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X-WR-CALDESC:Events for Northeastern University College of Engineering
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BEGIN:VEVENT
DTSTART;VALUE=DATE:20210317
DTEND;VALUE=DATE:20210422
DTSTAMP:20260510T014345
CREATED:20210318T134829Z
LAST-MODIFIED:20210318T134829Z
UID:25081-1615939200-1619049599@coe.northeastern.edu
SUMMARY:Study Recruitment: Ancient Techniques and Mental Health Today
DESCRIPTION:Northeastern Department of Philosophy & Religion  \nHave you been experiencing stress and anxiety? \nYou may be eligible to participate in our study! \nHelp us investigate the impact of mindfulness on various life outcomes! All components of this study will take place virtually; participants will be asked to attend two 30-minute Zoom sessions in addition to up to 5 weeks of short\, daily smartphone tasks. \nYou must be 18 years or older\, a Boston-based Northeastern undergraduate student\, and a native English speaker to be eligible to participate. \nParticipants will receive $80 in compensation. \nContact us at pwolstudy@gmail.com if you’re interested and to see if you are eligible! \nThis study has been reviewed and approved by the Northeastern University Institutional Review Board (#21-02-21).
URL:https://coe.northeastern.edu/event/study-recruitment-ancient-techniques-and-mental-health-today/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210421T090000
DTEND;TZID=America/New_York:20210421T100000
DTSTAMP:20260510T014345
CREATED:20210420T135730Z
LAST-MODIFIED:20210420T135730Z
UID:25484-1618995600-1618999200@coe.northeastern.edu
SUMMARY:ECE MS Thesis Defense: Rubens Lacouture
DESCRIPTION:MS Thesis Defense: GPUBLQMR: GPU-Accelerated Sparse Block Quasi-Minimum Residual Linear Solver \nRubens Lacouture \nLocation: Zoom Link \nAbstract: Solutions of linear systems of equations is the central point of many scientific and engineering research problems across a variety of domains. In many cases\, the solution of linear systems can even take most of the simulation time which presents a huge computational bottleneck issue. This can hinder the scalability of various scientific software hindering for larger problems. For large-scale simulations\, this can result in having to find the solutions of millions of unknowns\, making this an ideal problem to exploit parallelism to improve performance.\nPreconditioned Krylov subspace methods have proven effective and robust in various applications. The block Quasi-Minimum Residual (BLQMR) method as developed by Boyse et al. has been shown to be efficient for solving systems of equations with multiple righthand sides. This method is based on the conventional Quasi-Minimum Residual (QMR) method which is generalized using the block Lanczos algorithm to solve multiple solutions simultaneously. In particular\, it is shown that this method accelerates the convergence behavior based on the set number of righthand sides\, grouped to be solved simultaneously. Block iterative solver methods are often characterized by a high degree of parallelism.\nIn this thesis\, we show how BLQMR can be successfully implemented on a distributed memory computer taking advantage of Graphics Processing Units (GPU) accelerators. We leveraged the processing power of GPUs to show how the proposed GPU-accelerated BLQMR approach can out-perform state-of-the-art linear solvers and results in an ideal behavior for solving challenging linear algebra problems through data from various numerical experiments. The library code developed in this work is written using the CUDA framework. The performance of the parallel algorithm is optimized using several CUDA optimization strategies and the speedup of the parallel GPU implementation over the existing sequential CPU implementations is reported.
URL:https://coe.northeastern.edu/event/ece-ms-thesis-defense-rubens-lacouture/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210421T100000
DTEND;TZID=America/New_York:20210421T110000
DTSTAMP:20260510T014345
CREATED:20210420T140528Z
LAST-MODIFIED:20210420T140528Z
UID:25499-1618999200-1619002800@coe.northeastern.edu
SUMMARY:ECE MS Thesis Defense: Matin Raayai Ardakani
DESCRIPTION:MS Thesis Defense: A Framework for Denoising Two and Three-dimensional Monte CarloPhoton Transport Simulations Using Convolutional Neural Networks \nMatin Raayai Ardakani \nLocation: Zoom Link \nAbstract: The Monte Carlo (MC) method is considered to be the gold standard for modeling light propagation inside turbid media\, proving superior to other Radiative Transfer Equation (RTE) solvers relying on variational principles. However\, like most MC-based algorithm\, a large number of independently launched photons is needed for converging to the correct result and combating its inherent stochastic noise\, yielding longer computation times\, even when accelerated on GraphicProcessing Units (GPUs).\nTo remove this noise from the output without increasing the number of photons used for simulation\, modified versions of commonly used filters for image and volumetric data based on non-local self similarity has been used in the past. Current state-of-the-art denoising approaches rely on Convolutional Neural Networks (CNN) to remove spatially variant noise\, but the high dynamic range of MC simulations has hindered their adaptation to remove MC noise.\nIn this thesis\, we address this problem by presenting a supervised framework for using CNNs to denoise MC simulations. First\, a dataset is created with each entry comprising of a unique configuration simulated with different numbers of photons. The simulation configurations are generated using a simple generative model that introduces objects with both smooth and sharp edges into the volume. By selecting the group of fluence maps simulated with the maximum number of photons in the dataset as labels\, we train a range of CNN-based models to learn the underlying mapping between noisy and clean images. The CNN input is converted to log scale and normalized to reduce the high dynamic range\, and converted back after inference. The trained CNNs are then shown to have better performance compared to using an Adaptive Non-local Means filter\, in terms of mean square error (MSE)\, structural similarity index (SSIM)\, and peak signal-to-noise ratio (PSNR) in the image domain.\nFinally\, we purpose our own architecture that combines DnCNN and UNet\, a strategy that can learn both local and global residual noise maps\, achieving state-of-the-art performance compared to existing CNN methods. Future avenues of research and challenges for denoising 3D simulations are also discussed.
URL:https://coe.northeastern.edu/event/ece-ms-thesis-defense-matin-raayai-ardakani/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210421T100000
DTEND;TZID=America/New_York:20210421T170000
DTSTAMP:20260510T014345
CREATED:20210406T170701Z
LAST-MODIFIED:20210406T170701Z
UID:25342-1618999200-1619024400@coe.northeastern.edu
SUMMARY:MS Thesis Defense: Yuezhou Liu
DESCRIPTION:MS Thesis Defense: Optimizations of Caching Networks: Fairness and Application to Mobile Networks \nYuezhou Liu \nLocation: Zoom Link \nAbstract: In-network caching is playing a more and more important role in today’s network architectures\, because of the explosive growth of data traffic due to the proliferation of mobile devices and demands for high-volume media content\, as well as the development of low-latency applications\, such as VR/AR and cloud gaming. The replication of popular contents in the caches that located closer to end users than central servers\, can significantly reduce backbone traffic\, benefit request latency\, and balance the load of central servers. In this thesis\, we study two problems in the field of network caching. In the first part\, we consider fair caching policies in caching networks with arbitrary topology. We introduce a utility maximization framework to find a caching decision that reduces aggregate expected request routing cost in the network while taking fairness issues into consideration. The utility maximization problem is NP-hard\, and we propose two efficient approximation algorithms to solve it. In the second part\, we study how caching may affect user association in mobile networks. We jointly optimize the user association decision and caching at both base stations (BSs) and gateways (GWs). The resulting problem is also NP-hard. We propose a polynomial-time algorithm based on concave approximation and pipage rounding that produces a solution within a constant factor of 1-1/e from the optimal. Simulation results show that the proposed algorithm outperforms schemes that combine cache-independent user association methods with traditional caching strategies (e.g.\, LRU) in terms of minimizing the aggregate expected routing cost and backhaul traffic while achieving a high data sum rate in the access network.
URL:https://coe.northeastern.edu/event/ms-thesis-defense-yuezhou-liu/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210421T120000
DTEND;TZID=America/New_York:20210421T130000
DTSTAMP:20260510T014345
CREATED:20210420T140408Z
LAST-MODIFIED:20210420T140408Z
UID:25497-1619006400-1619010000@coe.northeastern.edu
SUMMARY:ChE Seminar Series: Biomaterials to unlock the regenerative capacity of tissues
DESCRIPTION:ChE Seminar Series Presets: Dr. Tatiana Segura \nTatiana Segura\, PhD \nProfessor of Biomedical Engineering\, Duke University \nBiomaterials to unlock the regenerative capacity of tissues \nAbstract: Injectable materials that can conform to the shape of a desired space are used in a variety of fields including medicine. The ability to fill a tissue defect with an injectable material can be used for example to deliver drugs\, augment tissue volume\, or promote repair of an injury. This talk will explore the development of injectable materials that are based on assembled particle building blocks\, for tissue repair. We find that using microparticle building blocks to build the scaffold generates a porous network by the space left behind between adjacent building blocks. Due to the injectability of this microporous material we have explored its wide applicability to tissue repair applications ranging from skin to brain wounds. In this talk\, I will describe how MAP scaffolds can modulate the wound healing immune response and lead to regenerative wound healing. \nBiography: Professor Tatiana Segura received her BS degree in Bioengineering from the University of California Berkeley and her doctorate in Chemical Engineering from Northwestern University. Her graduate work in designing and understanding non-viral gene delivery from hydrogel scaffolds was supervised by Prof. Lonnie Shea. She pursued post-doctoral training at the Swiss Federal Institute of Technology\, Lausanne under the guidance of Prof. Jeffrey Hubbell\, where her focus was self-assembled polymer systems for gene and drug delivery. Professor Segura’s Laboratory studies the use of materials for minimally invasive in situ tissue repair. On this topic\, she has published 113 peered reviewed publications to date. She has been recognized with the Outstanding Young Investigator Award from the American Society of Gene and Cell Therapy\, the American Heart Association National Scientist Development Grant\, and the CAREER award from National Science Foundation. She was Elected to the College of Fellows at the American Institute for Medical and Biological Engineers (AIMBE) in 2017. She spent the first 11 years of her career at UCLA department of Chemical and Biomolecular Engineering and has recently relocated to Duke University\, where she holds appointments in Biomedical Engineering\, Neurology and Dermatology.
URL:https://coe.northeastern.edu/event/che-seminar-series-biomaterials-to-unlock-the-regenerative-capacity-of-tissues/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210421T173000
DTEND;TZID=America/New_York:20210421T173000
DTSTAMP:20260510T014345
CREATED:20210421T153821Z
LAST-MODIFIED:20210421T153821Z
UID:25541-1619026200-1619026200@coe.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Muhamed Yildiz
DESCRIPTION:PhD Dissertation Defense: Interpretable Machine Learning for Retinopathy of Prematurity \nMuhamed Yildiz \nLocation: Zoom Link \nAbstract: Retinopathy of Prematurity (ROP)\, a leading cause of childhood blindness\, is diagnosed by clinical ophthalmoscopic examinations or reading retinal images. Plus disease\, defined as abnormal tortuosity and dilation of the posterior retinal blood vessels\, is the most important feature to determine treatment-requiring ROP. State-of-the-art ROP detection systems employ convolutional neural networks (CNNs) %\cite{brown2018automated} and achieve up to $0.947$ and $0.982$ area under the ROC curve (AUC) in the discrimination of \textit{normal} and \textit{plus} levels of ROP. However\, due to their black-box nature\, clinicians are reluctant to trust diagnostic predictions of CNNs.\nFirst\, we aim to create an interpretable\, feature extraction-based pipeline\, namely\, I-ROP ASSIST\, that achieves CNN like performance when diagnosing plus disease from retinal images. Our method segments retinal vessels\, detects the vessel centerlines. Then\, our method extracts features relevant to ROP\, including tortuosity and dilation measures\, and uses these features for classification via logistic regression\, support vector machines and neural networks to assess a severity score for the input. For predicting \textit{normal} and \textit{plus} levels of ROP on a dataset containing 5512 posterior retinal images\, we achieve $0.88$ and $0.94$ AUC\, respectively. Our system combining automatic retinal vessel segmentation\, tracing\, feature extraction and classification is able to diagnose plus disease in ROP with CNN like performance.\nThen\, we introduce a novel method for extracting tortuosity features. Current feature extraction pipelines of retinal image analysis systems extract tortuosity features based on the derivatives of vessel centerlines or a segment of a vessel. Our method eliminates the need for finding vessel centerlines by introducing a method for calculating curvature at each pixel in the fundus image. When calculating curvature\, we use the geometric interpretation of eigenvectors of the Hessian of an interpolation function. By selecting an appropriate interpolation function\, our method can be applied in many domains\, including corner detection\, noise removal and image registration. We present the results of our method on artificial images that contains curved structures such as circle\, sine waves as well as real images from MNIST and our retinal fundus image dataset. Experimental results shows that our model accurately captures the high curvature parts of the blood vessels. \nFurthermore\, we aim to address the interpretability problem of CNN-based ROP detection system. Incorporating visual attention capabilities in CNNs enhances interpretability by highlighting regions in the images that CNNs utilize for prediction. Generic visual attention methods do not leverage structural domain information such as tortuosity and dilation of retinal blood vessels in ROP diagnosis. We propose the Structural Visual Guidance Attention Networks (SVGA-Net) method\, that leverages structural domain information to guide visual attention in CNNs. SVGA-Net achieves $0.979$ and $0.987$ AUC to predict \textit{normal} and \textit{plus} levels of ROP. Moreover\, SVGA-Net consistently results in higher AUC compared to visual attention CNNs without guidance\, baseline CNNs\, and CNNs with structured masks.
URL:https://coe.northeastern.edu/event/ece-phd-dissertation-defense-muhamed-yildiz/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210422T090000
DTEND;TZID=America/New_York:20210422T100000
DTSTAMP:20260510T014345
CREATED:20210420T175252Z
LAST-MODIFIED:20210420T175252Z
UID:25524-1619082000-1619085600@coe.northeastern.edu
SUMMARY:ECE MS Thesis Defense: Peter Kelly
DESCRIPTION:MS Thesis Defense: Design of a Thruster-assisted Bipedal Robot \nPeter Kelly \nLocation: Zoom Link \nAbstract: During the past few years\, legged robot technology has been rapidly advancing.\nHowever\, even the most advanced bipedal legged robots are susceptible to strong disturbances and slippery or impassible terrain. By introducing thrusters to enable hybrid legged-aerial locomotion\, these problems can be circumvented by increasing a robot’s stability and allowing it to jump over obstacles. Harpy is a bipedal robot with eight actuators and two thrusters that serves as a hardware platform for developing control algorithms to advance research in thruster assisted bipedal legged locomotion. This thesis explores the conception\, simulation\, and electromechanical design process of the robot\, which prioritizes thrust-to-weight ratio\, impact resistance\, power density\, and modularity. The fabrication process of actuators and the leg which enable the robot to be both light and strong and testing of the leg design and thrusters is also discussed.
URL:https://coe.northeastern.edu/event/ece-ms-thesis-defense-peter-kelly/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210422T100000
DTEND;TZID=America/New_York:20210422T110000
DTSTAMP:20260510T014345
CREATED:20210414T173301Z
LAST-MODIFIED:20210414T173301Z
UID:25443-1619085600-1619089200@coe.northeastern.edu
SUMMARY:ECE MS Thesis Defense: Tianhong Xu
DESCRIPTION:MS Thesis Defense: A novel simple power analysis (SPA) attack on Elliptic Curve Cryptography (ECC) \nTianhong Xu \nLocation: Zoom Link \nAbstract: Elliptic Curve Cryptography (ECC)\, as a widely used public-key cryptography\, is vulnerable to simple power analysis(SPA) attacks. There are many countermeasures against simple power analysis(SPA) attacks on ECC implementation\, the Always-add algorithm is one of the most popular countermeasures. This research proposes a new SPA attack which is effective to the ECC encrypting implemented with Always-add algorithm\, it uses deep-learning tools and statistical method to retrieve a secret key from only one EM trace collected from a ASIC circuit running ECC encryption.
URL:https://coe.northeastern.edu/event/ece-ms-thesis-defense-tianhong-xu/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210422T100000
DTEND;TZID=America/New_York:20210422T110000
DTSTAMP:20260510T014345
CREATED:20210420T140653Z
LAST-MODIFIED:20210420T140709Z
UID:25501-1619085600-1619089200@coe.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Seyedmehdi Sadeghzadeh
DESCRIPTION:PhD Dissertation Defense: Physical Layer Security in Multi-User Wireless Networks: Impact of Interference and Artificial Noise on Large-Scale Analysis \nSeyedmehdi Sadeghzadeh \nLocation: Zoom Link \nAbstract: In this thesis\, we study the physical layer security in downlink multi-user wireless networks. Traditionally\, security has been addressed by cryptography at the higher layers of the communication stack. Security at the physical layer has been a major research topic in recent years. We study two different precoder designs alongside artificial noise (AN) to mitigate multi-user interference and deteriorate reception at the eavesdropper (Eve). We study the large scale analysis to calculate the secrecy sum-rate for these two cases and analyze the effect of AN on the system. First\, we consider the worst case scenario\, when eavesdropper’s (Eve’s) rate is not deteriorated by the interference caused by the legitimate users. Later\, we investigate how interference from legitimate users would affect the large scale security sum rate. At the end\, we assume more practical situation where the channel state information at the transmitter is not perfect due to feedback limitation and estimation error.
URL:https://coe.northeastern.edu/event/ece-phd-dissertation-defense-seyedmehdi-sadeghzadeh/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210422T103000
DTEND;TZID=America/New_York:20210422T113000
DTSTAMP:20260510T014345
CREATED:20210405T134659Z
LAST-MODIFIED:20210405T134659Z
UID:25307-1619087400-1619091000@coe.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Linbin Chen
DESCRIPTION:PhD Dissertation Defense: Low Power Designs using Approximate Computing and Emerging Memories at Nanoscales \nLinbin Chen \nLocation: Zoom Link \nAbstract: A power efficient integrated circuit design is essential for mobile and embedded computer systems. This dissertation proposes several novel low power designs using approximate computing and emerging memories for computers with arithmetic circuits and large on-chip caches. Initially\, low power approximate designs are proposed both for fixed point radix-2 and high-radix division at circuit-level. Then\, an approximate parallel CORDIC algorithm and its hardware implementation are developed. Trade-offs between circuit metrics and error characteristics are pursued by simulation and analysis. The proposed approximate arithmetic designs have excellent performance for image processing applications while significantly reducing power consumption. Then\, hybrid cache designs integrating SRAM with emerging memories are also investigated. An intra-cell\, as well as inter-subarray and inter-bank hybrid caches with SRAM\, eDRAM and NVM (such as PCM or STT-MRAM) are proposed. Architectural level approaches such as special migration structures and policies are designed to address the eDRAM refresh requirements and the NVM large write latency issue. An analytical circuit-level model based on NVsim focusing on hybrid granularity and an architecture level model based on gem5 focusing on a migration policy are developed. To explore the hybrid cache’s benefits to main memory\, a combined-cache design for addressing endurance issues of multi-level non-volatile memory in embedded system is proposed. It is shown that these hybrid cache designs exhibit smaller area and lower leakage than conventional designs so with great potential to be used for large-capacity on-chip caches in mobile and embedded systems.
URL:https://coe.northeastern.edu/event/ece-phd-dissertation-defense-linbin-chen/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210422T103000
DTEND;TZID=America/New_York:20210422T113000
DTSTAMP:20260510T014345
CREATED:20210420T152140Z
LAST-MODIFIED:20210420T152140Z
UID:25521-1619087400-1619091000@coe.northeastern.edu
SUMMARY:The Weird World of Quantum Matter
DESCRIPTION:JOINT SPECIAL COLLOQUIUM BY COLLEGE OF SCIENCE AND COLLEGE OF ENGINEERING\nThe Weird World of Quantum Matter\nProfessor Prineha Narang\, Harvard University \nQuantum materials host many spectacular functionalities enabled by their unusual excited-state and nonequilibrium quantum effects. Understanding these phenomena that involve a variety of time and length scales has remained elusive. My research focuses on addressing this grand challenge by developing next-generation\, predictive theoretical and computational approaches at the frontiers of quantum science and engineering [1-3] and paves the way for technologies ranging from scalable quantum information processing and networks\, to ultra-high efficiency optoelectronic and energy conversion systems. I will discuss how this research is helping unravel the microscopic dynamics\, decoherence and optically excited collective phenomena in quantum matter. I will also present selected examples of our ab initio design and control of active defects in quantum materials and our predictions of linear and nonlinear dynamics and transport in topological semimetals. Finally\, I will comment on driving quantum matter far out-of-equilibrium to control complex coupled degrees-of-freedom. \nWebsite: narang.seas.harvard.edu \nDr. Narang received her MS and PhD in Applied Physics from Caltech. She has received many awards including an NSF CAREER award. She has been named a Moore Inventor Fellow by the Gordon and Betty Moore Foundation\, CIFAR Azrieli Global Scholar by the Canadian Institute for Advanced Research\, a Top Innovator by MIT Tech Review (MIT TR35)\, and a Young Scientist by the World Economic Forum. \nZoom meeting link:\nhttps://northeastern.zoom.us/j/97384220271
URL:https://coe.northeastern.edu/event/the-weird-world-of-quantum-matter/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210422T123000
DTEND;TZID=America/New_York:20210422T133000
DTSTAMP:20260510T014345
CREATED:20210414T173642Z
LAST-MODIFIED:20210414T173642Z
UID:25437-1619094600-1619098200@coe.northeastern.edu
SUMMARY:MS Thesis Defense: Duschia Bodet
DESCRIPTION:MS Thesis Defense: Modulations to Exploit the THz Band \nDuschia Bodet \nLocation: Zoom Link \nAbstract: Terahertz (THz)-band (0.1-10 THz) communication has been envisioned as a key technology to enable wireless Terabit-per-second (Tbps) links. At THz frequencies\, the path-loss is governed by the spreading loss and the molecular absorption loss. The latter also determines the available transmission bandwidth\, which drastically shrinks with distance. As a result\, traditional modulation schemes cannot fully take advantage the THz channel\, and new modulation schemes are needed if THz channel communications are going to reach their full potential. Several solutions have been presented including Hierarchical Bandwidth Modulations (HBM)\, which is the only presented work that not only compensates for molecular absorption losses but leverages those losses to improve the capabilities of the system. The focus of this thesis is two-fold. First the design of HBM is formalized\, exploring the trade-offs and its achievable performance as a function of different system parameters. Secondly\, these trade-offs and performance metrics are verified using a one-of-a-kind experimental testbed for ultrabroadband communication networks. The results show that with proper design HBM successfully achieves its goal of exploiting the distance-dependent characteristics of the THz channel.
URL:https://coe.northeastern.edu/event/ms-thesis-defense-duschia-bodet/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210422T140000
DTEND;TZID=America/New_York:20210422T150000
DTSTAMP:20260510T014345
CREATED:20210421T192817Z
LAST-MODIFIED:20210421T192817Z
UID:25554-1619100000-1619103600@coe.northeastern.edu
SUMMARY:Order of the Engineer Ring Ceremony
DESCRIPTION:This year the College of Engineering is pleased to invite the Class of 2020 to join the Class of 2021 in a combined virtual Order of the Engineer Ring Ceremony. Eligible students will be inducted into the Order\, receive a ring\, and a certificate.\nThe Order of the Engineer is an organization composed of engineers within the United States who have publicly accepted the obligation of an Engineer\, a formal statement of an engineer’s responsibilities to both the public and the profession. As part of this ceremony\, you will receive a stainless-steel ring\, which symbolizes and reminds engineers of their obligation to serve the public and demonstrates the common bond engineers share. \nZoom link: https://northeastern.zoom.us/j/94311514917
URL:https://coe.northeastern.edu/event/order-of-the-engineer-ring-ceremony/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210422T150000
DTEND;TZID=America/New_York:20210422T163000
DTSTAMP:20260510T014345
CREATED:20210412T133519Z
LAST-MODIFIED:20210413T162110Z
UID:25377-1619103600-1619109000@coe.northeastern.edu
SUMMARY:Plant Shift Initiative | Speaker Series: Engineering & Climate Correction
DESCRIPTION:There is no debate that in order to combat climate change we need to think of creative solutions. Join us on Earth Day\, April 22nd\, to hear more about the Plant Shift Initiative which is dedicated to spark new “plant-based” ideas in all forms of productions and activities. \nFor decades\, entrepreneurs and leaders have invented new systems to minimize the carbon footprint in every part of our lives. To kick off this series\, we will hear from disrupters Sebastiano Cossia Castiglioni\, PNT’23\, Co-Founder and Director of Natural Order Acquisition Corp.\, Dale Vince\, Founder of Ecotricity\, and Paul Watson\, Founder and Chief Executive Officer of Sea Shepherd. They will share with you how they engineered new designs and systems within their industries to be more plant-based. \nRegister
URL:https://coe.northeastern.edu/event/plant-shift-initiative-speaker-series-engineering-climate-correction/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210423T130000
DTEND;TZID=America/New_York:20210423T140000
DTSTAMP:20260510T014345
CREATED:20210421T154056Z
LAST-MODIFIED:20210421T154056Z
UID:25545-1619182800-1619186400@coe.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Lichen Wang
DESCRIPTION:PhD Dissertation Defense: Correlation Discovery for Multi-view and Multi-label Learning \nLichen Wang \nLocation: Zoom Link \nAbstract: Correlation indicates the interactions or connections across different instances. It exists in a wide range of real-world applications such as social network\, scene understanding\, and time-series data analysis. Correlation provides the unique and informative knowledge to reveal the connections across instances\, and it plays an essential and important role in machine learning field. However\, recovering and utilizing correlation is challenging. First\, it is hard to explicitly define and understand the correlations. Second\, there are not sufficient datasets which contain the well-labeled task-specific correlations. Third\, how to efficiently utilize the learned correlations for other down-stream tasks have not been well-explored.\nIn this dissertation research\, we investigate the techniques to effectively discover various kinds of correlations in machine learning tasks including multi-view learning\, multi-label learning\, image/scene understanding\, time-series data analysis\, human action recognition\, and graph representation learning. Specifically\, we propose algorithms from the following perspectives: (1) designing an advanced correlation discovery network to automatically explore the label correlations in multi-label scenarios\, (2) proposing a multi-view fusion strategy which effectively dig the latent correlations across different views\, (3) exploring the correlations and structural knowledge from graph structured objects in an inductive and unsupervised scenario. To demonstrate the effectiveness of the proposed algorithms\, various experiments on commonly used datasets have been implemented and the results shows the superiority of our algorithms over the other state-of-the-art methods.
URL:https://coe.northeastern.edu/event/ece-phd-dissertation-defense-lichen-wang/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210426T100000
DTEND;TZID=America/New_York:20210426T110000
DTSTAMP:20260510T014345
CREATED:20210420T141019Z
LAST-MODIFIED:20210420T141019Z
UID:25506-1619431200-1619434800@coe.northeastern.edu
SUMMARY:ECE MS Thesis Defense: Anran Wei
DESCRIPTION:MS Thesis Defense: A soft-switching non-inverting buck-boost converter \nAnran Wei \nLocation: Zoom Link \nAbstract: There are numerous applications in which DC-DC converters with wide range of voltage gain are required. Non-inverting buck-boost converter is a classical topology that can provide wide range of voltage conversion and bidirectional power transfer; thus\, it is frequently used in industrial applications. However\, the conventional hard-switching configuration\, which transfers power through a link inductor\, can only reach a high voltage conversion ratio at the expense of low efficiency due to switching loss. This thesis proposes a soft switching non-inverting buck-boost converter. This converter uses a small film capacitor in parallel with the link inductor to provide zero voltage switching (ZVS) by allowing the link capacitor and link inductor resonate between power transfer states. Principles of the operation of this converter are presented in this thesis and its performance is evaluated through simulations and experiments.
URL:https://coe.northeastern.edu/event/ece-ms-thesis-defense-anran-wei/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210427T110000
DTEND;TZID=America/New_York:20210427T120000
DTSTAMP:20260510T014345
CREATED:20210415T171755Z
LAST-MODIFIED:20210415T171755Z
UID:25472-1619521200-1619524800@coe.northeastern.edu
SUMMARY:Rare Earth Element-Based Magnets: Science\, Supply and Sustainability in 2021 and Beyond
DESCRIPTION:University Distinguished Professor Vincent Harris is presenting “Rare Earth Element-Based Magnets: Science\, Supply and Sustainability in 2021 and Beyond” as part of the Jefferson Science Fellowship Program of the National Academies of Sciences and Engineering. \nRegistration is required in advance of the lecture: Register here \nRare earth elements (REEs) and their supply chain have become topics of great interest to the U.S. diplomatic and national security communities. Presently\, China dominates REE markets in all facets of processing from earth extraction to metals as well as value and commercialization verticals. Beijing has shown no hesitancy in using its position of market dominance to advance its broader political goals and agendas. \nIn this presentation\, we focus on REE-based magnets and associated challenges faced in 2021. We explore REE science and applications\, supply and policy\, and sustainability and environmental impact. We examine what the future holds in terms of alternative sources\, recycling\, and the practice of designing components around the need to employ REEs. Finally\, we report on steps taken by the global community to offset China’s monopoly on rare earths.
URL:https://coe.northeastern.edu/event/rare-earth-element-based-magnets-science-supply-and-sustainability-in-2021-and-beyond/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210427T150000
DTEND;TZID=America/New_York:20210427T160000
DTSTAMP:20260510T014345
CREATED:20210421T153929Z
LAST-MODIFIED:20210421T153929Z
UID:25543-1619535600-1619539200@coe.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Yue Zheng
DESCRIPTION:PhD Dissertation Defense: Modular Plug-and-Play Photovoltaic Subpanel System \nYue Zheng \nLocation: Zoom Link \nAbstract: This thesis designs\, builds and tests plug-and-play photovoltaic (PV) panels. A prototype modular PV system is built consisting of a dozen small PV units that can slide in and out of a mechanical frame without impacting other units. Each unit contains one PV subpanel and a DC-DC converter with a distributed maximum power point tracking (dMPPT) control board. Each PV unit works at its maximum power\, while every output of the converter is connected in parallel to a DC bus. A new combined control strategy is proposed in which the decision to use centralized or distributed control depends on the system efficiency at the varying load operating points. A disadvantage of this dMPPT structure is that in each PV unit\, the DC-DC converter must convert the entire power from its PV subpanel. Therefore\, this research also explores the use of Differential Power Processing (DPP) system\, which harvests maximum power while only processing a small amount of power due to the mismatches between PV panels. Thus\, DPP structure reduces power loss compared to traditional dMPPT structure. Since it processes only a small amount of power\, differential power processing structure has the potential to further be integrated on a chip and become installed in the junction box during the assembling process. Finally\, the research proposes to implement the plug-and-play features of the solar PV system using wireless power transfer (WPT) instead of hard wire connectors. A series-to-series topology of WPT system (L-R-C series circuit) for one PV unit is proposed. In this system\, the DC-DC converter on the PV side is used to perform MPPT\, while the DC-AC inverter simultaneously perturbs its switching frequency to match possible variations in resonance frequencies. Wireless communication is used between transmitter and receiver. Thus\, the maximum efficiency point on the constant output voltage trajectory can be tracked dynamically under wide and varying operating conditions.
URL:https://coe.northeastern.edu/event/ece-phd-dissertation-defense-yue-zheng/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210427T180000
DTEND;TZID=America/New_York:20210427T193000
DTSTAMP:20260510T014345
CREATED:20210421T142251Z
LAST-MODIFIED:20210421T142251Z
UID:25535-1619546400-1619551800@coe.northeastern.edu
SUMMARY:NE GWISE Presents Dr. Adriana Bankston\, Science Policy Workshop
DESCRIPTION:NE GWiSE is excited to announce the sixth seminar in our “Building Inclusive Communities” 2020-21 virtual series\, which will take place on Tuesday\, April 27th from 6:00-7:30pm. \nThis month’s speaker\, Dr. Adriana Bankston\, is a Principal Legislative Analyst in the University of California (UC) Office of Federal Governmental Relations\, where she serves as an advocate for UC with Congress\, the Administration\, and federal agencies. She’s also the new CEO and Managing Publisher of the Journal of Science Policy & Governance. \nDuring this interactive workshop\, Dr. Bankston will cover effective strategies for graduate students to communicate their research to policymakers\, both orally and in writing. She will discuss different types of policy writing and how to tailor your message to a specific audience within the current political context. The session will also provide opportunities to practice speaking and writing on a policy topic of your choice. \nThe event is free to attend and open to all genders. It will be held on Tuesday\, April 27th from 6:00-7:30PM EST. Learn more about the event and RSVP!
URL:https://coe.northeastern.edu/event/ne-gwise-presents-dr-adriana-bankston-science-policy-workshop/
ORGANIZER;CN="GWiSE%3A Graduate Women in Science and Engineering":MAILTO:gwise.neu@gmail.com
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210429T090000
DTEND;TZID=America/New_York:20210429T100000
DTSTAMP:20260510T014345
CREATED:20210322T140415Z
LAST-MODIFIED:20210412T212812Z
UID:25116-1619686800-1619690400@coe.northeastern.edu
SUMMARY:Women In Engineering Webinar
DESCRIPTION:Join the Graduate School of Engineering for a Women in Engineering Webinar that will take place on April 29th at 9:00AM EST. Please find the registration link below. A recording will be available to those who are unable to attend. \nJoin link: https://us02web.zoom.us/webinar/register/WN_4ij5_t78QSyEJxC9VPiK6Q
URL:https://coe.northeastern.edu/event/women-in-engineering-webinar/
ORGANIZER;CN="Graduate School of Engineering":MAILTO:coe-gradadmissions@northeastern.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210429T100000
DTEND;TZID=America/New_York:20210429T113000
DTSTAMP:20260510T014345
CREATED:20210426T135507Z
LAST-MODIFIED:20210426T135507Z
UID:25580-1619690400-1619695800@coe.northeastern.edu
SUMMARY:Culturally Responsive & Anti-Racist Teaching: Strategies for Inclusive Excellence
DESCRIPTION:Join Dr. Ivonne M. García\, Chief DEI Officer at the College of Wooster\, for a seminar & workshop that will cover culturally responsive teaching strategies with respect to multilingual learners\, and then delve into antiracism as defined by Ibram X. Kendi and the practices that we as educators can incorporate into our classes and our departments. Intensive breakout sessions will provide ample opportunity for participants to think deeply about incorporating antiracist strategies into courses and provide a platform for brainstorming how to engage our students in these conversations. \nAlthough this workshop was designed with faculty in mind\, staff are very welcomed to attend – this is going to be a highly interactive event with plenty of takeaways – and even some homework! \n\nFor more information and to register for the event please click on the link below: \n\nhttps://www.eventbrite.com/e/multilingual-learners-strategies-in-teaching-for-inclusive-excellence-tickets-145930499051
URL:https://coe.northeastern.edu/event/culturally-responsive-anti-racist-teaching-strategies-for-inclusive-excellence/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210429T120000
DTEND;TZID=America/New_York:20210429T133000
DTSTAMP:20260510T014345
CREATED:20210426T135332Z
LAST-MODIFIED:20210426T135332Z
UID:25583-1619697600-1619703000@coe.northeastern.edu
SUMMARY:Distinguished Speaker Series in Robotics
DESCRIPTION:We cordially invite you to join the\nDISTINGUISHED SPEAKER SERIES IN ROBOTICS\nThursday\, April 29\, 12:00 – 1:30pm \n\nVirtual Meeting – Zoom Link | Meeting ID: 928 6786 9946 | Passcode: 103234 \nhttps://northeastern.zoom.us/j/92867869946?pwd=VTA5R1EwRmZKUjdSeHRpYXpVM09Kdz09 \n\nManual Skills and Dexterity in Robots and Humans \nAude Billard \nProfessor of Robotics\, Swiss Federal Institute of Technology (EPFL)\, Switzerland \n\nPart 1: Robots have moved from imitating humans to exceeding humans’ capabilities – sometimes: The design of robots’ manipulation capabilities is driven by our admiration for humans’ exquisite dexterity and motor agility. Yet\, robots are far from reproducing the complexity of human cognition\, for some skills robots do better than humans. Thanks to their powerful motors and the speed of computation of their computer-driven circuits\, robots can beat humans in precision and reactivity. This talk will give an overview of several approaches developed at LASA to endow robots with the ability to react extremely rapidly in the face of unexpected changes in the environment\, combining control with dynamical systems and machine learning. We use human demonstrations to guide the design of the controller’s parameters to modulate the compliance and to determine the range of feasible paths. A review of these algorithms will be accompanied with illustrations of their implementation for controlling uni-manual and bi-manual manipulation. I will conclude by showing some examples of super-human capabilities for catching objects with a dexterity that exceeds that of human beings. \nPart 2: Understanding bimanual skill – a case study in watchmaking: Human dexterity still eludes largely robotics. In an effort to better understand and model this dexterity\, we took on an adventure and decided to follow a cohort of apprentices at watchmaking\, a craft unique in its requirement for precise control of finger movements. Precise control of force is also of essence to prevent breakage of the tiny\, and often highly valuable\, pieces. In a two-year long training\, apprentice acquire the ability to go beyond their natural perception of touch\, so as to sense when the piece clicks and the screw in. Most impressive is the ability with which they acquire unusual but efficient hand postures. Our study unveils how the two hands work in coordination to distribute control variables and achieve better precision than when using a single hand. \nBio: Aude Billard is professor in robotics at the School of Engineering at the Swiss Federal Institute of Technology in Lausanne (EPFL). Trained in physics and robotics\, she has been a pioneer in the application of machine learning for robotic control and human-robot interactions. Billard’s research focuses on manual control and dexterity\, inspired by human skill. Her work on robotics and human-robot interactions has been recognized numerous times by the Institute of Electrical and Electronics Engineers (IEEE) and she currently holds a leadership position on the executive committee of the IEEE Robotics and Automation Society (RAS) as the vice president of publication activities. \n\nPresented by the Institute for Experiential Robotics and Action Club
URL:https://coe.northeastern.edu/event/distinguished-speaker-series-in-robotics/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210429T170000
DTEND;TZID=America/New_York:20210429T180000
DTSTAMP:20260510T014345
CREATED:20210420T140324Z
LAST-MODIFIED:20210420T140324Z
UID:25494-1619715600-1619719200@coe.northeastern.edu
SUMMARY:CommLab Data Visualization Workshop
DESCRIPTION:The ability to visually display your data is an integral part of your scientific communication toolbox. In our second Research Dissemination Series workshop\, the COE Communication Lab will discuss best practices for clear design and inspire new ideas for designing figures.  Data visualization resources and tools will also be shared to effectively communicate the meaning of your data. Register for this event
URL:https://coe.northeastern.edu/event/commlab-data-visualization-workshop/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210504T183000
DTEND;TZID=America/New_York:20210504T193000
DTSTAMP:20260510T014345
CREATED:20210420T140051Z
LAST-MODIFIED:20210420T140051Z
UID:25491-1620153000-1620156600@coe.northeastern.edu
SUMMARY:CEE Seminar: Cycling for Sustainable Cities
DESCRIPTION:Please consider joining the Department of Civil and Environmental Engineering for this seminar on sustainable urban travel. \nABSTRACT: Cycling is the most sustainable means of urban travel\, practical for most short- and medium-distance trips—commuting to and from work and school\, shopping\, visiting friends—as well as for recreation and exercise. Cycling promotes physical\, social\, and mental health\, helps reduce car use\, enhances mobility and independence\, and is economical for both public and personal budgets. \nThis presentation explores how to make city cycling—the most sustainable means of travel—safe\, practical\, and convenient for all. Buehler and Pucher discuss the latest cycling trends and policies around the world and consider specific aspects of cycling. Taken together\, the presentation demonstrates that successful promotion of cycling depends on a coordinated package of mutually supportive infrastructure\, programs\, and policies. Cycling should be made feasible for everyone and not limited to especially fit\, daring\, well-trained cyclists riding expensive bicycles. \nBios: \nRalph Buehler: Ph.D. is Professor and Chair of Urban Affairs and Planning in the School of Public and International Affairs at Virginia Tech’s Research Center in Arlington\, VA. Most of his research has an international comparative perspective\, contrasting transport and land-use policies\, transport systems\, and travel behavior in Western Europe and North America. Between 2012 and 2018\, he served as chair of the Committee for Bicycle Transportation of the Transportation Research Board (TRB). His research interests include: (1) the influence of transport policy\, land use\, and socio-demographics on travel behavior; (2) active travel and public health; and (3) public transport demand\, supply\, regional coordination\, and financial efficiency. \nJohn Pucher: Ph.D. is professor emeritus at the Bloustein School of Planning and Public Policy at Rutgers University\, in New Jersey.  He was a professor at Rutgers University from 1978 to 2014\, conducting research on urban transportation in the United States\, Canada\, Australia\, and Europe.  Over the past 25 years\, John’s research has focused on walking and bicycling\, and how to improve their safety and convenience for all age groups\, for women as well as men\, and for all levels of physical ability. John has published four books and over 200 articles in academic and professional journals.  His most recent book\, “Cycling for Sustainable Cities\,” was published by MIT Press in February 2021.
URL:https://coe.northeastern.edu/event/cee-seminar-cycling-for-sustainable-cities/
ORGANIZER;CN="Civil & Environmental Engineering":MAILTO:civilinfo@coe.neu.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210507T110000
DTEND;TZID=America/New_York:20210507T120000
DTSTAMP:20260510T014345
CREATED:20210506T193919Z
LAST-MODIFIED:20210506T193919Z
UID:25832-1620385200-1620388800@coe.northeastern.edu
SUMMARY:ECE Faculty Seminar: Sumientra Rampersad
DESCRIPTION:Faculty Seminar: Is temporal interference the key to noninvasive deep brain stimulation? Answers from simulation studies in mice and humans. \nSumientra Rampersad \nLocation: Zoom Link \nAbstract: Transcranial current stimulation (tCS) has been used for two decades to noninvasively investigate and influence brain function in both healthy volunteers and clinical populations. While many positive effects have been found\, the goals of high focality\, accurate targeting and deep stimulation are yet to be achieved. Transcranial temporal interference stimulation (tTIS) is a new form of tCS that might improve the method on all three fronts. tTIS uses two alternating currents to create an amplitude-modulated electric field that can peak deep in the brain. A recent murine study showed promising effects of tTIS and concluded that the technique may be used as a noninvasive form of deep brain stimulation in humans\, but results from human experiments have not yet been published. In this talk I will present results of finite element simulations with realistic head models to investigate the electric fields induced by tTIS in the brain\, comparing results in murine and human head models for tTIS and conventional tCS. Due to the nonlinear nature of tTIS\, conventional methods to optimize tCS fields for a specific brain target cannot be used. I will present two nonconvex optimization methods for tTIS and compare their efficiency and results. Finally\, I will discuss the implications of the results of these simulation and optimization studies for potential applications of tTIS in humans. \nBio: Sumientra Rampersad is an Assistant Research Professor in the Department of Electrical and Computer Engineering at Northeastern University in Boston\, where she leads the Brain Stimulation & Simulation Lab. Dr. Rampersad’s research aims to improve understanding of the working mechanisms behind neuromodulation and improve its application using computational methods and experiments with human subjects. She investigates invasive (ECoG\, sEEG) and noninvasive (tCS\, TMS) brain stimulation\, as well as peripheral stimulation\, and is especially interested in bridging the gap between modeling and experiments through model-based experimentation. Her research in collaboration with various academic and clinical partners has been awarded funding by NIA\, NINDS and NIMH. Dr. Rampersad was previously a research scientist in Northeastern’s Cognitive Systems Lab and obtained her PhD at the Radboud University Donders Institute in Nijmegen\, the Netherlands.
URL:https://coe.northeastern.edu/event/ece-faculty-seminar-sumientra-rampersad/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210507T120000
DTEND;TZID=America/New_York:20210507T130000
DTSTAMP:20260510T014345
CREATED:20210427T210636Z
LAST-MODIFIED:20210427T210636Z
UID:25608-1620388800-1620392400@coe.northeastern.edu
SUMMARY:Urban-scale Measurements and Modeling Fate & Transport of PFAS Across Media
DESCRIPTION:Join the Department of Civil and Environmental Engineering and the PROTECT Center for a seminar with the EPA’s Dr. Kiran Alapaty\, who will deliver a talk titled “Urban-scale Measurements and Modeling Fate & Transport of PFAS Across Media.” \nSeminar Abstract: \nPer- and Polyfluoroalkyl Substances (PFAS) have gained attention due to their adverse health effects as well as unknown exposures to legacy and novel compounds. As many of these compounds are stable and persistent\, many PFAS compounds have been detected worldwide across different media in the total environment. Thus\, comprehensive multi-media PFAS chemical concentration data are needed to study PFAS human exposure and health impacts. While some PFAS measurements and exposure studies are available\, no comprehensive PFAS measurement data exist at a continental scale. Also\, it is not clear to the local and federal government agencies as to how to account for the spatiotemporal distributions of PFAS contamination and associated long-term health impacts. Such issues are acute at local to urban scales. Thus\, the in-depth understanding of fate and transport of PFAS across media is much needed and may provide critical information for stakeholders. \nThe Washington Works plant in Parkersburg\, West Virginia has emitted long-chain perfluorooctanoic acid (PFOA) into the environment for decades and at present\, it continues to emit hexafluoropropylene oxide dimer acid [(HFPO-DA)\, GenX]. A database for PFOA at Parkersburg was developed and these PFOA measurements in air\, water\, and soil provide a good opportunity to validate the multi-media modeling system. \nWe are tailoring a robust and efficient suite of modeling tools to simulate PFAS fate and transport in air\, water\, and soil at urban scales. For air\, a state-of-the-art dispersion model (QUIC) is being tested for PFAS air modeling. For other media\, we are testing two state-of-the-art USGS models (MODFLOW and MT3D) for groundwater\, the BreZo model for surface water\, and EPA’s model PRZM-5 for vadose zone. These modeling tools can be used at seasonal to decadal timescales\, and their PFOA estimations can be provided as input data to a high throughput physiologically based pharmacokinetic (PBPK) model to estimate human exposure to PFAS. The combination of multi-media modeling system and PBPK model bridges the gaps between PFAS emissions and human exposure estimates and thus can provide the basis for epidemiological studies. This research opens doors to study the association between human exposure to PFAS and specific human diseases. \nSpeaker Bio:  \nKiran Alapaty is the Senior Science Advisor in the Atmospheric & Environmental Systems Modeling Division in the ORD of US EPA. His research interests are in air quality modeling and model development\, PFAS life cycle modeling\, integrated assessment modeling\, convective cloud parameterization development\, boundary layer modeling\, climate change and exposure science\, and socio-economics. In the past\, he was the Chief of the Climate Branch of the AMA Division with research interests in improving regional climate data for use with exposure science research. \nBefore joining EPA in 2011\, for several years Kiran was at DOE HQ as a Program Director for the DOE’s national climate program managing DOE’s National Labs and research grants.  Prior to that\, he was also a Program Director at the National Science Foundation managing climate research at NCAR and academia. \nKiran holds an MS in Aerospace Engineering from the Indian Institute of Sciences and a PhD in Atmospheric Science from North Carolina State University. \n  \nAbout the Lunch & Learn Seminar Series: \nA new Bimonthly seminar series from the Department of Civil and Environmental Engineering (CEE)\, focusing on convergent research\, bringing together Northeastern colleagues and collaborators to think big/bold\, explore ideas that build cooperation\, and foster transformative innovation within CEE and across disciplines beyond CEE. \nDue to COVID restrictions\, this event remains virtual-only. Please bring your own lunch and join us online.
URL:https://coe.northeastern.edu/event/urban-scale-measurements-and-modeling-fate-transport-of-pfas-across-media/
ORGANIZER;CN="Civil & Environmental Engineering":MAILTO:civilinfo@coe.neu.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20210510
DTEND;VALUE=DATE:20210515
DTSTAMP:20260510T014345
CREATED:20210421T202724Z
LAST-MODIFIED:20210421T203805Z
UID:25558-1620604800-1621036799@coe.northeastern.edu
SUMMARY:Huskies Wellness Week
DESCRIPTION:Welcome to Huskies Wellness Week\, a personal retreat that will leave you feeling refreshed and empowered from the comfort of your own home. Build some ‘you time’ into your days through a lineup of exclusive programming\, hosted by the Northeastern Boston Community. Share your progress with our Instagram gameboard—designed for us to stay on track together—for the chance to win a prize. \nHow do I play? \nOn Sunday\, May 9 we’re posting a gameboard on our Instagram account for the chance to win a $100 gift card to the bookstore. Complete 7 squares for one entry into our challenge\, and 10 squares for two entries. Send us a screenshot of the gameboard with your completed tiles checked off or with pictures of your activity overlayed on top by the following Sunday\, May 16 at 12pm EST to be entered into the raffle. \nThe winner will be selected on Monday\, May 17 by 12pm EST and contacted via email by Ilana Gensler\, MA’19\, Assistant Director\, Affinity and Domestic Engagement. \nYour completed board can be sent directly to @northeastern_alumni through direct message on Instagram. Be sure to share your progress throughout the week on Instagram by tagging @northeastern_alumni. \nWhat if I have a private account?\nUpon registering you will be asked to provide your Instagram handle. You will receive a follow-request ahead of Huskies Wellness Week. \nSessions\nViewing in Eastern Time \n\n\n\nActivate your Full Potential\n5/10/21\n8:30 AM-9:30 AM ET \nLeena Prabhoo\, MEd’90\nManaging Partner\nPath to Prajñā\nSolutions LLP \nEnhance your personal and professional wellness by learning how to activate your full potential. This session will explore what it means to be living your full potential and give you the tools to act so you can make it a reality. Learn about some of the benefits you can gain from this process and develop your personal game-plan for moving forward on this journey.\n\n\n\nMeditation to Cultivate Peace of Mind\n5/12/21\n3:00 PM-4:00 PM ET \nStacy Hernandez\, AS’98\, MS’01\nOwner/College\nCounselor\nThe Best U \nSettle in for a 40-minute meditation session to cultivate peace of mind in service of your mental health. When you dedicate time exploring within\, you learn to listen to your inner voice rather than the influences outside of you. This internal reconnection can help you activate the power you have inside of yourself to stay grounded and bring enhanced mindfulness to every element of your life as you move through each day.\n\n\n\nMOVE by The Handle Bar\n5/14/21\n8:00 AM-8:45 AM ETAnthony Charter\nIndoor Cycling Instructor\nThe Handle Bar Indoor Cycling Studio \nMOVE is a 45-minute\, total-body workout that combines high-intensity plyometric movement with slow-burning kettlebell strength work. It fuses The Handle Bar’s passion for music-driven exercise with thoughtful programming that complements and enhances the studio’s work on the bike. Class will be accessible for 48 hours after it goes Live at the time of the event.\n\n\n\nVinyasa Flow Yoga\n5/14/21\n12:00 PM-12:30 PM ET \nH Alex Harrison\, JD’11\nYoga Instructor\nBeacon Hill Yoga \nMake space both physically and mentally as we explore and connect with our bodies through the synthesis of yoga\, movement\, breath\, and mindfulness exercises. Expect to flow through traditional yoga postures as we explore the intricacies of skeletal alignment and the muscular engagement required to link pose to pose\, and end with a restful savasana.\n\n\n\n\nRegister Now
URL:https://coe.northeastern.edu/event/huskies-wellness-week/
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20210513
DTEND;VALUE=DATE:20210515
DTSTAMP:20260510T014345
CREATED:20210513T192140Z
LAST-MODIFIED:20210513T192140Z
UID:25946-1620864000-1621036799@coe.northeastern.edu
SUMMARY:AMGEN Lecture Series: Biotechnology Edge
DESCRIPTION:This two-day lecture series is presented by AMGEN scientists\, in partnership with the School of Pharmacy and Bouvé College of Health Sciences Dean’s office. It will provide participants with an overview of the drug development process\, as well as the biotechnology industry and potential career paths. Lectures will be presented live online.  Recording is not allowed. See the full program below. \nThe lectures will be in real time on WebEx.  A link will be sent to everyone who has registered. Attendees of the two-day lecture series will be eligible to earn a Badge. \nRSVP \n\n\n\n\n\n\n\nThursday May 13th 2021\, 9.00 am – 4.30 pm\n\n\n9.00\nIntroduction\n\n\n9.10\nLife of a Drug\, Roger Hart\, PhD\n\n\n10.00\nDrug Discovery at Amgen: A Multi‐Modality Approach\, Roger Hart\, PhD\n\n\n10.50\nBreak and Informational Session\n\n\n11.10\nProcess Development from Clinic to Approval\, Jennifer Litowski\,Process Development Principal Scientist\n\n\n12.00\nLunch and Learn\n\n\n1.00\nTarget Identification & Validation\, John Ferbas\, PhD\,\n\n\n1.50\nIntroduction to Pharmaceutical Solid-State Chemistry and MaterialsScience\, Hyunsoo Park\, Process Development Principal  Scientist\n\n\n2.40\nBreak\n\n\n2.50\nFrom Bench to Bedside: Discovery of the First FDA‐ ApprovedAntibody Therapeutic for Migraine\, Cen Xu\, PhD\n\n\n3.40\nThe Role of Continuous Manufacturing to Advance Amgen’s SyntheticPortfolio\, Matt Beaver\, Principal Scientist\n\n\n\n\n\n\nFriday\, May 14th 2021\, 9.00 am – 4.30 pm\n\n\n9.00\nIntroduction\n\n\n9.10\nDrug Safety: An Industry Perspective Oluwadamilola Ogunyankin\,MD\,MPH\n\n\n10.00\nModeling of Processes\, Products and Devices for Drug Development &Manufacturing\, Pablo Rolandi\, Director Data Sciences\n\n\n10.50\nBreak and Informational Session\n\n\n11.10\nRaw Material Selection and Control for ManufacturingPharmaceuticals\, Susan Burke\, PhD\n\n\n12.00\nLunch and Learn\n\n\n1.00\nOncology\, Kristin Tarbell\, Principal Scientist\n\n\n1.50\nInnovations in Device Technologies for Delivering Biologics to Patients\,Shirish Ingawale\, PhD\n\n\n2.40\nBreak\n\n\n2.50\nDigital Transformation in Biopharmaceutical Operations\, Myra Coufal\,PhD\n\n\n3.40\nCareers in Biotech\, Jessica  Smith\,  Process  Development  AssociateScientist
URL:https://coe.northeastern.edu/event/amgen-lecture-series-biotechnology-edge/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210513T140000
DTEND;TZID=America/New_York:20210513T150000
DTSTAMP:20260510T014345
CREATED:20210503T135624Z
LAST-MODIFIED:20210510T135607Z
UID:25648-1620914400-1620918000@coe.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Siyue Wang
DESCRIPTION:PhD Proposal Review: Towards Robust and Secure Deep Learning Models and Beyond \nSiyue Wang \nLocation: Zoom Link \nAbstract: Modern science and technology witness the breakthroughs made by deep learning during the past decades. Fueled by rapid improvements of computational resources\, learning algorithms\, and massive amount of data\, deep neural networks (DNNs) have played a dominant role in more and more real-world applications. Nonetheless\, there is a spring of bitterness mingling with this remarkable success – recent studies reveals that there are two main security threats of DNNs which limit its widespread usage: 1) the robustness of DNN models under adversarial attacks\, and 2) the protection and verification of intellectual properties of well-trained DNN models. \nIn this dissertation\, we fist focus on the security problems of how to build robust DNNs under adversarial attacks\, where deliberately crafted small perturbations added to the clean input can lead to wrong prediction results with high confidence. We approach the solution by incorporating stochasticity into DNN models. We propose multiple schemes to harden the DNN models when facing adversarial threats\, including Defensive Dropout (DD)\, Hierarchical Random Switching (HRS)\, and Adversarially Trained Model Switching (AdvMS). \nThe second part of this dissertation focuses on how to effectively protect the intellectual property for DNNs and reliably identify their ownership. We propose Characteristic Examples (C-examples) for effectively fingerprinting DNN models\, featuring high-robustness to the well-trained DNN and its derived versions (e.g. pruned models) as well as low-transferability to unassociated models. The generation process of our fingerprints does not intervene with the training phase and no additional data are required from the training/testing set.
URL:https://coe.northeastern.edu/event/ece-phd-proposal-review-siyue-wang/
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DTSTART;TZID=America/New_York:20210521T110000
DTEND;TZID=America/New_York:20210521T120000
DTSTAMP:20260510T014345
CREATED:20210503T135740Z
LAST-MODIFIED:20210503T135740Z
UID:25650-1621594800-1621598400@coe.northeastern.edu
SUMMARY:ECE MS Thesis Defense: Daniel Uvaydov
DESCRIPTION:MS Thesis Defense titled DeepSense: Fast Wideband Spectrum Sensing Through Real-Time In-the-Loop Deep Learning \nDaniel Uvaydov \nLocation: Microsoft Teams \nAbstract: Spectrum sharing will be a key technology to tackle spectrum scarcity in the sub-6 GHz bands. To fairly access the shared bandwidth\, wireless users will necessarily need to quickly sense large portions of spectrum and opportunistically access unutilized bands. The key unaddressed challenges of spectrum sensing are that (i) it has to be performed with extremely low latency over large bandwidths to detect tiny spectrum holes and to guarantee strict real-time digital signal processing (DSP) constraints; (ii) its underlying algorithms need to be extremely accurate\, and flexible enough to work with different wireless bands and protocols to find application in real-world settings. To the best of our knowledge\, the literature lacks spectrum sensing techniques able to accomplish both requirements. In this paper\, we propose DeepSense\, a software/hardware framework for real-time wideband spectrum sensing that relies on real-time deep learning tightly integrated into the transceiver’s baseband processing logic to detect and exploit unutilized spectrum bands. DeepSense uses a convolutional neural network (CNN) implemented in the wireless platform’s hardware fabric to analyze a small portion of the unprocessed baseband waveform to automatically extract the maximum amount of information with the least amount of I/Q samples. We extensively validate the accuracy\, latency and generality performance of DeepSense with (i) a 400 GB dataset containing hundreds of thousands of WiFi transmissions collected “in the wild” with different Signal-to-Noise-Ratio (SNR) conditions and over different days; (ii) a dataset of transmissions collected using our own software-defined radio testbed; and (iii) a synthetic dataset of LTE transmissions under controlled SNR conditions. We also measure the real-time latency of the CNNs trained on the three datasets with an FPGA implementation\, and compare our approach with a fixed energy threshold mechanism. Results show that our learning-based approach can deliver a precision and recall of 98% and 97% respectively and a latency as low as 0.61ms.
URL:https://coe.northeastern.edu/event/ece-ms-thesis-defense-daniel-uvaydov/
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