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X-ORIGINAL-URL:https://coe.northeastern.edu
X-WR-CALDESC:Events for Northeastern University College of Engineering
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TZID:America/New_York
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DTSTART:20220313T070000
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DTSTART:20221106T060000
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DTSTART:20231105T060000
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DTSTART:20240310T070000
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BEGIN:VEVENT
DTSTART;VALUE=DATE:20231101
DTEND;VALUE=DATE:20231106
DTSTAMP:20260521T014509
CREATED:20230913T191049Z
LAST-MODIFIED:20230913T191049Z
UID:38749-1698796800-1699228799@coe.northeastern.edu
SUMMARY:SHPE 2023 National Convention
DESCRIPTION:COE Graduate Admissions is excited to attend the SHPE National Convention again this year in Salt Lake City\, UT! Learn about our graduate engineering programs available across the U.S. and Canada at the Career Fair & Graduate Expo on November 3rd-4th! Northeastern University will be hosting a Hospitality Suite on November 1st at 7:30pm.
URL:https://coe.northeastern.edu/event/shpe-2023-national-convention/
ORGANIZER;CN="Graduate School of Engineering":MAILTO:coe-gradadmissions@northeastern.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231103T183000
DTEND;TZID=America/New_York:20231105T130000
DTSTAMP:20260521T014509
CREATED:20231016T135551Z
LAST-MODIFIED:20231016T135551Z
UID:39519-1699036200-1699189200@coe.northeastern.edu
SUMMARY:Lean Green-Belt certification course
DESCRIPTION:The IISE Lean Green Belt Certification program is coming to Northeastern on November 3rd\, 4th\, & 5th 2023. This is a great process improvement and project management course that teaches Lean 6S\, muda\, value stream mapping\, point of use\, SMED\, pull\, the eight forms of waste\, visual workplace\, and other related topics. \nThis course is open to all majors and skill levels\, including alumni! No prerequisites or prior knowledge required. \nThis certification can provide a tremendous resume boost and increase the likelihood of getting that dream job or co-op. \nLearn more at iisenortheastern.org/professional-certifications/
URL:https://coe.northeastern.edu/event/lean-green-belt-certification-course/
ORGANIZER;CN="Mechanical & Industrial Engineering":MAILTO:mie-web@coe.neu.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231106T090000
DTEND;TZID=America/New_York:20231106T094500
DTSTAMP:20260521T014509
CREATED:20231023T185358Z
LAST-MODIFIED:20231023T185358Z
UID:40083-1699261200-1699263900@coe.northeastern.edu
SUMMARY:GSE Fall 2023 Wonder Week: Civil and Environmental Engineering
DESCRIPTION:Learn about the Civil and Environmental Engineering graduate programs and how you’ll become a prepared professional to address the global\, complex\, and ever-evolving engineering challenges of our time by building on current department strengths and expanding into vital areas.
URL:https://coe.northeastern.edu/event/gse-fall-2023-wonder-week-civil-and-environmental-engineering/
ORGANIZER;CN="Graduate School of Engineering":MAILTO:coe-gradadmissions@northeastern.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231106T110000
DTEND;TZID=America/New_York:20231106T114500
DTSTAMP:20260521T014509
CREATED:20231023T185210Z
LAST-MODIFIED:20231023T185210Z
UID:40087-1699268400-1699271100@coe.northeastern.edu
SUMMARY:GSE Fall Wonder Week: MS Telecommunications & Cyber Physical Systems
DESCRIPTION:Learn about the MS in Telecommunication Networks & Cyber-Physical Systems graduate programs and how you’ll become a prepared professional to address complex\, and ever-evolving engineering challenges! 
URL:https://coe.northeastern.edu/event/gse-fall-wonder-week-ms-telecommunications-cyber-physical-systems/
ORGANIZER;CN="Graduate School of Engineering":MAILTO:coe-gradadmissions@northeastern.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231107T080000
DTEND;TZID=America/New_York:20231107T084500
DTSTAMP:20260521T014509
CREATED:20231023T185115Z
LAST-MODIFIED:20231106T191748Z
UID:40089-1699344000-1699346700@coe.northeastern.edu
SUMMARY:GSE Fall 2023 Wonder Week: MSIS\, MSIS Bridge\, MSSWES\, MSDAAM programs
DESCRIPTION:Join this webinar to learn about MS Information Systems\, MS Information Systems Bridge\, MS Data Architecture & Management\, and MS Software Engineering Systems. You’ll learn how each of these programs learning opportunities to develop the skills needed to create innovative\, practical\, and effective solutions that can be easily applied to current professional challenges.
URL:https://coe.northeastern.edu/event/gse-fall-2023-wonder-week-msis-msis-bridge-msswes-msdaam-programs/
ORGANIZER;CN="Graduate School of Engineering":MAILTO:coe-gradadmissions@northeastern.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231107T090000
DTEND;TZID=America/New_York:20231107T094500
DTSTAMP:20260521T014509
CREATED:20231023T185034Z
LAST-MODIFIED:20231106T192019Z
UID:40091-1699347600-1699350300@coe.northeastern.edu
SUMMARY:GSE Fall 2023 Wonder Week: Product Development
DESCRIPTION:Join this webinar to learn more about the growing demand for Product Development and why it is the key to the success of businesses and the technology sector as it continues to fuel the world economy.
URL:https://coe.northeastern.edu/event/gse-fall-2023-wonder-week-product-development/
ORGANIZER;CN="Graduate School of Engineering":MAILTO:coe-gradadmissions@northeastern.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231107T100000
DTEND;TZID=America/New_York:20231107T104500
DTSTAMP:20260521T014509
CREATED:20231023T184919Z
LAST-MODIFIED:20231106T192158Z
UID:40093-1699351200-1699353900@coe.northeastern.edu
SUMMARY:GSE Fall 2023 Wonder Week: Electrical and Computer Engineering
DESCRIPTION:Join this webinar to learn more about the Electrical and Computer Engineering graduate program including interdisciplinary research opportunities and a world-renowned co-op program.
URL:https://coe.northeastern.edu/event/gse-fall-2023-wonder-week-electrical-and-computer-engineering/
ORGANIZER;CN="Graduate School of Engineering":MAILTO:coe-gradadmissions@northeastern.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20231108
DTEND;VALUE=DATE:20231111
DTSTAMP:20260521T014509
CREATED:20231031T135300Z
LAST-MODIFIED:20231031T135322Z
UID:40252-1699401600-1699660799@coe.northeastern.edu
SUMMARY:STAHY 2023 – 13th International Workshop on Statistical Hydrology (STAHY)
DESCRIPTION:The International Commission on Statistical Hydrology (ICSH) of the International Association of Hydrological Sciences (IAHS) invites researchers to submit abstracts for presentation at the 13th International Workshop on Statistical Hydrology (STAHY2023)\, which will be hosted by Northeastern University in Boston\, Massaschuetts (USA)\, from 8-10 November 2023. \nThe STAHY 2023 workshop brings together the international statistical hydrology community for vibrant scientific discussions and debates on advanced statistical methods for hydrological applications. This year’s theme aims to provide a bridge between the environmental statistics and artificial intelligence communities with methodological discussions\, exchange of knowledge\, and identification of opportunities for mutual support to solve climate\, water\, and sustainability issues. \nFocusing on the broader scope of the Sustainable Development Goals (SDGs) established by the United Nations in 2015\, the theme of the workshop is expected to address several goals but are not limited to Clean Water and Sanitation (SDG 6)\, Sustainable Cities and Communities (SDG 11)\, Climate Action (SDG 13)\, and Life on Land (SDG 15). \nArtificial intelligence and machine learning are creating a renaissance for environmental and hydrologic statistics that STAHY 2023 wishes to capture in its theme. For example\, machine learning\, including the latest generative pre-trained transformers (GPTs)\, cannot work without probability theory\, and similarly nonlinear statistics can benefit from the flexibility offered by neural network processing. Integrating AI technologies with environmental and hydrologic statistics would provide valuable insights for policymakers to devise informed planning and robust water resources management decisions. Similarly\, in this era of Big Data and automated decisions\, a wide range of statistical theories such as extreme value theory and human-in-the-loop decisions\, continue to remain relevant. \nWe welcome contributions that use machine learning\, artificial intelligence\, hydrologic statistics and/or environmental statistics and address climate\, water\, and sustainability issues. Sessions will be arranged by scientific topic and aim to include the diverse ways in which traditional environmental statistics\, artificial intelligence\, and machine learning are applied within these topics.
URL:https://coe.northeastern.edu/event/stahy-2023-13th-international-workshop-on-statistical-hydrology-stahy/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231108T080000
DTEND;TZID=America/New_York:20231108T084500
DTSTAMP:20260521T014509
CREATED:20231023T184806Z
LAST-MODIFIED:20231106T192324Z
UID:40095-1699430400-1699433100@coe.northeastern.edu
SUMMARY:GSE Fall 2023 Wonder Week: Mechanical and Industrial Engineering
DESCRIPTION:Join this webinar to learn about the Mechanical and Industrial Engineering graduate programs at Northeastern University. You’ll learn about our experiential graduate mechanical and industrial engineering programs including interdisciplinary research opportunities and world-renown co-op.
URL:https://coe.northeastern.edu/event/gse-fall-2023-wonder-week-mechanical-and-industrial-engineering/
ORGANIZER;CN="Graduate School of Engineering":MAILTO:coe-gradadmissions@northeastern.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231108T090000
DTEND;TZID=America/New_York:20231108T094500
DTSTAMP:20260521T014509
CREATED:20231023T184713Z
LAST-MODIFIED:20260514T141930Z
UID:40097-1699434000-1699436700@coe.northeastern.edu
SUMMARY:GSE Fall 2023 Wonder Week: MGEN Co-op
DESCRIPTION:Come learn more about Northeastern’s Multidisciplinary Co-op Program for graduate engineering students! The webinar will feature a presentation by Associate Coop Coordinator & Associate Director Laura Meyer. 
URL:https://coe.northeastern.edu/event/gse-fall-2023-wonder-week-seis-co-op/
ORGANIZER;CN="Graduate School of Engineering":MAILTO:coe-gradadmissions@northeastern.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231108T120000
DTEND;TZID=America/New_York:20231108T124500
DTSTAMP:20260521T014509
CREATED:20231023T184447Z
LAST-MODIFIED:20231106T192438Z
UID:40099-1699444800-1699447500@coe.northeastern.edu
SUMMARY:GSE Fall 2023 Wonder Week: MS in Internet of Things & Wireless Network Engineering
DESCRIPTION:Join experts from the Institute for Wireless Internet of Things as they dive deep into the MS in Internet of Things and Wireless and Network Engineering programs and how they relate to their vision of a future in which people and their environment are wirelessly connected by a continuum of AI-powered devices and networks\, from driverless cars and search-and-rescue drone swarms to implantable medical devices and smart cities.
URL:https://coe.northeastern.edu/event/gse-fall-2023-wonder-week-ms-in-internet-of-things-wireless-network-engineering/
ORGANIZER;CN="Graduate School of Engineering":MAILTO:coe-gradadmissions@northeastern.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231108T120000
DTEND;TZID=America/New_York:20231108T130000
DTSTAMP:20260521T014509
CREATED:20231116T162029Z
LAST-MODIFIED:20231116T162201Z
UID:40429-1699444800-1699448400@coe.northeastern.edu
SUMMARY:Diwali "Festival of Light"
DESCRIPTION:Join the Department of Bioengineering for a Diwali lunch celebration in collaboration with the BioE DEI committee. Food and music included. Colorful clothing is encouraged. \nLocation: The Fenway Center
URL:https://coe.northeastern.edu/event/diwali-festival-of-light/
ORGANIZER;CN="Bioengineering":MAILTO:bioe@northeastern.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20231109
DTEND;VALUE=DATE:20231113
DTSTAMP:20260521T014509
CREATED:20230913T191012Z
LAST-MODIFIED:20230913T191012Z
UID:38752-1699488000-1699833599@coe.northeastern.edu
SUMMARY:oSTEM 13th Annual Conference
DESCRIPTION:Join COE Graduate Admissions at the 13th Annual oSTEM Conference in Anaheim\, CA! Ask your questions about our graduate engineering programs across the U.S. and Canada during the Career Fair Expo on November 10-11th. We look forward to meeting you there!
URL:https://coe.northeastern.edu/event/ostem-13th-annual-conference/
ORGANIZER;CN="Graduate School of Engineering":MAILTO:coe-gradadmissions@northeastern.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231109T080000
DTEND;TZID=America/New_York:20231109T084500
DTSTAMP:20260521T014509
CREATED:20231024T201013Z
LAST-MODIFIED:20231103T172709Z
UID:40147-1699516800-1699519500@coe.northeastern.edu
SUMMARY:GSE Fall 2023 Wonder Week: Chemical Engineering
DESCRIPTION:Learn about the Chemical Engineering graduate program and the cutting-edge research you can be a part of that’s tackles pressing challenges facing our society and our planet in areas such as biomedicine\, energy\, security\, and sustainability.
URL:https://coe.northeastern.edu/event/gse-fall-2023-wonder-week-chemical-engineering/
ORGANIZER;CN="Graduate School of Engineering":MAILTO:coe-gradadmissions@northeastern.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231109T100000
DTEND;TZID=America/New_York:20231109T104500
DTSTAMP:20260521T014509
CREATED:20231023T184328Z
LAST-MODIFIED:20231106T192536Z
UID:40101-1699524000-1699526700@coe.northeastern.edu
SUMMARY:GSE Fall 2023 Wonder Week: Disciplinary Co-Op
DESCRIPTION:Come learn more about Northeastern’s Co-op Program for graduate engineering students! A member of our admissions team\, and the Assistant Dean & Senior Co-op Coordinator\, Lorraine Mountain will present and answer questions.
URL:https://coe.northeastern.edu/event/gse-fall-2023-wonder-week-disciplinary-co-op/
ORGANIZER;CN="Graduate School of Engineering":MAILTO:coe-gradadmissions@northeastern.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231114T120000
DTEND;TZID=America/New_York:20231114T133000
DTSTAMP:20260521T014509
CREATED:20231113T210706Z
LAST-MODIFIED:20231113T210838Z
UID:40373-1699963200-1699968600@coe.northeastern.edu
SUMMARY:Cookies with the Dean
DESCRIPTION:Join Dean Gregory Abowd for cookies and warm apple cider! Stop by the Tents at Robinson Quad from 12pm-1:30pm for our monthly Cookies with the Dean event.
URL:https://coe.northeastern.edu/event/cookies-with-the-dean/
LOCATION:The Tents at Robinson Quad\, 336 Huntington Ave\, Boston\, MA\, 02115\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231115T120000
DTEND;TZID=America/New_York:20231115T130000
DTSTAMP:20260521T014509
CREATED:20231019T134646Z
LAST-MODIFIED:20231019T134646Z
UID:39778-1700049600-1700053200@coe.northeastern.edu
SUMMARY:Chemical Engineering Fall Seminar Series: Professor Malika Jeffries-El
DESCRIPTION:Design and Synthesis of Organic Electronic Material \nThe past two decades have seen a dramatic increase in the number of consumer electronics in use. Previously\, most households had a landline phone\, one or two televisions\, and the occasional desktop computer. These days\, most people own numerous electronic devices\, resulting in an increased demand for the semiconducting materials that drive this technology and the energy needed to power them. Accordingly\, there has been a lot of interest in developing organic semiconductors\, as many of the inorganic materials used in these devices are in limited supply. Organic semiconductors are either polymers or small molecules that feature an extended pi-conjugation. These materials possess many exceptional electronic\, optical\, and thermal properties and thus are well-suited for applications such as transistors\, solar cells\, and light-emitting diodes. Unfortunately\, several issues must be addressed before real-life products can be developed. Unfortunately\, several issues must be addressed before real-life products can be developed. Our group focuses on the design and synthesis of new organic semiconductors based on low-cost and/or easily prepared starting materials. Since the properties of organic semiconductors can be readily modified through chemical synthesis\, we have turned our attention towards the design and synthesis of novel aromatic building blocks. Our group developed several new materials\, including wide-band materials for organic light-emitting diodes and narrow-band gap materials for photovoltaic cells. Our recent work will be presented. \n\nDr. Jeffries-El’s research focuses on developing organic semiconductors–materials that combine the processing properties of polymers with the electronic properties of semiconductors. She has authored over 40 peer reviewed publications and has given over 180 lectures globally. She is a Fellow of the American Chemical Society (ACS)\, the Association for the Advancement of Science (AAAS)\, and the Royal Society of Chemistry. She has won numerous awards\, including the ACS Stanley C. Israel Regional Award for Advancing Diversity in the Chemical Sciences. She is currently an Associate Editor for Chemical Science. She has also served on the editorial boards for the Journal of Materials Chemistry C and Materials Advances and the editorial advisory boards for ACS Central Science and Chemical and Engineering News. Professor Jeffries-El is a staunch advocate for diversity and a dedicated volunteer who has served in several activities within the ACS and is currently an elected board of directors member as a director-at-large. She is also a science communicator who seeks to encourage students from underrepresented groups to pursue STEM degrees and recently appeared on the NOVA series Beyond the Elements. She also serves the community through her work with Alpha Kappa Alpha Sorority\, Incorporated. She is a native of Brooklyn\, New York.
URL:https://coe.northeastern.edu/event/chemical-engineering-fall-seminar-series-professor-malika-jeffries-el/
LOCATION:010 Behrakis\, 360 Huntington Ave\, Boston\, MA\, 02115\, United States
GEO:42.3396156;-71.0886534
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=010 Behrakis 360 Huntington Ave Boston MA 02115 United States;X-APPLE-RADIUS=500;X-TITLE=360 Huntington Ave:geo:-71.0886534,42.3396156
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231116T090000
DTEND;TZID=America/New_York:20231116T110000
DTSTAMP:20260521T014509
CREATED:20231003T200656Z
LAST-MODIFIED:20231003T200656Z
UID:39075-1700125200-1700132400@coe.northeastern.edu
SUMMARY:NIH Webinar
DESCRIPTION:The Center for Research Innovation will be hosting a webinar with the National Institutes of Health\, a division of the U.S. Department of Health and Human Services. Join us to learn about the grants and resources available for translational researchers and aspiring entrepreneurs.
URL:https://coe.northeastern.edu/event/nih-webinar/
ORGANIZER;CN="Center for Research Innovation":MAILTO:cri@northeastern.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231117T083000
DTEND;TZID=America/New_York:20231117T093000
DTSTAMP:20260521T014509
CREATED:20231020T143903Z
LAST-MODIFIED:20231020T143903Z
UID:39994-1700209800-1700213400@coe.northeastern.edu
SUMMARY:Mahshid Asri PhD Dissertation Defense
DESCRIPTION:Title:\nDevelopment of Anomaly Detection and Characterization Algorithms Using Wideband Radar Image Processing for Security Applications \nDate:\n11/17/2023 \nTime:\n8:30:00 AM \nLocation: 302 Stearns \nCommittee Members:\nProf. Carey Rappaport (Advisor)\nProf. Charles DiMarzio\nProf. Edwin Marengo \nAbstract:\nDetection and characterization of suspicious body-worn objects is necessary for safe and effective personnel screening. In airports\, developing a precise system that can distinguish threats and explosives from objects like money belt can reduce the pat-down significantly while maintaining effective security. This dissertation proposes two main algorithms which are developed for different millimeter-wave radar systems. The first project is a material characterization algorithm designed for a 30 GHz wideband multi bi-static radar system used for passenger screening in airports. The proposed algorithm can automatically distinguish lossless materials from lossy ones and calculate their thickness and permittivities. Starting from the radar reconstructed image showing a cross-section of the body\, we extract the nominal body contour using Fourier series\, separate body and object responses\, categorize the object as lossy or lossless based on the depression and protrusion of the body contour\, and finally predict possible values for the object’s permittivity and thickness. Our resulting classification is good\, implying fewer nuisance alarms at check points. We have also trained a deep learning model for pixel-wise localization of body worn anomalies. The second project is a metal detection algorithm developed to monitor pedestrians walking along a sidewalk for large\, concealed metallic objects. Finite Difference Frequency Domain and SAR algorithms are used to simulate the images produced by this 6 GHz wideband radar system. A deep learning model has then been used to predict a pixel level mask for the body and anomaly based on the inputted radar image.
URL:https://coe.northeastern.edu/event/mahshid-asri-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231117T130000
DTEND;TZID=America/New_York:20231117T140000
DTSTAMP:20260521T014509
CREATED:20230816T195124Z
LAST-MODIFIED:20230816T195124Z
UID:37876-1700226000-1700229600@coe.northeastern.edu
SUMMARY:FacDev Fridays: How Faculty Can Support Student Mental Health
DESCRIPTION:Register for this event
URL:https://coe.northeastern.edu/event/facdev-fridays-how-faculty-can-support-student-mental-health/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231127T080000
DTEND;TZID=America/New_York:20231127T170000
DTSTAMP:20260521T014509
CREATED:20231127T163640Z
LAST-MODIFIED:20231127T163640Z
UID:40519-1701072000-1701104400@coe.northeastern.edu
SUMMARY:Bruno Souto Maior Muniz Morais PhD Dissertation Defense
DESCRIPTION:Title:\nEnabling Domain Platform Design for Streaming Applications: A Holistic Approach \nCommittee Members:\nGunar Schirner (Advisor)\nProf. David Kaeli\nProf. Hamed Tabkhi (UNCC) \nTime:\n10:00:00 AM \nLocation: ISEC 601 \nAbstract:\nIn recent years\, more demanding streaming applications make striking a balance between high compute performance and efficiency paramount in platforms designs for edge computing. In addition\, designing a platform that is optimized for a single application is costly due to non-recurring engineering (NRE) costs. In contrast\, multiple applications can be grouped in domains\, e.g. computer vision\, software-defined radio. Leveraging shared characteristics of similar applications within a domain\, e.g. structural composition/computation patterns\, a single domain platform that caters to these similarities and accelerates applications can be generated\, thus benefiting multiple applications at once and dramatically improving NRE and time-to-market (TTM). \nThis dissertation introduces methodologies atvarious abstraction levels to enable streamlined domain platform design for streaming applications. Thrust 1 introduces high level DSE methods based on integer linear programming (ILP)\, Tile-based Synchronization Aware ILP (TSAR-ILP). Initially\, single-application platform allocations are considered using TSAR-ILP. While TSAR-ILP only focuses on applications in isolation\, its formulation lays the foundations for DmTSAR-ILP\, a method that performs domain DSE with multiple applications\, obtaining an optimal unified platform allocation that and achieving an increase of 22.5% in throughput\, while being 70x faster when compared to previous methods (MG-DmDSE). However\, DmTSAR-ILP aims to aggregate all applications fairly. This presents a challenge when the designer wishes to focus on a subset of applications. To enable ultimate flexibility in a product-oriented setting\, modeled after a market analysis process\, this dissertation introduces ProdDSE. ProdDSE enables application prioritization while also introducing concurrent application modeling and a multi-objective optimization (area\, performance) approach. This enables up to a 3.4x boost in performance depending on use case\, while also providing gains in DSE runtime (4.3x faster). \nThrust 2 introduces Sedona\, a domain-specific language (DSL) and exploration enviroment that captures parametric dataflow application descriptions with language features dedicated to streaming applications. A design identified by Thrust 1 can be further refined using the tools in Thrust 2\, by capturing the connectivity of a design using Sedona. Then\, automatic wiring is performed for target outputs such as timing-aware simulations or RTL-level code\, enabling structural manipulation at a high-level description without the burden of low-level manual integration. \nFinally\, to better guide the high-level decisions performed in Thrust 1 and further exploration/integration in Thrust 2\, Thrust 3 considers the implications of HWACC topology choices in an HWACC-rich SoC. The ACTAR flow is introduced to explore different topologies in a RISC-V based SoC and the side-effects of topology and memory sizing choices on the system-wide performance and synchronization burdens due computation offloading to HWACCs. This produces valuable and actionable insights for designers to make informed choices on system-level compositions depending on application communication and computation demands.
URL:https://coe.northeastern.edu/event/bruno-souto-maior-muniz-morais-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231129T120000
DTEND;TZID=America/New_York:20231129T130000
DTSTAMP:20260521T014509
CREATED:20231019T134601Z
LAST-MODIFIED:20231019T134601Z
UID:39789-1701259200-1701262800@coe.northeastern.edu
SUMMARY:Chemical Engineering Fall Seminar Series: Professor Haotian Wang
DESCRIPTION:Electrochemical Approaches to Decarbonizing Fuels and Chemicals \nElectrochemical conversion of atmospheric molecules (CO2\, O2\, H2O\, N2) into fuels and chemicals represents a green and alternative route compared to traditional manufacturing approaches. However\, its practice is currently challenged at two systematic levels: the lack of active\, selective\, and stable electrocatalysts for efficient and reliable chemical bond transformations\, and the lack of novel catalytic reactors for practical reaction rates and efficient product separation. In this talk\, using CO2 reduction to gas and liquid products and O2 reduction to hydrogen peroxide as representative reactions\, I will introduce the rational design of both catalytic materials and reactors towards practical electrochemical manufacturing of fuels and chemicals. \n\nDr. Haotian Wang is currently an Associate Professor in the Department of Chemical and Biomolecular Engineering at Rice University. He obtained his PhD degree in the Department of Applied Physics at Stanford University in 2016 and his Bachelor of Science in Physics at the University of Science and Technology of China in 2011. In 2016 he received the Rowland Fellowship and began his independent research career at Harvard as a principal investigator. He was awarded the 2021 Sloan Fellow\, 2020 Packard Fellow\, 2019 CIFAR Azrieli Global Scholar\, 2019 Forbes 30 Under 30\, highly cited researchers\, etc. He serves as the editorial board of Communications Materials. His research group has been focused on developing novel nanomaterials for energy and environmental applications including energy storage\, chemical/fuel generation\, and water treatment.
URL:https://coe.northeastern.edu/event/chemical-engineering-fall-seminar-series-professor-haotian-wang/
LOCATION:010 Behrakis\, 360 Huntington Ave\, Boston\, MA\, 02115\, United States
GEO:42.3396156;-71.0886534
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=010 Behrakis 360 Huntington Ave Boston MA 02115 United States;X-APPLE-RADIUS=500;X-TITLE=360 Huntington Ave:geo:-71.0886534,42.3396156
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231129T150000
DTEND;TZID=America/New_York:20231129T160000
DTSTAMP:20260521T014509
CREATED:20231127T163756Z
LAST-MODIFIED:20231127T163756Z
UID:40521-1701270000-1701273600@coe.northeastern.edu
SUMMARY:Aria Masoomi PhD Proposal Review
DESCRIPTION:Title:\nMaking Deep Neural Network Transparent \nDate:\n11/29/2023 \nTime:\n3:00:00 pm \nCommittee Members:\nProf. Jennifer Dy (Advisor)\nProf. Mario Sznaier\nProf. Eduardo Sontag\nProf. Peter Castaldi \nAbstract:\nAs machine learning algorithms are deployed ubiquitously to a variety of domains\, it is imperative to make these often black-box models transparent.\nThe ability to interpret and comprehend the reasoning behind machine learning models plays a pivotal role in increasing  user trust. It not only offers insights into how a model functions but also opens avenues for model enhancements. \nThis research delves into the realm of interpretability\, focusing on the dichotomy between ‘intrinsic’ and ‘post hoc’ interpretability. Intrinsic interpretability involves constraining the complexity of the machine learning model itself\, resulting in models inherently interpretable due to their simplicity\, such as decision trees or sparse linear regression. On the other hand\, post hoc interpretability employs techniques that assess the model’s behavior after training\, offering insights into the model’s outcomes. Examples of post hoc techniques include permutation feature importance and the Shapley value method for feature importance. \nThe core contribution of this Thesis proposal lies in the development of novel methods to enhance both intrinsic and post hoc interpretability. These methods aim to advance the field by offering new perspectives on understanding machine learning models\, thereby contributing to the ongoing discourse on model transparency and user trust.
URL:https://coe.northeastern.edu/event/aria-masoomi-phd-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231129T163000
DTEND;TZID=America/New_York:20231129T170000
DTSTAMP:20260521T014509
CREATED:20231127T164136Z
LAST-MODIFIED:20231127T164136Z
UID:40525-1701275400-1701277200@coe.northeastern.edu
SUMMARY:Aria Masoomi PhD Proposal Review
DESCRIPTION:Title:\nMaking Deep Neural Network Transparent \nDate:\n11/29/2023 \nTime:\n4:30:00 PM \nCommittee Members:-\nProf. Jennifer Dy\nProf. Eduardo Sontag\nProf. Mario Sznaier\nProf. Peter Castaldi \nAbstract:\nAs machine learning algorithms are deployed ubiquitously to a variety of domains\, it is imperative to make these often black-box models transparent. The ability to interpret and comprehend the reasoning behind machine learning models plays a pivotal role in increasing user trust. It not only offers insights into how a model functions but also opens avenues for model enhancements. \nThis research delves into the realm of interpretability\, focusing on the dichotomy between ‘intrinsic’ and ‘post hoc’ interpretability. Intrinsic interpretability involves constraining the complexity of the machine learning model itself\, resulting in models inherently interpretable due to their simplicity\, such as decision trees or sparse linear regression. On the other hand\, post hoc interpretability employs techniques that assess the model’s behavior after training\, offering insights into the model’s outcomes. Examples of post hoc techniques include permutation feature importance and the Shapley value method for feature importance. \nThe core contribution of this Thesis proposal lies in the development of novel methods to enhance both intrinsic and post hoc interpretability. These methods aim to advance the field by offering new perspectives on understanding machine learning models\, thereby contributing to the ongoing discourse on model transparency and user trust.
URL:https://coe.northeastern.edu/event/aria-masoomi-phd-proposal-review-2/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231129T173000
DTEND;TZID=America/New_York:20231129T183000
DTSTAMP:20260521T014509
CREATED:20230907T172201Z
LAST-MODIFIED:20230907T172201Z
UID:38124-1701279000-1701282600@coe.northeastern.edu
SUMMARY:Gordon Institute Virtual Information Session
DESCRIPTION:Learn how you can earn a Graduate Certificate in Engineering Leadership as a stand-alone certificate or in combination with one of twenty-three Master of Science degrees offered through Northeastern’s College of Engineering\, College of Science\, or Khoury College of Computer Sciences. \nThe National Academy of Engineering recognized The Gordon Institute of Engineering Leadership (GIEL) for its innovative curriculum that combines technical education\, leadership capabilities\, and the “Challenge Project”: an opportunity for students to receive master’s level credit while working in industry. \nBy aligning technical proficiency with leadership capabilities\, GIEL accelerates the development of high-potential engineers and prepares them to lead complex projects early in their careers. Upon completing the program\, more than 88% of the 2022 class reported increased leadership responsibility\, while more than 50% of the 2022 class reported being promoted within one year of graduation. \nOur Director of Admissions will answer your application questions for Fall 2024. \nYou will have the opportunity to hear from Alumni on how The Gordon Institute propelled their engineering careers. Program professors will also be present to answer curriculum questions.
URL:https://coe.northeastern.edu/event/gordon-institute-virtual-information-session-18/
ORGANIZER;CN="Gordon Engineering Leadership program":MAILTO:gordonleadership@northeastern.edu
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231201T090000
DTEND;TZID=America/New_York:20231201T120000
DTSTAMP:20260521T014509
CREATED:20231116T213530Z
LAST-MODIFIED:20231116T213530Z
UID:40442-1701421200-1701432000@coe.northeastern.edu
SUMMARY:First-Year Engineering Fall Expos
DESCRIPTION:Join us for First-Year Engineering’s Fall Expos on Friday\, December 1\, from 9:00 AM – 12:00 PM and Monday\, December 4\, from 11:00 AM – 3:00 PM in the Curry Student Center Pit and Quad. Cornerstone of Engineering students will be showcasing their Fall projects. Themes include sumo robots\, sustainability\, carnival games\, animals and the natural world\, and interactive games. \n  \n 
URL:https://coe.northeastern.edu/event/first-year-engineering-fall-expos/2023-12-01/
LOCATION:Curry Student Center\, 360 Huntington Ave.\, Boston\, MA\, 02115\, United States
GEO:42.3394629;-71.0885286
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Curry Student Center 360 Huntington Ave. Boston MA 02115 United States;X-APPLE-RADIUS=500;X-TITLE=360 Huntington Ave.:geo:-71.0885286,42.3394629
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231204T103000
DTEND;TZID=America/New_York:20231204T113000
DTSTAMP:20260521T014509
CREATED:20231127T163905Z
LAST-MODIFIED:20231127T163905Z
UID:40523-1701685800-1701689400@coe.northeastern.edu
SUMMARY:Cheng Gongye PhD Dissertation Defense
DESCRIPTION:Title:\nHardware Security Vulnerabilities in Deep Neural Networks and Mitigations \nDate:\n12/4/2023 \nTime:\n10:30:00 AM \nCommittee Members:\nProf. Yunsi Fei (Advisor)\nProf. Aidong Ding\nProf. Xue Lin\nProf. Xiaolin Xu \nAbstract:\nIn the past decade\, Deep Neural Networks (DNNs) have become pivotal in numerous fields\, including security-sensitive autonomous driving and privacy-critical medical diagnosis. This Ph.D. dissertation delves into the hardware security of DNNs\, discovering their vulnerabilities to fault and side-channel attacks and exploring novel countermeasures essential for their safe deployment in critical applications. \nFault attacks disrupt computation or inject faults into parameters\, compromising the integrity of targeted applications. This dissertation demonstrates a power-glitching fault injection attack on FPGA-based DNN accelerators\, common in cloud environments\, which exploits vulnerabilities in the shared power distribution network and results in model misclassification. In response to these threats\, we introduce a novel\, lightweight defense mechanism to protect DNN parameters from adversarial bit-flip attacks. The proposed framework incorporates a dynamic channel-shuffling obfuscation scheme coupled with a logits-based model integrity monitor. The approach effectively safeguards various DNN models against bit-flip attacks\, without necessitating retraining or structural changes to the models. Furthermore\, our research expands the scope of fault analysis beyond just the parameters of DNN models. We thoroughly examine the entire implementation of commercial products\, defying the prevailing assumption that quantized DNNs are inherently resistant to bit-flips. \nSide-channel attacks exploit information leakage of system implementations\, such as power consumption and electromagnetic emanations\, to reveal system secrets and therefore compromise confidentiality. This dissertation makes significant contributions to side-channel assisted model extraction of DNNs. We present a floating-point timing side-channel attack on x86 CPUs that reverse-engineers DNN model parameters in software implementations. For hardware accelerators\, we target the state-of-the-art AMD-Xilinx deep-learning processor unit (DPU)\, a reconfigurable engine dedicated to convolutional neural networks (CNNs) and representing the most complex commercial FPGA accelerator with encrypted IPs. Our work demonstrates that electromagnetic analysis can be leveraged to recover the data flow and scheduling of the DNN accelerators\, facilitating follow-on architecture and parameter extraction attacks. To mitigate EM side-channel model extraction attacks\, we introduce a novel defense mechanism that devises a random importance-aware activation mask on input pixels to disrupt the operation alignment on EM traces\, with minimal performance and efficiency impacts. \nOverall\, this dissertation significantly deepens the understanding of hardware security of DNN models. It makes important contributions in discovering novel and critical vulnerabilities of DNN inference pertaining to system implementations\, and proposing effective and practical solutions for securing DNNs in mission-critical environments. The research work marks a substantial step forward in the development of resilient and secure AI systems.
URL:https://coe.northeastern.edu/event/cheng-gongye-phd-dissertation-defense/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231206T120000
DTEND;TZID=America/New_York:20231206T130000
DTSTAMP:20260521T014509
CREATED:20231019T134500Z
LAST-MODIFIED:20240102T154342Z
UID:39802-1701864000-1701867600@coe.northeastern.edu
SUMMARY:Chemical Engineering Fall Seminar Series: Professor Cameron Abrams
DESCRIPTION:Molecular Dynamics Investigations of Thermosetting Polymers \nThermosetting polymers comprise a wide variety of monomer constituents and polymerization chemistries that in principle provide the degrees of freedom necessary to tailor these materials to a broad range of applications\, from structural composites\, coatings and barrier materials\, ballistic shielding\, and even solid rocket fuels. In this talk\, I will trace my group’s history in using molecular dynamics simulations to investigate conceptual links among molecular architectures\, intermolecular interactions\, and network structures and how they determine thermomechanical properties of polymerized materials that these applications demand. Highlights in this history include the discovery of the links between crosslink arrangements and protovoid-based toughening; toughening using partially reacted substructures; long-timescale material response through time-temperature superposition; and rationalizing improvements over petrochemically derived monomers using novel bio-based subunits. A consistent theme will be demonstration of how close collaboration with experimental groups allows for simulation predictions to be tested. I will conclude with a presentation of our group’s software package\, HTPolyNet\, that represents the first opensource\, end-to-end generator of all-atom models of network-polymerized monomer mixtures based only on monomer structures\, which should accelerate the community’s use of MD simulation to investigate thermosetting polymers. \n\nCameron F. Abrams is the Bartlett ’81 – Barry ’81 Professor of Chemical and Biological Engineering at Drexel University\, where he has served on the faculty since 2002 and as Department Head since 2017. Abrams’ research expertise lies in advancing modern molecular simulation methods with applications in protein science\, drug discovery\, complex fluids\, and high-performance materials. He is the recipient of an ONR Young Investigator Award\, an NSF CAREER Award\, and the AIChE Computational and Molecular Sciences Forum Impact Award. He received a BS in Chemical Engineering from North Carolina State University in 1995 and a PhD from the University of California\, Berkeley\, in 2000. He trained as a postdoc for two years in the Theory Group at the Max-Planck-Institute for Polymer Research in Mainz\, Germany\, before joining Drexel.
URL:https://coe.northeastern.edu/event/chemical-engineering-fall-seminar-series-professor-cameron-abrams/
LOCATION:010 Behrakis\, 360 Huntington Ave\, Boston\, MA\, 02115\, United States
GEO:42.3396156;-71.0886534
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=010 Behrakis 360 Huntington Ave Boston MA 02115 United States;X-APPLE-RADIUS=500;X-TITLE=360 Huntington Ave:geo:-71.0886534,42.3396156
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231206T130000
DTEND;TZID=America/New_York:20231206T150000
DTSTAMP:20260521T014509
CREATED:20231121T182340Z
LAST-MODIFIED:20231121T182402Z
UID:40494-1701867600-1701874800@coe.northeastern.edu
SUMMARY:Enabling Engineering Fall Showcase
DESCRIPTION:Students will present their final design projects at the Enabling Engineering Fall Showcase. The projects that will be presented are listed below. \n\nAccessible Golfing\nCaution Radar\nAccessible Plant Watering System\nSwitch Activated Cornhole\nVR App\nSwitch Activated Toys\nAdaptive Drum Set\nProsthetic Finger\n\nEnabling Engineering is a Northeastern University student group that designs and builds devices to empower individuals with physical and cognitive disabilities. Our students collaborate with clients on projects that provide greater independence\, reduce medical burdens\, and increase social connectedness. We help family members\, clinicians\, and teachers care for people with disabilities. By giving students the opportunity to participate in Enabling Engineering projects\, we are training the next generation of engineers to be knowledgeable about\, and aware of\, the needs of individuals with disabilities. \nIf you are unable to join in person\, you can join via Zoom.
URL:https://coe.northeastern.edu/event/enabling-engineering-fall-showcase-2/
LOCATION:002 Ell Hall\, 360 Huntington Ave\, Boston\, MA\, 02115\, United States
ORGANIZER;CN="Enabling Engineering":MAILTO:enable@coe.neu.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231206T150000
DTEND;TZID=America/New_York:20231206T170000
DTSTAMP:20260521T014509
CREATED:20231204T185947Z
LAST-MODIFIED:20231204T185947Z
UID:40690-1701874800-1701882000@coe.northeastern.edu
SUMMARY:Suyash Pradhan MS Thesis Defense
DESCRIPTION:Title: COPILOT: Cooperative Perception using Lidar for Handoffs between Road Side Units \nCommittee Members:\nProf. Kaushik Chowdhury (Advisor)\nProf. Stratis Ioannidis\nProf. Jennifer Dy \nAbstract:\nThis thesis presents COPILOT\, a ML-based approach that allows vehicles requiring ubiquitous high bandwidth connectivity to identify the most suitable road side units (RSUs) through proactive handoffs. By cooperatively exchanging the data obtained from local 3D Lidar point clouds within adjacent vehicles and with coarse knowledge of their relative positions\, COPILOT identifies transient blockages to all candidate RSUs along the path under study. Such cooperative perception is critical for choosing RSUs with highly directional links required for mmWave bands\, which majorly degrade in the absence of LOS. COPILOT proposes three modules that operate in an inter-connected manner: (i) As an alternative to sending raw Lidar point clouds\, it extracts and transmits low-dimensional intermediate features to lower the overhead of inter-vehicle messaging; (ii) It utilizes an attention-mechanism to place greater emphasis on data collected from specific vehicles\, as opposed to nearest neighbor and distance-based selection schemes\, and (iii) it experimentally validates the outcomes using an outdoor testbed composed of an autonomous car and Talon AD7200 60GHz routers emulating the RSUs\, accompanied by the public release of the datasets. Results reveal COPILOT yields upto 69.8% and 20.42% improvement in latency and throughput compared to traditional reactive handoffs for mmWave networks\, respectively
URL:https://coe.northeastern.edu/event/suyash-pradhan-ms-thesis-defense/
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