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DTSTART;TZID=America/New_York:20210422T090000
DTEND;TZID=America/New_York:20210422T100000
DTSTAMP:20260510T042146
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:20260510T042146
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:20260510T042146
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:20260510T042146
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:20260510T042146
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:20260510T042146
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:20260510T042146
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:20260510T042146
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:20260510T042146
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:20260510T042146
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:20260510T042146
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:20260510T042146
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:20260510T042146
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:20260510T042146
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:20260510T042146
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:20260510T042146
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:20260510T042146
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:20260510T042146
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:20260510T042146
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:20260510T042146
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:20260510T042146
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:20260510T042146
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:20260510T042146
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/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210521T110000
DTEND;TZID=America/New_York:20210521T120000
DTSTAMP:20260510T042146
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/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210525T100000
DTEND;TZID=America/New_York:20210525T110000
DTSTAMP:20260510T042146
CREATED:20210517T134657Z
LAST-MODIFIED:20210517T134657Z
UID:25994-1621936800-1621940400@coe.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Mohammad Hossein Hajkazemi
DESCRIPTION:PhD Dissertation Defense: High-performance Translation Layers for Cloud Immutable Storage \nMohammad Hossein Hajkazemi \nLocation: Zoom Link \nAbstract: Most storage interfaces support in-place updates: blocks can be rewritten\, files can be modified at byte granularity\, fields may be updated in database table rows. Yet internally these layers often rely on out-of-place (immutable) writes. In some cases\, this may be necessary to use media\, such as flash\, SMR (shingled magnetic recording) and IMR (interlaced magnetic recording) disk\, which do not allow overwrites. In others\, it is used to simplify the implementation of transactions and/or crash consistency\, in the form of journaling\, write-ahead logging\, shadow paging\, etc. \nIn a storage system\, translation layers perform out-of-place writes\, and they are implemented in different layers of storage stack from the file system to the storage device firmware depending on the application. In this dissertation I focus on translation layers for cloud immutable storage technologies to improve the cloud I/O performance. As a part of my thesis\, I focus on translation layers for state-of-the-art immutable storage media such as SMR and IMR used in cloud environments\, proposing several novel algorithms to improve their efficiency. I also introduce FSTL\, a framework to design and implement SMR translation layer. Finally\, I describe Collage\, a virtual disk I developed over S3 (could be implemented over a similar object storage) using a translation layer which performs large\, sequential\, out-of-place writes for high performance. It optionally uses fast local storage for write logging and as a write-back cache\, guaranteeing prefix consistency under all failure conditions and recovery of all acknowledged writes if the local cache is not lost. Collage supports snapshots and cloned volumes\, performs well over erasure-coded storage\, and allows consistent asynchronous volume replication over geographic distances. I show that Collage can achieve massive performance improvements (e.g.\, over 100x for microbenchmarks and 10x for macro-benchmarks) over CEPH RBD\, a popular open-source scale-out virtual disk implementation.
URL:https://coe.northeastern.edu/event/ece-phd-dissertation-defense-mohammad-hossein-hajkazemi/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210526T130000
DTEND;TZID=America/New_York:20210526T140000
DTSTAMP:20260510T042146
CREATED:20210524T182653Z
LAST-MODIFIED:20210524T182653Z
UID:26078-1622034000-1622037600@coe.northeastern.edu
SUMMARY:PhD Dissertation Defense: Kunpeng Li
DESCRIPTION:PhD Dissertation Defense: Visual Learning with Limited Supervision \nKunpeng Li \nLocation: Zoom Link \nAbstract: Deep learning models have achieved remarkable success in many computer vision tasks. However\, they typically rely on large amounts of carefully labeled training data whose annotating process is usually expensive\, time-consuming and even infeasible when considering the task complexity and scarcity of expert knowledge.\nIn this dissertation talk\, I will discuss several explorations along the direction of visual learning with limited supervision. They are mainly about learning from data with weak forms of annotations and learning from multi-modal data pairs. Specifically\, I will first present a guided attention learning framework to conduct semantic segmentation by mainly using image-level labels\, as such weak form of annotation can be collected much more efficiently than pixel-level labels. Under mild assumptions\, our framework can also be used as a plug-in to existing convolutional neural networks to improve their generalization performance. This is achieved by guiding the network to focus on correct things when learning concepts from a limited set of training samples. Besides\, I will also introduce models that can effectively learn from multi-modal data pairs without relying on dense annotations of visual semantic concepts. Our models incorporate relational reasoning ability into the visual representation learning process so that it can be better aligned with the supervision from corresponding text descriptions.
URL:https://coe.northeastern.edu/event/phd-dissertation-defense-kunpeng-li/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210527T090000
DTEND;TZID=America/New_York:20210527T180000
DTSTAMP:20260510T042146
CREATED:20210512T175130Z
LAST-MODIFIED:20210520T192306Z
UID:25900-1622106000-1622138400@coe.northeastern.edu
SUMMARY:NanoSI 2021 Annual Workshop on Nano Systems Innovation
DESCRIPTION:NanoSI 2021 Annual Workshop on Nano Systems Innovation \nThursday\, May 27th\, 2021 \nRegistration: Zoom Link \nLocation: Once registered\, a Zoom Link will be available. \nDescription: The goal of the Northeastern SMART Annual Workshop on Nano Systems Innovation (NanoSI 2021) is to bring together researchers\, government and industry to discuss new strategies to address the growing demand of sensing\, communication and artificial intelligence at the chip-scale while reducing the time for innovation and transition of the new foundational nano-system technologies that are going to be the at root of our nation’s economic strength\, national security and technological standing in the years to come. \nPreliminary Agenda:\n8:50 am – 9:00 am: Check-in\n9:00 am – 10:00 am: Opening remarks from University and SMART Center Leadership\, DARPA PMs\n10:00 am – 10:30 am: Plenary Talk – David Horsley\n10:30 am – 11:00 am: Intros from Industrial Partners\n11:00 am – 12:00 pm: Center Projects Presentations\n12:00 pm – 1:00 pm: Lunch Break\n1:00 pm – 3:00 pm: Center Projects Presentations\n3:00 pm – 4:30 pm: Panel Discussion with DARPA\, Industry\, and Academia: Benjamin Griffin\, Ronald Polcawich\, Amit Lal\, Troy Olsson\, David Horsley\n4:30 pm – 4:45 pm: Closing Remarks\n4:50 pm – 5:50 pm: IAB Meeting (Members Only)\n6:00 pm: Meeting adjourn \n  \n 
URL:https://coe.northeastern.edu/event/nanosi-2021-annual-workshop-on-nano-systems-innovation/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210601T120000
DTEND;TZID=America/New_York:20210601T130000
DTSTAMP:20260510T042146
CREATED:20210520T191425Z
LAST-MODIFIED:20210520T191425Z
UID:26062-1622548800-1622552400@coe.northeastern.edu
SUMMARY:Plant Shift Initiative | Food Tech
DESCRIPTION:To have 100% plant based diets is the most ecological and sustainable way to combat climate change. The main issue we have—making it tasty and accessible. This panel will dive into the food revolution taking form. \nPanelists \nSebastiano Cossia Castiglioni\, PNT’23 – Moderator\nFounder & Chairman\, Vegan Capital \nChris Kerr – Speaker\nCo-founder & Chair\, Gathered Foods Corp (Good Catch)\nFounding Partner & Chief Investment Officer\, Unovis Asset Management \nChristie Lagally – Speaker\nFounder & Chief Executive Officer\, Rebellyous Foods \nJulie Farkas – Speaker\nCo-founder & Social Impact Director\, PLNT Burger \nREGISTER
URL:https://coe.northeastern.edu/event/plant-shift-initiative-food-tech/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210603T110000
DTEND;TZID=America/New_York:20210603T120000
DTSTAMP:20260510T042146
CREATED:20210526T161737Z
LAST-MODIFIED:20210526T161737Z
UID:26088-1622718000-1622721600@coe.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Mehdi Nasrollahpourmotlaghzanjani
DESCRIPTION:PhD Proposal Review: RFICs for Biomedical Magnetic and Magnetoelectric Microsystems \nMehdi Nasrollahpourmotlaghzanjani \nLocation: Zoom Links \nAbstract: Design and analysis of the advanced biomedical circuit and systems in wide variety of applications has emerged a significant interest. Not only in different engineering disciplines\, but also in a variety of applications such as neuroscience\, COVID-19\, etc. In this study\, we are proposing an implantable device\, handheld device for detecting different diseases and the RFIC design for the ME antenna and sensor evaluations.\nFirst\, we show and miniaturized implantable device for deep brain implantation that provides wireless power transfer efficiency (PTE) of 1 to 2 orders of magnitude higher than the reported micro-coils for brain stimulation. The proposed device will simultaneously measure the as magnetic field activity when neurons are firing. In the second part we will go over the RFIC design for the bio-implant devices\, evaluation of the ME antennas for communication purposes and the circuit interface to measure the ME and GMI sensors. For final part\, we will discuss the handheld device design for early diagnosis of different diseases such as\, lung cancer\, Alzheimer\, Covid-19\, etc through exhaled breath on the molecularly imprinted polymer (MIP) gas sensors.
URL:https://coe.northeastern.edu/event/ece-phd-proposal-review-mehdi-nasrollahpourmotlaghzanjani/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210603T150000
DTEND;TZID=America/New_York:20210603T160000
DTSTAMP:20260510T042146
CREATED:20210602T173220Z
LAST-MODIFIED:20210602T173220Z
UID:26116-1622732400-1622736000@coe.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Yumin Liu
DESCRIPTION:PhD Dissertation Defense: Learning from Spatio-Temporal Data with Applications in Climate Science \nYumin Liu \nLocation: Zoom Link \nAbstract: Climate change is one of the major challenges to human beings and many other species in our time. In the recent decade\, the number of disasters related to climate change such as wildfires\, storms\, floods and droughts are increasing\, and the casualty and economic losses caused by them are larger compared to those of decades ago. This calls for better and efficient ways to predict climate change in order to better prepare and reduce losses. Predicting climate change involves using historical observational data and model simulated data\, both of which usually involve multiple locations and timestamps and are spatio-temporal. With the rapid development and progress of machine learning\, these methods have achieved several impactful contributions in many domains; we would like to translate its impact to climate science.\nIn this thesis we address several problems in climate science. This challenging complex domain enable us to develop\, innovate\, adapt\, and advance machine learning in the following ways. 1) We develop a multi-task learning method to estimate relationships between tasks and learn the basis tasks in different locations especially for nearby locations which may share similar climate patterns. This method assumes that the weights of an observed task is a linear combination of several latent basis tasks and that the task relationships can be learnt by imposing a regularized precision matrix. 2) We propose a nonparameteric mixture of sparse linear regression models to cluster and identify important climate models for prediction. This model incorporates Dirichlet Process (DP) to automatically determine the number of clusters\, imposes Markov Random Field (MRF) constraints to guarantee spatio-temporal smoothness\, and selects a subset of global climate models (GCMs) that are useful for prediction within each spatio-temporal cluster with a spike-and-slab prior. We derive an effective Gibbs sampling method for this model. 3) We adapt image super resolution methods to climate downscaling — increasing spatial resolution for climate variables for local impact analysis. The proposed method is called YNet which is a novel deep convolutional neural network (CNN) with skip connections and fusion capabilities to perform downscaling for climate variables on multiple GCMs directly rather than on reanalysis data. 4) We use saliency map method to discover dependencies among climate variables. We propose the concept of cyclical saliency map (Cyclic-SM) which are meaningful in climate context and more robust to noise as compared to ordinary saliency maps. We show that Cyclic-SMs can reveal relevant spatial regions for prediction. We demonstrate the effectiveness of this method in climate downscaling\, ENSO index prediction and river flow prediction.
URL:https://coe.northeastern.edu/event/ece-phd-dissertation-defense-yumin-liu/
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