample in a 4f system. After data acquisition of the effect of the structured patterns on sample in three different phases images multiplied by a phase factor (1\, 𝑒−𝑖2𝜋/3\,𝑒−𝑖4𝜋/3 ) and then combined. By implementing the Radon transform of Fourier transform of phase of complex image\, and evaluate the Radon transform in center of x’=0 \, direction of pairs obtain. we enhance image reconstruction and analysis\, utilizing their characteristics for detailed imaging and data extraction in Structured–Illumination Point–Separation. With this strategy a non-destructive and effective technique that allows researchers to measure orientation of collagen monomer in a way that preserves the sample and offers high-resolution imaging\, which is crucial for studying the structure and properties of collagen.
URL:https://ece.northeastern.edu/event/zohreh-azizi-phd-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240903T100000
DTEND;TZID=America/New_York:20240903T110000
DTSTAMP:20240820T215639Z
CREATED:20240820T215639Z
LAST-MODIFIED:20240820T215639Z
UID:7178-1725357600-1725361200@ece.northeastern.edu
SUMMARY:Yuhui Bao PhD Dissertation Defense
DESCRIPTION:Name:\nYuhui Bao \nTitle:\nA Design Methodology for Producing Highly-Adaptable and High-Performance Simulation Frameworks \nDate:\n9/3/2024 \nTime:\n10:00:00 AM\nCommittee Members:\nProf. David Kaeli (Advisor)\nProf. Ningfang Mi\nProf. Yifan Sun (William and Mary) \nAbstract:\nComputer architecture simulators play an essential role in the development and optimization of computer hardware. A variety of simulators have been developed to explore the design space of CPUs\, GPUs\, and customer accelerators. As GPUs continue to grow in popularity for accelerating demanding applications\, such as high-performance computing and machine learning\, GPU architects have been pushing the envelope of GPU performance in every new GPU generation. GPU vendors (e.g.\, NVIDIA and AMD) have been introducing subsequent generations of GPU architectures and products with updated instruction set architectures (ISAs) and new microarchitectural features every 2-3 years. Modeling the state-of-the-art architecture is a crucial feature of GPU simulators\, which are used to characterize and accelerate challenging workloads facilitating performance evaluation and design exploration. However\, the effort required to design and construct an accurate and performant simulator is huge. Due to the rapid rate of innovation in GPU technology\, any simulator that is over-customized to capture the design of a specific architecture will quickly become outdated. Thus\, we need to develop a design methodology for simulators that can guard against this trend\, embracing future architectures. \nIn this dissertation\, we propose a design methodology for producing highly-adaptable and high-performance simulation frameworks. We aim to design simulators featuring high adaptability\, being able to accommodate future alterations or extensions\, high performance and high fidelity. We leverage the Akita simulator framework to enable the modular and extensible design of various GPU components. To fulfill the goal of high fidelity\, we design a set of microbenchmarks to evaluate individual GPU subsystems. We demonstrate how we follow our design methodology to achieve a highly-adaptable and accurate simulator — NaviSim\, which provides the flexibility to support simulation of three different ISAs. To demonstrate the full utility of the NaviSim simulator\, we conduct a performance study of the impact of individual architecture features revealing the high flexibility and configurability of NaviSim. In addition\, we showcase how NaviSim’s high adaptability contributes to design space exploration\, offering solutions to enhance the performance of real-world demanding applications. \nFast simulation speed is one of the key requirements of any simulators. NaviSim is designed to support multi-threaded execution\, which is able to leverage the parallel capabilities offered by today’s multi-core CPUs\, enabling parallel simulation. In this thesis we identify key performance bottlenecks in terms of both serial and parallel simulation execute modes and optimize simulation speed. We also present lessons learned about efficient simulator design and provide guidance for future simulator developers.
URL:https://ece.northeastern.edu/event/yuhui-bao-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240909T090000
DTEND;TZID=America/New_York:20240909T100000
DTSTAMP:20240822T181919Z
CREATED:20240820T181643Z
LAST-MODIFIED:20240822T181919Z
UID:7174-1725872400-1725876000@ece.northeastern.edu
SUMMARY:Rajiv Singh PhD Dissertation Defense
DESCRIPTION:Name:\nRajiv Singh \nTitle:\nInterpolation and Convexification Methods for Tractable Learning of Dynamic Systems \nDate:\n9/9/2024 \nTime:\n9:00:00 AM \nLocation: https://northeastern.zoom.us/j/97729968899?pwd=WfExrC0k60ocNpzCrkJXK3HyJDptMK.1 \nCommittee Members:\nProf. Mario Sznaier (Advisor) \nProf. Lennart Ljung \nProf. Octavia Camps\,\nProf. Stratis Ioannidis \nAbstract:\nIn this thesis\, we present interpolation and convexification based system identification techniques that are geared towards producing\, practical\, engineering-friendly models. The models are either linear\, or close to being linear – they are either weakly nonlinear\, are described by a switching among linear models\, or linear models whose parameters are allowed to depend upon certain states or inputs or the system. A common objective in all the proposed approaches is to determine the lowest-order models that are consistent with the information available in the form of data and available priors. We leverage ideas from the rational interpolation community in order to create tractable algorithms that are efficient and often scale well with the amount of data. In addition\, we present control-oriented learning methods extend the basic approaches by directly incorporating the closed-loop objectives. The resulting models are self-certified in that they produce certificates of guaranteed closed-loop behavior. \nA summary of the essential ideas presented in this thesis follows next.\n1. Using the rank-revealing properties of Loewner and Hankel matrices\, we develop a convex algorithm for identification of low order stable transfer functions using time and frequency domain data. This results are guaranteed to meet prescribed worst case bounds. \n2. We propose a set of techniques geared towards control-oriented identification of potentially unstable linear models using open-loop data. These models come with a certification of robust stabilizability which greatly aids the control design procedure. The first technique leverages the concept of coprime factors of a linear system while the second technique uses robust identification of a system’s predictor as a vehicle towards identification of the plant model. The latter technique also directly incorporates the closed-loop objective of νgap minimization into the identification procedure. \n3. We present convex approaches to identification of nonlinear polynomial models with time-varying coefficients. The model coefficients evolution is governed by scheduling maps that are described by low-order linear differential equations. A first approach uses a Hankel matrix rank minimization technique towards a joint identification of the model’s parameters and the scheduling map. A second approach leverages the atomic norm minimization framework to extend the first approach to bilinear systems\, and also support easy incorporation of scheduling priors. \n4. We present some results regarding sparse identification of Nonlinear ARX models incorporating bounded nonlinear maps. We present approaches to achieve sparsity with respect to the number of regressors used\, and with respect to the maximum lag employed by any of the contributing regressors. The proposed algorithm leverages ideas from sparse learning and ensemble learning for sparse NARX models. \n5. We present a new framework for identification of switched linear and parameter-varying systems based on rational interpolation. We develop multivariate interpolation procedures based on the recent “block-AAA” algorithms. We demonstrate that this modeling framework leads to fast\, accurate\, and scalable algorithms that can be used in various settings where the data domain is described by correlations\, frequency\, or scheduling variables.
URL:https://ece.northeastern.edu/event/rajiv-singh-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240920T100000
DTEND;TZID=America/New_York:20240920T170000
DTSTAMP:20240819T185214Z
CREATED:20240819T185214Z
LAST-MODIFIED:20240819T185214Z
UID:7169-1726826400-1726851600@ece.northeastern.edu
SUMMARY:Visual AI Hackathon
DESCRIPTION:ECE Associate Professor Sarah Ostadabbas\, in collaboration with Voxel51\, is hosting an exciting hackathon on September 20\, 2024\, from 10 AM to 5 PM Eastern at the Raytheon Amphitheater – 240 Egan Building. This event offers an immersive experience for machine learning enthusiasts and college students\, featuring cash prizes\, a collaborative environment\, refreshments\, and swag for participants. Whether you’re a beginner or looking to sharpen your skills\, there’s something for everyone. \nLink: https://voxel51.com/computer-vision-events/visual-ai-hackathon-sept-20-2024/ \nThe hackathon coincides with Prof. Ostadabbas’s new course\, “Machine Learning with Small Data\,” which is being offered for the first time on the Boston campus this Fall. Industry experts will judge submissions\, with prizes awarded to the most innovative solutions. Don’t miss this opportunity to collaborate\, learn\, and make your mark in the AI community!
URL:https://ece.northeastern.edu/event/visual-ai-hackathon/
LOCATION:Raytheon Amphitheater (240 Egan)\, 360 Huntington Ave\, 240 Egan\, Boston\, MA\, 02115\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20241007T103000
DTEND;TZID=America/New_York:20241007T130000
DTSTAMP:20240910T010233Z
CREATED:20240903T234950Z
LAST-MODIFIED:20240910T010233Z
UID:7261-1728297000-1728306000@ece.northeastern.edu
SUMMARY:Women in Engineering at Northeastern
DESCRIPTION:Women in Engineering (WIE) at Northeastern College of Engineering (CoE)\, in collaboration with the Electrical and Computer Engineering (ECE) and Mechanical and Industrial Engineering (MIE) Departments\, ECE PhD Student Association (EPSA) and MIE Diversity\, Equity\, and Inclusion (DEI) student groups\, is hosting a special gathering for female-identifying PhD students\, postdocs\, and faculty from the ECE and MIE departments. The purpose of this event is to check in with our students\, hear their thoughts and concerns\, and ensure they feel supported—not only to strengthen their sense of belonging but also to equip them with the necessary skills for their future careers. \nThe gathering will take place on Monday\, October 7th\, from 10:30 am to 12:30 pm at the Curry Student Center Ballroom\, with lunch provided. \nTo better understand the experiences\, motivations\, and challenges faced by female-identifying PhD students and Postdocs at Northeastern University in the MIE and ECE departments\, we’ve prepared the following questions\, which will be answered anonymously: https://docs.google.com/forms/d/e/1FAIpQLSd64OWZK3yxEQmzmn3yCXNYJyBi5GaiarxGJjUnTKWKtBFUTg/viewform?usp=sf_link \nYour feedback will play a crucial role in shaping college programming\, departmental support\, and potential admissions changes to better support female-identifying and non-binary individuals in the College of Engineering. While the survey is focused on driving meaningful changes within the CoE\, some areas\, like university-wide policies\, may be beyond its direct influence. Nonetheless\, your input will contribute to broader discussions and advocacy efforts across the university. We will report the cumulative results of this survey to your departments\, COE\, as well as Northeastern University.
URL:https://ece.northeastern.edu/event/women-in-engineering-at-northeastern/
LOCATION:Curry Student Center\, 360 Huntington Ave.\, Boston\, MA\, 02115\, United States
GEO:42.3394629;-71.0885286
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Curry Student Center 360 Huntington Ave. Boston MA 02115 United States;X-APPLE-RADIUS=500;X-TITLE=360 Huntington Ave.:geo:-71.0885286,42.3394629
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20241010T170000
DTEND;TZID=America/New_York:20241010T190000
DTSTAMP:20240806T175016Z
CREATED:20240806T175016Z
LAST-MODIFIED:20240806T175016Z
UID:7150-1728579600-1728586800@ece.northeastern.edu
SUMMARY:SOURCE\, the Showcase of Opportunities for Undergraduate Research and Creative Endeavor
DESCRIPTION:Learn more about what cutting-edge research and creative endeavors look like at Northeastern. Talk one-on-one with faculty from across the colleges about their work – and learn how you can get involved in projects during your time at Northeastern. \nSOURCE is a collaboration between Bouvé College of Health Sciences; College of Arts\, Media and Design; College of Engineering; College of Science; College of Social Sciences and Humanities; D’Amore-McKim School of Business; and Khoury College of Computer Science. It is coordinated by Undergraduate Research and Fellowships on behalf of the Office of the Chancellor. \nPlease write to URF@Northeastern.edu with any questions.
URL:https://ece.northeastern.edu/event/source-the-showcase-of-opportunities-for-undergraduate-research-and-creative-endeavor-2/
LOCATION:Curry Student Center\, 360 Huntington Ave.\, Boston\, MA\, 02115\, United States
GEO:42.3394629;-71.0885286
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Curry Student Center 360 Huntington Ave. Boston MA 02115 United States;X-APPLE-RADIUS=500;X-TITLE=360 Huntington Ave.:geo:-71.0885286,42.3394629
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251007T080000
DTEND;TZID=America/New_York:20251007T090000
DTSTAMP:20250917T000657Z
CREATED:20250917T000657Z
LAST-MODIFIED:20250917T000657Z
UID:8023-1759824000-1759827600@ece.northeastern.edu
SUMMARY:Disciplinary Engineering Programs Co-op Overview
DESCRIPTION:During Wonder Week\, you’ll have the chance to learn how the top-ranked Graduate School of Engineering at Northeastern University combines rigorous academics with experiential learning and convergent research. Register for a variety of program-specific webinars throughout the week tailored to your career aspirations and get direct insights from faculty members and current students. Each session includes a 30-minute presentation followed by a Q&A session\, allowing you to directly connect with panelists and presenters. \nThis session will highlight our Disciplinary Engineering Programs Co-op Overview \nTuesday October 7th\, 2025 at 8:00AM ET
URL:https://ece.northeastern.edu/event/disciplinary-engineering-programs-co-op-overview/
LOCATION:Virtual
ORGANIZER;CN="Graduate School of Engineering":MAILTO:coe-gradadmissions@northeastern.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251008T110000
DTEND;TZID=America/New_York:20251008T120000
DTSTAMP:20250917T000745Z
CREATED:20250917T000745Z
LAST-MODIFIED:20250917T000745Z
UID:8027-1759921200-1759924800@ece.northeastern.edu
SUMMARY:Electrical and Computer Engineering Programs Overview
DESCRIPTION:During Wonder Week\, you’ll have the chance to learn how the top-ranked Graduate School of Engineering at Northeastern University combines rigorous academics with experiential learning and convergent research. Register for a variety of program-specific webinars throughout the week tailored to your career aspirations and get direct insights from faculty members and current students. Each session includes a 30-minute presentation followed by a Q&A session\, allowing you to directly connect with panelists and presenters. \nThis session will highlight our Electrical and Computer Engineering Programs Overview \nWednesday October 8th\, 2025 at 11AM ET
URL:https://ece.northeastern.edu/event/electrical-and-computer-engineering-programs-overview/
LOCATION:Virtual
ORGANIZER;CN="Graduate School of Engineering":MAILTO:coe-gradadmissions@northeastern.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251105T100000
DTEND;TZID=America/New_York:20251105T120000
DTSTAMP:20260720T185250Z
CREATED:20251016T135047Z
LAST-MODIFIED:20260720T185250Z
UID:8077-1762336800-1762344000@ece.northeastern.edu
SUMMARY:Digital Twin Models for Semiconductor Manufacturing Unit Process
DESCRIPTION:Join ECE Assistant Professor Benyamin Davaji\, head of the AIMS Lab\, as he explores how digital twin models are redefining semiconductor manufacturing. \n\nSEMI Master Class #27 will feature two thought leaders redefining the future of semiconductor process innovation — Dr. Benyamin Davaji of Northeastern University and Dr. Peter Doerschuk of Cornell University. \nDr. Davaji\, Assistant Professor of Electrical and Computer Engineering at Northeastern and head of the Autonomous Integrated Microsystems (AIMS) Lab\, merges data science\, physics\, and nanofabrication to revolutionize semiconductor manufacturing. His work on digital twin models is reshaping how we simulate\, optimize\, and scale unit processes—from lab-scale experimentation to high-volume production. \nDr. Doerschuk\, Professor of Electrical and Computer Engineering at Cornell University\, brings decades of experience in computational modeling and systems analysis. With degrees in electrical engineering from MIT and an M.D. from Harvard Medical School\, his research bridges computation and biology\, developing advanced models that drive new approaches to sensor signal processing\, pattern recognition\, and data-driven system design. \nTogether\, these speakers will explore how digital twins and computational models are transforming semiconductor process development and enabling smarter\, faster\, and more efficient manufacturing. \nIn this session\, you’ll learn:\n• How digital twins are transforming semiconductor process development\n• Real-world applications of AI and virtual metrology in printed electronics\n• Strategies for integrating computational modeling with physical systems to boost reliability and throughput \nWhether you’re an engineer\, executive\, or technologist\, this Master Class will deliver actionable insights on bridging data\, modeling\, and manufacturing. \nRegister
URL:https://ece.northeastern.edu/event/digital-twin-models-for-semiconductor-manufacturing-unit-process/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Phoenix:20260224T133000
DTEND;TZID=America/Phoenix:20260224T163000
DTSTAMP:20260720T185325Z
CREATED:20260109T220817Z
LAST-MODIFIED:20260720T185325Z
UID:8222-1771939800-1771950600@ece.northeastern.edu
SUMMARY:Digital Twins for Printed Electronics: How Can AI Learn FHE Printing
DESCRIPTION:Benyamin Davaji\, Assistant Professor in the College of Engineering\, alongside Haiyang Yun\, Senior PhD Student\, will instruct a professional course titled “Digital Twins for Printed Electronics: How Can AI Learn FHE Printing” on February 24\, 2026\, from 1:30–4:30 p.m. MT. at FLEX 2026\, the premier international conference for Flexible and Hybrid Electronics (FHE)\, taking place in Phoenix\, Arizona. \nDigital Twin is a virtual representation of the structure\, context\, and behavior of physical systems or a process\, with a live link to a physical system serving as a key enabler for predictive and data-driven optimization. In Printed and Flexible Hybrid Electronics (FHE)\, manufacturing involves multiple interdependent variables—different printing technologies\, inks\, substrates\, and process conditions—each introducing its own complexity. In practice\, additional challenges such as equipment drift\, batch-to-batch variations\, and environmental fluctuations further impact process consistency and yield. Changing a process or transferring it between tools is often difficult\, as each setup is highly customized and sensitive to local conditions. To address these challenges\, Digital Twin frameworks connect data from design\, fabrication\, and metrology into continuously learning digital models. They enable early detection of process drifts\, virtual experimentation for process development\, and data-driven optimization that reduces time\, cost\, and waste. \nThis course introduces Digital Twin frameworks for FHE\, focusing on Deep Neural Network (DNN)-based predictive models. Participants will learn how to integrate design\, fabrication\, and metrology data into continuously learning virtual twins that detect process drifts\, enable virtual experimentation\, and optimize manufacturing. The program covers the full workflow—from image processing and virtual metrology to AI model training\, validation\, and hyperparameter tuning—using real datasets. A hands-on “Build Your Own Digital Twin” module in Google Colab will provide practical experience in training and refining models for printed electronics applications\, equipping attendees with both theoretical insight and applied skills for process optimization and performance prediction. \nFor more information\, visit the FLEX 2026 course page.
URL:https://ece.northeastern.edu/event/digital-twins-for-printed-electronics-how-can-ai-learn-fhe-printing/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260331T153000
DTEND;TZID=America/New_York:20260331T170000
DTSTAMP:20260323T174858Z
CREATED:20260323T174858Z
LAST-MODIFIED:20260323T174858Z
UID:8349-1774971000-1774976400@ece.northeastern.edu
SUMMARY:ECE Distinguished Lecture: Ubiquitous Active Surfaces
DESCRIPTION:ECE DISTINGUISHED LECTURE: Ubiquitous Active Surfaces \nProf. Vladimir Bulović\nProfessor of Emerging Technologies\, MIT\nTuesday\, March 31\n3:30-5:00 PM (ET)\n102 ISEC Auditorium or Teams \nWhat if any surface could generate light\, harvest solar energy\, sense motion\, or emit sound? Paper-thin devices are making this possible — turning walls\, windows\, and everyday objects into active technology. Prof. Bulović will showcase newly invented MIT technologies and the startups bringing them to market. \nAbout the speaker: Founding Director of MIT.nano\, holder of 120+ U.S. patents\, and author of 300+ research articles (cited 70\,000+ times). His lab’s spinouts — including QD Vision\, Ubiquitous Energy\, and Swift Solar — have brought thin-film technology to millions of users worldwide.
URL:https://ece.northeastern.edu/event/ece-distinguished-lecture-ubiquitous-active-surfaces/
LOCATION:102 ISEC\, 360 Huntington Ave\, 102 ISEC\, Boston\, MA\, 02115\, United States
GEO:42.3377335;-71.0869121
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=102 ISEC 360 Huntington Ave 102 ISEC Boston MA 02115 United States;X-APPLE-RADIUS=500;X-TITLE=360 Huntington Ave\, 102 ISEC:geo:-71.0869121,42.3377335
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260406T100000
DTEND;TZID=America/New_York:20260406T110000
DTSTAMP:20260310T174934Z
CREATED:20260310T174934Z
LAST-MODIFIED:20260310T174934Z
UID:8336-1775469600-1775473200@ece.northeastern.edu
SUMMARY:Wonder Week: Electrical and Computer Engineering
DESCRIPTION:During Wonder Week\, you’ll have the chance to learn how the top-ranked Graduate School of Engineering at Northeastern University combines rigorous academics with experiential learning and convergent research. You’ll also see how our unique learning model better prepares the next generation of engineering leaders to address the complex challenges of global society. \nPrograms discussed in this webinar include electrical and computer engineering and data science.
URL:https://ece.northeastern.edu/event/wonder-week-electrical-and-computer-engineering/
LOCATION:Virtual
ORGANIZER;CN="Graduate School of Engineering":MAILTO:coe-gradadmissions@northeastern.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260531T100000
DTEND;TZID=America/New_York:20260531T150000
DTSTAMP:20260720T185433Z
CREATED:20260326T154318Z
LAST-MODIFIED:20260720T185433Z
UID:8365-1780221600-1780239600@ece.northeastern.edu
SUMMARY:Digital Twins for MEMS Manufacturing
DESCRIPTION:Northeastern University College of Engineering will be at the forefront of one of the MEMS and Microsystem community’s most anticipated gatherings this summer. Benyamin Davaji \, Assistant Professor in the College of Engineering\, is co-organizing a full-day workshop on Digital Twins for MEMS Manufacturing at Hilton Head 2026 \, taking place May 31–June 4 at the Sonesta Resort on Hilton Head Island\, South Carolina. The workshop\, co-organized with Prof. Gary Fedder of Carnegie Mellon University\, will open the conference on Sunday\, May 31\, from 10:00 a.m. to 3:00 p.m. \nHilton Head 2026 is the 22nd edition of the biennial Workshop on the Science and Technology of Solid-State Sensors\, Actuators\, and Microsystems\, a multidisciplinary gathering that draws researchers from academia\, industry\, and government worldwide. \nThe Digital Twins for MEMS Manufacturing workshop reflects growing momentum around smarter\, more adaptive and process-aware MEMS and semiconductor fabrication. The session will explore how digital twins — virtual representations of physical manufacturing systems — differ from conventional modeling approaches and will cover topics including automated critical-dimension extraction\, virtual metrology using equipment data\, and the integration of AI methods such as agentic approaches for iterative process and design optimization. A panel with industry participants will spotlight unmet needs and pathways for collaboration\, making the workshop a bridge between academic research and real-world manufacturing challenges. \nThis workshop builds on research from Northeastern’s Autonomous Integrated Microsystems (AIMS) Laboratory \, which develops AI-driven digital twin frameworks to connect design\, fabrication\, and metrology data in continuously learning systems. The session at Hilton Head 2026 represents an opportunity to share this work with the broader MEMS community and shape the conversation around data infrastructure\, interoperability\, and standards that will define the next generation of MEMS manufacturing. \nAbstract: Recent interest in semiconductor digital twins is driven by the expectation of accelerating process development\, enabling device – technology design co-optimization\, and supporting agile manufacturing workflows. This participatory workshop examines digital twins for MEMS from a manufacturing-centered perspective\, emphasizing how they differ from traditional modeling and simulation. Topics include an introduction to digital twin concepts with examples from MEMS-relevant processes\, automated CD extraction and process characterization\, virtual metrology using equipment data\, and the challenges of extending these approaches across complete process flows. The workshop will also introduce the role of digital twins in enabling AI methods relevant to MEMS workflows\, including agentic approaches for process and design iteration. Commercial tools and current capabilities will be reviewed\, followed by a discussion of gaps in data infrastructure\, interoperability\, and standards. A panel with industry participants will highlight unmet needs and opportunities for research and collaboration in MEMS digital twins. \nFor more information on the workshop program\, visit https://www.hh2026.org/events/sunday_workshops.html. \n 
URL:https://ece.northeastern.edu/event/digital-twins-for-mems-manufacturing/
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20260929
DTEND;VALUE=DATE:20261001
DTSTAMP:20260924T120450Z
CREATED:20260924T115132Z
LAST-MODIFIED:20260924T120450Z
UID:8671-1790640000-1790812799@ece.northeastern.edu
SUMMARY:NANOscientific Symposium Americas 2026
DESCRIPTION:Join us for the NANOscientific Symposium Americas 2026\, taking place September 29 at EXP Northeastern University and September 30 at Northeastern University’s Innovation Campus in Burlington\, MA. NSS Americas 2026 brings together research\, instrumentation\, and collaborative frameworks to advance developments in nanoscale discovery. \nSeptember 29 (Day 1)\nIn-person event with live online streaming\, featuring high-level speakers and technical talks. \nSeptember 30 (Day 2)\nLive\, hands-on tool training with the Park Atomic Force Microscopes\, Accurion EP4 Imaging Spectroscopic Ellipsometer\, and Lyncée Digital Holographic Microscope. The speaker lineup and registration details will be announced in the forthcoming official program. \nBenyamin Davaji\, of Northeastern University’s Autonomous Integrated Microsystems (AIMS) Laboratory\, plays a leading role in the event — delivering the symposium’s welcome remarks and presenting a technical talk\, “Measure\, Learn\, Predict\, and Act: Metrology-Enabled Digital Twins for Autonomous Nanomanufacturing.” \nDavaji joins a lineup of speakers from Stanford University\, the University of Pennsylvania\, the University of Illinois Urbana-Champaign\, MIT\, the University of Toronto\, and the University of North Carolina at Chapel Hill\, alongside Park Systems leadership. Topics span atomic force microscopy\, 2D materials\, semiconductor failure analysis\, and advanced optical metrology. Day two offers live\, hands-on training with Park Atomic Force Microscopes\, the Accurion EP4 Imaging Spectroscopic Ellipsometer\, and a Lyncée digital holographic microscope.
URL:https://ece.northeastern.edu/event/nanoscientific-symposium-americas-2026/
LOCATION:EXP\, 815 Columbus Avenue\, Boston\, MA\, 02120\, United States
CATEGORIES:use the department, audience, and topic lists
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20261112T173000
DTEND;TZID=America/New_York:20261112T203000
DTSTAMP:20260901T195753Z
CREATED:20260901T195753Z
LAST-MODIFIED:20260901T195753Z
UID:8581-1794504600-1794515400@ece.northeastern.edu
SUMMARY:Boston Physical AI Workshop and Meetup
DESCRIPTION:Join us at Northeastern University on November 12th for the Boston Physical AI Workshop and Meetup\, co-presented by Nebius and Voxel51. \nThe evening kicks off meetup-style with lightning talks from local speakers working across AI\, machine learning\, and computer vision\, followed by a hands-on workshop on building physical AI applications with FiftyOne\, from curating robotics and sensor datasets to evaluating vision models on real-world data. \nA laptop is required to participate in the hands-on workshop – please bring one. Space is limited\, so register early. \nLocation: Northeastern University\, 140 Fenway\, Room 378\, Boston\, MA 02115 \n\nNetworking and Meetup\n5:30–6:30 PM\n\nNetworking\, food\, drinks\, and lightning talks\n\nWorkshop Agenda\nThis hands-on session uses DROID\, a real-world robotics dataset loaded into FiftyOne as a native multimodal MCAP recording\, and YOLO11n\, fine-tuned live during the session.\n6:30–7:30 PM\n\nWelcome + framing: from raw robot logs to a trained detector\nExplore a real DROID robotics recording in FiftyOne’s native multimodal MCAP viewer: camera\, proprioception\, and language on one synced timeline\, no ROS install required\nCurate: extract and browse frames from the recording\, filter and deduplicate\nCompute embeddings on the curated frames; explore via similarity search and embeddings visualization (via Nebius Serverless AI Jobs)\n\n7:30–8:00 PM\n\nAuto-label: open-vocabulary detection to generate bounding boxes for the robot gripper and target objects\nTrain: fine-tune a YOLO11n detector on the auto-labeled frames (via Nebius Serverless AI Jobs)\nEvaluate results and close the loop: view predictions back on the original MCAP timeline\n\n8:00–8:30 PM\n\nWhat else Nebius offers: Token Factory walkthrough — chat/vision models\, fine-tuning\, credits
URL:https://ece.northeastern.edu/event/boston-physical-ai-workshop-and-meetup/
END:VEVENT
END:VCALENDAR