BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Northeastern University College of Engineering - ECPv6.15.20//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Northeastern University College of Engineering
X-ORIGINAL-URL:https://coe.northeastern.edu
X-WR-CALDESC:Events for Northeastern University College of Engineering
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/New_York
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20220313T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20221106T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20230312T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20231105T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20240310T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20241103T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230602T110000
DTEND;TZID=America/New_York:20230602T120000
DTSTAMP:20260507T152914
CREATED:20230508T153647Z
LAST-MODIFIED:20230508T153647Z
UID:36931-1685703600-1685707200@coe.northeastern.edu
SUMMARY:Cheng Gongye's PhD Proposal Review
DESCRIPTION:“Hardware Security Vulnerabilities in Deep Neural Networks and Mitigations” \nCommittee Members:\nProf. Yunsi Fei (Advisor)\nProf. Xue Lin\nProf. Xiaolin Xu \nAbstract:\nOver the past decade\, Deep Neural Networks (DNNs) have revolutionized numerous fields. With the increasing deployment of DNN models in security-sensitive and mission-critical applications\, such as autonomous driving\, ensuring the security and privacy of DNN inference is of paramount importance. \nThis Ph.D. dissertation investigates two primary hardware security attack vectors: fault attacks and side-channel attacks. Fault attacks compromise the integrity of a targeted application by intentionally disrupting the computation or injecting faults on parameters. Side-channel attacks exploit information leakage from the application execution through physical parameters such as power consumption\, electromagnetic emanations\, and timing to retrieve secrets\, thereby breaching confidentiality. \nFor fault attacks\, we demonstrate a power-glitching fault injection attack on FPGA-based DNN accelerators in cloud environments. The attack exploits vulnerabilities in the shared power distribution network and leverages time-to-digital converter (TDC) sensors for precise fault injection timing\, and results in model misclassification\, an integrity compromise on the targeted application. We propose a lightweight defense framework for detecting and mitigating adversarial bit-flip attacks induced by RowHammer on DNNs. This framework employs a dynamic channel-shuffling obfuscation scheme and a logits-based model integrity monitor\, offering negligible performance loss. This framework effectively protects various DNN models from RowHammer attacks without any retraining or model structure modifications. \nFor side-channel attacks\, we present a floating-point timing side channels attack to reverse-engineer multi-layer perceptron (MLP) model parameters in software implementations. This attack successfully recovers DNN parameters\, weights and biases. \nRegarding ongoing research\, we observe that previous studies often focus on academic prototypes\, resulting in limited applicability. To bridge these gaps\, we select the AMD-Xilinx DPU\, one of the most advanced DNN accelerators to date\, to conduct the analysis. We propose a side-channel attack that utilizes electromagnetic emissions to extract parameters. Furthermore\, we propose a comprehensive fault analysis of quantized DNN models by simulations and discuss potential mitigation strategies.
URL:https://coe.northeastern.edu/event/cheng-gongyes-phd-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230602T110000
DTEND;TZID=America/New_York:20230602T120000
DTSTAMP:20260507T152914
CREATED:20230601T171608Z
LAST-MODIFIED:20230601T171608Z
UID:37168-1685703600-1685707200@coe.northeastern.edu
SUMMARY:MIE Seminar - Dr. Andrew Akbashev
DESCRIPTION:Please join us for a special seminar with Dr. Andrew Akbashev\, visiting from the Paul Scherrer Institute (Switzerland) to give a talk titled “Electrochemistry: Fundamental Research\, Academic Culture and Education”. \nHe is a leading expert in electrochemistry and catalysis research and has a large following on social media (>40\,000 followers on LinkedIn) discussing various issues in academia\, education\, and frequently shares valuable career advice for under/graduate students and early career professionals. As an example\, he has discussed “Tackling overpublishing by moving to open-ended papers” in Nature Materials. Further\, he is the founder and solo organizer of the Electrochemical Online Colloquium frequently attracting an audience of up to 700 attendees. \nPlease mark your calendars for this treat – the seminar will take place Friday 6/2\, from 11:00 am to 12:00 pm in 168 Snell Engineering and via Zoom. Light snacks will be served.
URL:https://coe.northeastern.edu/event/mie-seminar-dr-andrew-akbashev/
LOCATION:168 SN\, 360 Huntington Ave\, Boston\, MA\, 02115\, United States
CATEGORIES:use the department, audience, and topic lists
ORGANIZER;CN="Mechanical & Industrial Engineering":MAILTO:mie-web@coe.neu.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230603T100000
DTEND;TZID=America/New_York:20230603T110000
DTSTAMP:20260507T152914
CREATED:20230531T154921Z
LAST-MODIFIED:20230627T171530Z
UID:37145-1685786400-1685790000@coe.northeastern.edu
SUMMARY:Graduate School of Engineering Campus Tour (In Person)
DESCRIPTION:Interested to learn more about the Graduate School of Engineering on the Boston campus? Then we welcome you to sign up for a Graduate School of Engineering campus tour! Led by one of our expert Graduate Student Ambassadors\, we’ll show you key locations on campus\, in addition to resources specific to Engineering\, and answer your questions about Boston. Please complete the registration form linked below to select the date and time that works best for you. Tours are open to both newly admitted and prospective students. We can’t wait to meet you!
URL:https://coe.northeastern.edu/event/graduate-school-of-engineering-campus-tour-in-person-3/2023-06-03/
ORGANIZER;CN="Graduate School of Engineering":MAILTO:coe-gradadmissions@northeastern.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230605T110000
DTEND;TZID=America/New_York:20230605T123000
DTSTAMP:20260507T152914
CREATED:20230522T171405Z
LAST-MODIFIED:20230522T171405Z
UID:37079-1685962800-1685968200@coe.northeastern.edu
SUMMARY:Can Qin's PhD Dissertation Defense
DESCRIPTION:“Unveiling the Power of Transfer Learning in Data-Driven AI” \nCommittee Members:\nProf. Raymond Fu (Advisor)\nProf. Octavia Camps\nProf. Huaizu Jiang \nAbstract:\nThe big data stands as a cornerstone of deep learning\, which has significantly improved a wide range of machine learning and computer vision tasks. Despite such a great success\, data collection is time-consuming and costly\, considering manual efforts and privacy restrictions. Transfer learning is a promising direction toward data-efficient AI by leveraging acquired data and pre-trained models as guidance. This dissertation focus on the feature and model transfer across different domains and tasks\, which can be roughly summarized into three sections. \n(1) Section One focuses on Unsupervised Domain Adaptation (UDA) without any labels in the target domain. The technical challenge of UDA is the distribution mismatch across domains. I have presented a hierarchical alignment model as the solution. \n(2) Section Two extends UDA into semi-supervised domain adaptation (SSDA) with minimal target-domain labels\, which is useful and effortless to acquire. To avoid overfitting toward labeled data\, I have proposed structural regularization verified on different classification benchmarks. \n(3) Section Three mainly explores the model transfer\, including teacher-student knowledge distillation and heterogeneous models infusion with a high potential for model compression and enhancement.
URL:https://coe.northeastern.edu/event/can-qins-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230606T100000
DTEND;TZID=America/New_York:20230606T110000
DTSTAMP:20260507T152914
CREATED:20230405T134807Z
LAST-MODIFIED:20230516T202454Z
UID:36470-1686045600-1686049200@coe.northeastern.edu
SUMMARY:Civil and Environmental Engineering Alumni Feature
DESCRIPTION:The College of Engineering is excited to host a Civil and Environmental Engineering Alumni Feature event on Tuesday\, June 6\, 2023\, at 10:00am ET. We’d love to see you there. \nAs a Northeastern student\, you will have the future benefit of joining a robust alumni network that connects you with engineers around the world with exciting experiences and beneficial connections. In this webinar\, you’ll learn from a Civil and Environmental alum where you will have the opportunity to hear about their Northeastern experience and how that set them up for their success in their industry today. \nTopics this event will cover include: \n\nTime as a student: Learn from our alum on how their time as a student at Northeastern University prepared them for the career they have today.\nTransition to Industry: Our alum will share their experiences in industry and how they were able to use their Northeastern education to excel in the field. \nAlumni Network: Northeastern University has alumni across the country and around the world. Learn how those connections remain beneficial to you after graduation.\n\nReserve your spot today and join us on June 6\, 2023\, at 10:00am ET.
URL:https://coe.northeastern.edu/event/civil-and-environmental-engineering-alumni-feature/
ORGANIZER;CN="Civil & Environmental Engineering":MAILTO:civilinfo@coe.neu.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230612T080000
DTEND;TZID=America/New_York:20230612T100000
DTSTAMP:20260507T152914
CREATED:20230606T175132Z
LAST-MODIFIED:20230606T175200Z
UID:37198-1686556800-1686564000@coe.northeastern.edu
SUMMARY:The Hague Space Diplomacy Symposium
DESCRIPTION:The Center for International Affairs and World Cultures (CIAWC) is convening an international symposium on space diplomacy in the Hague with the University of Leiden. Hosted by Mai’a Cross\, Director of the Center for International Affairs and World Cultures; Dean’s Professor of Political Science\, International Affairs\, and Diplomacy\, who will also provide the keynote\, and Jan Melissen\, the symposium features interdisciplinary experts from the US\, Asia\, and Europe on the future challenges and opportunities of outer space exploration and cooperation. \nDiscussions will be aimed at policy and practitioner audiences from both the public and private sectors\, and will address major issues in international policy\, governance\, law\, business\, communications\, security\, materials\, and sustainability. This is the second in a series of workshops showcasing the research findings of CIAWC’s Global Spotlight Project on Space Diplomacy\, which recently resulted in the publication of a special issue in the Hague Journal of Diplomacy. \nView the agenda and RSVP to attend
URL:https://coe.northeastern.edu/event/the-hague-space-diplomacy-symposium/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230612T193000
DTEND;TZID=America/New_York:20230612T203000
DTSTAMP:20260507T152914
CREATED:20230531T134757Z
LAST-MODIFIED:20230531T134757Z
UID:37136-1686598200-1686601800@coe.northeastern.edu
SUMMARY:Art and Science: Art exhibit on bones at Needham Public Library
DESCRIPTION:Professor Sandra Shefelbine and local artist Colleen Pearce have joined together to bring an art exhibit about bones to the Needham Public Library Jun 1-July 15\, with a talk by Professor Shefelbine on June 12 at 7:30pm in the Library Community Room. The talk will be about bones – their structure and the importance of mechanical load and the process of an artist and scientist coming together to share scientific knowledge and creative expression. \nRegistration required (free): https://needhamma.assabetinteractive.com/calendar/collaboration-of-an-artist-and-a-scientist-exploring-bones/ \n 
URL:https://coe.northeastern.edu/event/art-and-science-art-exhibit-on-bones-at-needham-public-library/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230613T173000
DTEND;TZID=America/New_York:20230613T191500
DTSTAMP:20260507T152914
CREATED:20230522T181116Z
LAST-MODIFIED:20230616T221155Z
UID:37090-1686677400-1686683700@coe.northeastern.edu
SUMMARY:Ignorance Is Bliss: A Career Retrospective
DESCRIPTION:  \nDean Gregory D. Abowd will present his SIGCHI Lifetime Research Award Acceptance Lecture \nDate: Tues.\, June 13\, 2023 \nTime: 5:30 to 7:15 PM\, reception following Dean Abowd’s talk \nPlace: In-Person and Livestream\nBostonCHI meeting at Northeastern University in ISEC Auditorium (102 ISEC)\, and reception in ISEC Atrium \nRegistration is appreciated but not required. View BostonCHI for more information. \nPresentation Abstract: In 1988\, as a graduate student grappling to find a research identity\, Gregory D. Abowd accidentally discovered the field of Human Computer Interaction (HCI). Over the past 35 years\, he pursued a passion for applying the tools and techniques of computing to uncover how the human experience with technology can be understood and transformed. That leap into HCI was just the first of a number of leaps of faith. Abowd’s career has been a series of shifting research agendas\, each one inspired by some life events. In all cases\, he was buoyed by a bevy of talented and supportive colleagues\, advisors and advisees alike\, who gave him the courage to jump into a research topic that he didn’t know much about. That “ignorance” has allowed him to be more fearless than he had the right to be. In this talk\, Abowd will reflect on his professional journey\, hoping to inspire others to dispel fear of the unknown and unlock their potential. Life\, like research\, is best when shared with others whom you can respect and befriend. \n—————————————— \nGregory D. Abowd\, dean of the College of Engineering and professor of electrical and computer engineering at Northeastern University\, has received the Lifetime Research Award from the Association for Computing Machinery’s (ACM) Special Interest Group on Computer-Human Interaction (SIGCHI). The award is presented to individuals for “the best\, most fundamental\, and influential research contributions to the study of human-computer interaction (HCI)” and is awarded for a lifetime of innovation and leadership. \n 
URL:https://coe.northeastern.edu/event/ignorance-is-bliss-a-career-retrospective/
LOCATION:102 ISEC\, 360 Huntington Ave\, 102 ISEC\, Boston\, MA\, 02115\, United States
GEO:42.3377335;-71.0869121
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=102 ISEC 360 Huntington Ave 102 ISEC Boston MA 02115 United States;X-APPLE-RADIUS=500;X-TITLE=360 Huntington Ave\, 102 ISEC:geo:-71.0869121,42.3377335
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230614T090000
DTEND;TZID=America/New_York:20230614T170000
DTSTAMP:20260507T152914
CREATED:20230508T173630Z
LAST-MODIFIED:20230508T173630Z
UID:36939-1686733200-1686762000@coe.northeastern.edu
SUMMARY:Bioengineering Research Symposium
DESCRIPTION:Join us for the annual bioengineering research symposium on June 14th\, 9am-5pm in the ISEC Atrium and Auditorium. There will be oral and poster presentations by students\, a 5-minute thesis competition and awards. \nAll are welcome!
URL:https://coe.northeastern.edu/event/bioengineering-research-symposium/
LOCATION:ISEC Auditorium\, 805 Columbus Ave\, Boston\, MA\, 02115\, United States
ORGANIZER;CN="Bioengineering":MAILTO:bioe@northeastern.edu
GEO:42.3377049;-71.0870109
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=ISEC Auditorium 805 Columbus Ave Boston MA 02115 United States;X-APPLE-RADIUS=500;X-TITLE=805 Columbus Ave:geo:-71.0870109,42.3377049
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230614T173000
DTEND;TZID=America/New_York:20230614T183000
DTSTAMP:20260507T152914
CREATED:20230424T144517Z
LAST-MODIFIED:20230424T144517Z
UID:36805-1686763800-1686767400@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 plus 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 2021 class reported increased leadership responsibility\, while more than 50% of the 2021 class reported being promoted within one year of graduation.  \nOur Director of Admissions will answer your application questions for Fall 2023.  \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-16/
ORGANIZER;CN="Gordon Engineering Leadership program":MAILTO:gordonleadership@northeastern.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230616T140000
DTEND;TZID=America/New_York:20230616T150000
DTSTAMP:20260507T152914
CREATED:20230613T202130Z
LAST-MODIFIED:20230613T202130Z
UID:37232-1686924000-1686927600@coe.northeastern.edu
SUMMARY:Jiacheng Shi’s PhD Dissertation Defense
DESCRIPTION:Title: \nTowards a Programmable\, High Speed\, and Robust Internet of Underwater Things \nLocation: \nISEC 232  \nCommittee Members: \nProf. Tommaso Melodia (Advisor) \nProf. Stefano Basagni \nProf. Kaushik Chowdhury \nAbstract: \nIncreasing demand of underwater exploration requires a platform with higher data rate\, more robust performance\, and hardware/software flexibility. The biggest challenge to realize these networked platforms is due to the availability of narrow bandwidth and the long propagation delay of acoustic wave transmission\, which suffers from much less attenuation than radio-frequency (RF) electromagnetic waves. Meanwhile\, wirelessly networked systems of underwater devices are becoming the basis of many commercial and scientific activities at sea. However\, existing commercial modems are built with fixed hardware and insufficient data rate. Therefore\, there is a demand for a platform to fully support higher data rate\, underwater acoustic network functionalities and applications\, as well as the capability of adapting their communication parameters in real time based on the environmental conditions. \nTowards addressing these challenges\, we first propose the SEANet Project\, which is supported by the NSF\, intends to develop a new generation of programmable platforms and a networking testbed to enable the goal of a programmable Internet of Underwater Things (IoUT). SEANet is built on new software-defined platforms with an open architecture that will allow users to specify\, add\, upgrade\, and swap new hardware and software components with ease. Over short and moderate range links\, SEANet is designed to offer data speeds at least one order of magnitude higher than existing commercial systems. Moreover\, a new MIMO-OFDM transceiver configuration is suggested. We show a prototype of a 2×2 MIMO-OFDM transceiver node for the Internet of Underwater Things (IoUT)\, which intends to build a new generation of programmable and portable platforms a networking testbed with real-time processing and reconfiguration. The suggested receiver operates on a block-by-block basis\, using pilot subcarriers for channel estimation to prevent matrix inversion and accommodate the hardware’s limited resources. We also use space-frequency block coding (SFBC) for transmission diversity. In addition\, null-carrier based Doppler compensation enables high-resolution uniform Doppler drifting. We show that the suggested MIMO-OFDM IUoT prototype can support data speeds of up to 600kbit/s with 16QAM modulation. \nWe also investigate physical layer signal processing approaches to further enhance the robustness of underwater acoustic communication. First\, we propose a novel modulation scheme based on Quasi-Orthogonal Chirp Multiplexing (QOCM). We introduce two sets of mutually quasi-orthogonal chirp signals\, which are employed as transmission subcarriers. We provide the QOCM transmitter and receiver design\, as well as digital implementation. The results of simulations and tank experiments reveal that QOCM has a high data rate and robustness against Doppler effect.
URL:https://coe.northeastern.edu/event/jiacheng-shis-phd-dissertation-defense/
LOCATION:232 ISEC\, 360 Huntington Ave\, Boston\, MA\, 02115\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230618T080000
DTEND;TZID=America/New_York:20230623T170000
DTSTAMP:20260507T152914
CREATED:20230127T165522Z
LAST-MODIFIED:20230127T165522Z
UID:35294-1687075200-1687539600@coe.northeastern.edu
SUMMARY:Reconnect Workshop 2023: Risk Assessment
DESCRIPTION:Where: The Omni Hotel\, Providence\, Rhode Island   \nThis year’s workshop is focused on Risk Assessment. Risk assessment is an overall process of identifying\, analyzing\, and preparing for potential risks (e.g.\, natural or man-made disasters)\, which is extremely important to ensure the continuity of organization operations and the well-being of the people involved. Risk assessment goals include (a) a better understanding of the vulnerability and the potential impact on the organization and people involved\, and (b) prescribing control measures and contingency plans to minimize the potential impacts. Reconnect 2023 will review classic methods and tools for risk assessment for various risks and hazards through real-world case studies. Several researchers affiliated with the newly established DHS Center of Excellence\, SENTRY\, will provide an overview of SENTRY’s research mission and offer examples of how they will use risk assessment methods in the center’s research. These include allocating resources for disaster management facing adaptive adversaries\, deploying “virtual sentries” to protect civilian spaces—so-called “soft targets”—around the country\, and several others. Registration fees\, lodging\, meals\, and travel: Accepted participants from US academic institutions: registration\, lodging in a single room\, and meals will be provided at no charge. Limited funds may be available to provide partial support for travel.   \nThe application deadline is March 15\, 2023\, or until all slots are filled. Click here to apply. \nOrganizers: Margaret (Midge) Cozzens\, DIMACS\, Rutgers University; Tamra Carpenter\, Rutgers University Jun Zhuang\, University at Buffalo Primary Speakers: Jun Zhuang\, University at Buffalo; Allison Coffey Reilly\, University of Maryland\, Seth Guikema\, University of Michigan; and Auroop Ganguly\, Northeastern University  
URL:https://coe.northeastern.edu/event/reconnect-workshop-2023-risk-assessment/
ORGANIZER;CN="ALERT":MAILTO:alert-info@coe.neu.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230619T140000
DTEND;TZID=America/New_York:20230619T160000
DTSTAMP:20260507T152914
CREATED:20230701T214046Z
LAST-MODIFIED:20230701T214046Z
UID:37315-1687183200-1687190400@coe.northeastern.edu
SUMMARY:CommLab Writing Group
DESCRIPTION:Join us weekly in the Curry Student Center\, room 433. \nSetting and sticking to a consistent writing schedule is key to improving skills and accomplishing your writing tasks (don’t just take our word for it). \nCOE graduate students are invited to join the CommLab writing group to share best practices\, get feedback from your peers\, and work on any writing project (dissertation\, thesis\, manuscript\, fellowship\, poster\, presentation\, or other forms of technical communications). We will meet weekly for 2 hours; you do not have to attend all the sessions or the full 2 hours to participate.
URL:https://coe.northeastern.edu/event/commlab-writing-group-2/2023-06-19/
LOCATION:Curry Student Center\, 360 Huntington Ave.\, Boston\, MA\, 02115\, United States
GEO:42.3394629;-71.0885286
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Curry Student Center 360 Huntington Ave. Boston MA 02115 United States;X-APPLE-RADIUS=500;X-TITLE=360 Huntington Ave.:geo:-71.0885286,42.3394629
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20230620
DTEND;VALUE=DATE:20230624
DTSTAMP:20260507T152914
CREATED:20230518T135356Z
LAST-MODIFIED:20230518T135448Z
UID:37050-1687219200-1687564799@coe.northeastern.edu
SUMMARY:2023 AEESP Conference Hosted at Northeastern University
DESCRIPTION:Northeastern University is proud to present the 2023 AEESP Research and Education Conference\, organized in collaboration with a large team from universities in New England including Massachusetts Institute of Technology\, Tufts University\, University of Connecticut\, University of Maine\, University of Massachusetts-Amherst\, University of New Hampshire\, and University of Rhode Island.  This conference will be welcoming participants from across the country and internationally\, as well as representatives from industry\, for 4 days of sharing\, learning\, and planning. The conference activities will commence on Tuesday\, June 20 and continue through Friday\, June 23\, under the theme “Responding Together to Global Challenges”. The conference and its attendees will reflect the many contributions environmental scientists and engineers are making to address concerning global\, historical\, and emerging challenges to living in a healthy\, safe\, and just society such as a changing climate\, emerging environmental quality and human health threats\, aging infrastructure and networks\, marginalization of communities\, and evolving education demands. \nThe Association of Environmental Engineering and Science Professors (AEESP) is a global organization of professors in academic programs who strive to provide education in the sciences and technologies of environmental protection. Founded in 1963\, the Association has grown to universities around the world\, facilitating improving education and research programs and encouraging graduate education. “This conference is an important meeting to exchange novel ideas in environmental research and education. We are proud to host academics\, students\, and friends from far and wide who will be sharing their recent advances in their fields of study\,” said Philip Larese-Casanova\, Associate Professor at Northeastern University\, who also serves as the Conference Chair. In addition\, the conference also serves the profession by providing information to government agencies and the public and provides direct benefits to its members. \nUnique elements of this year’s conference include a career fair and outreach activities on Conference Day 1\, a gala dinner on Day 2 at the New England Aquarium\, and a series of field trips in and around Boston on Friday June 23 to round out the week! The conference schedule includes the conventional and highly sought-after workshops and technical presentations. Students\, post-docs\, and faculty will find ample organized opportunities for networking\, career development\, and community outreach at the conference. \nRegistration is open through June 5 for the 2023 AEESP Conference!
URL:https://coe.northeastern.edu/event/2023-aeesp-conference-hosted-at-northeastern-university/
ORGANIZER;CN="Civil & Environmental Engineering":MAILTO:civilinfo@coe.neu.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230620T080000
DTEND;TZID=America/New_York:20230620T170000
DTSTAMP:20260507T152914
CREATED:20230624T181028Z
LAST-MODIFIED:20230624T181028Z
UID:37261-1687248000-1687280400@coe.northeastern.edu
SUMMARY:Alfred P. Navato's PhD Dissertation Defense
DESCRIPTION:Title:\nEnabling Anomaly Detection in Complex Chemical Mixtures Through Multimodal Data Fusion \nDate:\n6/26/2023 \nTime:\n10:00:00 AM \nLocation:\nSH 210\, \nCommittee Members:\nProf. Mueller (Advisor)\nProf. Erdogmus\nProf. Ioannidis\nProf. Onnis-Hayden \nAbstract:\nRecently innovations in machine learning and data processing are increasingly tied to ensuring useability and interpretability when these methods are applied within end-user domains.  One societally important example of such a domain is management and operations of water infrastructure in cities\, where data collection is currently costly and limited\, enabling analytics have the potential to generate real impact for urban communities\, and correctness of results is critical to protect human and environmental health.  This dissertation holistically considers issues of generalizability\, transferability\, and applicability of a range of data fusion and machine learning approaches across end-user domains within the context of solution building for improved real-time management of wastewater infrastructure.  The first chapter provides an overview of the challenges associated with anomaly detection within the wastewater field and reviews the performance of various anomaly detection techniques implemented in other disciplines.  The second chapter discusses the barriers and opportunities in cross-disciplinary pollination of data fusion techniques.  The third chapter presents development of an unsupervised approach facilitating quantitative characterization of the complex background which is wastewater\, necessary to be able to implement any automated operational interventions.  The fourth chapter develops an approach for cost-minimization/information-maximization design of a sensor to facilitate specifically detection of chemical anomalies (defined as inflow events that might compromise wastewater treatment facilities) by using machine learning and feature selection techniques to minimize the number of input signals needed to achieve reasonable accuracies.  Together the third and fourth chapters provide a clear\, explainable\, actionable pathway forward in envisioning next generation wastewater infrastructure\, demonstrating novel and impactful use of data fusion and machine learning techniques in a real-world context.
URL:https://coe.northeastern.edu/event/alfred-p-navatos-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230620T130000
DTEND;TZID=America/New_York:20230620T140000
DTSTAMP:20260507T152914
CREATED:20230522T172041Z
LAST-MODIFIED:20230522T172041Z
UID:37070-1687266000-1687269600@coe.northeastern.edu
SUMMARY:Chang Liu's PhD Dissertation Defense
DESCRIPTION:“Unleashing the Potential of Transfer Learning for Visual Applications” \nCommittee Members:\nProf. Raymond Fu (Advisor)\nProf. Sarah Ostadabbas\nProf. Zhiqiang Tao \nAbstract:\nThe recent flourish of deep learning in various tasks is largely accredited to the rich and accessible labeled data. Nonetheless\, massive supervision remains a luxury for many real-world applications. Further\, the domain shift problem has also seriously impeded large-scale deployments of deep-learning models. As a remedy\, Transfer learning aims at improving the performance of target learners on target domains by transferring the knowledge contained in different but related source domains. In this way\, the dependence on a large number of target domain data can be reduced for constructing target learners. \nIn this dissertation research\, I investigate two major problems in transfer learning\, domain adaptation (DG) and domain adaptation (DA)\, on various visual applications. (1) The challenge of DG lies in an over-simplified assumption\, that is\, the source and target data are independent and identically distributed (i.i.d.) while ignoring out-of-distribution (OOD) scenarios commonly encountered in practice. This issue is common in visual applications such as object recognition\, hyperparameter optimization\, and face recognition. We propose algorithms that are specifically designed for each task\, such as metric learning\, adversarial regularization\, feature disentanglement\, and meta-learning. (2) DA can be considered a special case of DG with unlabeled target data available. The major challenge is how to align the labeled source and unlabeled target data. We delve into the applications of image recognition and video recognition and propose algorithms to ensure domain-wise discriminativeness and class-wise closeness across domains. Experiments show that the proposed algorithms outperform the state-of-the-art methods on the commonly-used benchmark datasets.
URL:https://coe.northeastern.edu/event/chang-lius-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230623T100000
DTEND;TZID=America/New_York:20230623T110000
DTSTAMP:20260507T152914
CREATED:20230606T153237Z
LAST-MODIFIED:20230606T153237Z
UID:37194-1687514400-1687518000@coe.northeastern.edu
SUMMARY:Cooper Loughlin's PhD Dissertation Defense
DESCRIPTION:“Deep Generative Models for High Dimensional Spatial and Temporal Data Analysis” \nCommittee Members:\nProf. Vinay Ingle (Advisor)\nDr. Dimitris Manolakis\nProf. Purnima Ratilal-Makris \nAbstract:\nData analysis and exploitation in practical applications is challenging when observations are the result of many interacting natural and man-made phenomena. We address two important problems for which traditional methods of analysis are insufficient. One problem of practical interest is the identification of particular materials from remotely sensed hyperspectral imagery. This is traditionally accomplished by comparing image pixel spectra to those from a known material library. Such techniques are limited by spectral variability\, background interference\, and imperfect compensation of atmospheric components. Established methods address these limitations with statistical techniques. Simple probability models result in tractable methods; however\, analyses are limited by errors due\, in particular\, due to false alarms. \nAnalysis of complex time series is another challenging problem\, particularly when data are high dimensional. This arises in air quality monitoring\, where atmospheric concentration measurements of multiple pollutants are taken over time. Two analysis goals in this context are forecasting and anomaly detection. Both tasks are enabled by an accurate model for the temporal dynamics and interaction between pollutants. Air quality data are complex due to long term temporal dependencies\, non-linear dependence between pollutants\, and missing observations. Traditional multivariate time series analysis approaches\, such as the vector autoregression and linear dynamical system models\, fail to capture those characteristics necessary for a sufficient probabilistic model. \nWe use deep generative models to develop practical solutions that address these problems. This is made possible through the application of deep latent variable models. The modeling approach follows the philosophy that complex data can typically be explained by simpler underlying factors of variation. Variational autoencoders (VAEs) are deep latent variable models that emulate data generation by transforming simple\, low dimensional\, latent random vectors through a deep neural network. VAEs are trained to produce samples that resemble the training data\, thus capturing a manifold on which complex data are distributed. This philosophy is extended to time series data\, where we consider sequences of latent vectors. \nWe utilize VAEs develop a flexible generative model for hyperspectral imagery. Based on that model\, we develop a novel material identification framework which localizes target material spectra along the manifold. Through experiments on real data\, we show that the \ac{VAE} approach is better able to reject false alarms from materials with similar spectra when compared to established methods alone. We additionally develop a novel dynamical \ac{VAE} model for time series of air quality data. Using that model\, we develop practical methods for computing forecast distributions using Monte Carlo integration. We evaluate forecast distributions against real air quality data and demonstrate the ability to predict temporal dynamics and forecast uncertainty. The primary contribution of this work is to develop practical solutions to challenging data analysis problems through the use of deep generative models.
URL:https://coe.northeastern.edu/event/cooper-loughlins-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20230626
DTEND;VALUE=DATE:20230629
DTSTAMP:20260507T152914
CREATED:20230612T135208Z
LAST-MODIFIED:20230612T135208Z
UID:37215-1687737600-1687996799@coe.northeastern.edu
SUMMARY:2023 ASEE Conference- Baltimore\, MA
DESCRIPTION:Northeastern College of Engineering will attend ASEE 2023 Annual Conference this year at the Baltimore Convention Center\, MA. Join us to learn about Northeastern’s graduate engineering programs from Sunday\, June 25th to Wednesday\, June 28th! Our booth number is 98. \n  \n 
URL:https://coe.northeastern.edu/event/2023-asee-conference-baltimore-ma/
ORGANIZER;CN="Graduate School of Engineering":MAILTO:coe-gradadmissions@northeastern.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230626T083000
DTEND;TZID=America/New_York:20230626T093000
DTSTAMP:20260507T152914
CREATED:20230624T180848Z
LAST-MODIFIED:20230624T180848Z
UID:37265-1687768200-1687771800@coe.northeastern.edu
SUMMARY:Deniz Unal's PhD Proposal Review
DESCRIPTION:Title:\nSoftware-Defined Underwater Acoustic Networks \nCommittee Members:\nProf. Tommaso Melodia (Advisor)\nProf. Stefano Basagni\nProf. Kaushik Chowdhury\nDr. Emrecan Demirors \nAbstract:\nThe exploration\, monitoring\, and understanding of oceans play a crucial role in addressing climate change\, overseeing underwater pipelines\, and preventing maritime warfare attacks. To achieve these significant objectives\, it is vital to utilize networks of cost-effective and flexible underwater devices capable of efficiently collecting and transmitting information to the shore. However\, the progress of underwater networks heavily relies on underwater acoustic modems\, which currently face limitations such as low data rates and inflexible hardware designs\, limiting their usability to specific scenarios. To overcome these limitations\, we propose a modular software-defined acoustic networking platform built on the Zynq system-on-chip architecture that can be easily deployed in a compact form factor. Our platform distinguishes itself from existing solutions in several ways. Firstly\, it possesses the capability to adapt to varying conditions by adjusting protocol parameters at all layers of the networking stack. Secondly\, it achieves high data rate connections\, particularly over short distances. Additionally\, it seamlessly integrates with other sub-sea platforms\, including underwater drones. We demonstrate the capabilities and the performance of our platform with tasks\, such as channel estimation and characterization\, establishing high data rate Orthogonal Frequency-Division Multiplexing (OFDM) links\, and running third-party software to implement JANUS standard. In addition\, we introduce the enabling technologies for the development and implementation of underwater networks. These technologies facilitate the establishment of connectivity between underwater networks and the shore\, as well as the integration of modems with underwater vehicles. Lastly\, we provide a demonstration of the algorithmic development conducted on our platform. We mainly consider high-rate\, wideband\, adaptive links and perform experimental evaluations at sea. In particular\, we demonstrate multicarrier communications with mobile platforms with the presence of Doppler and compare the performance of forward error correction methods\, and demonstrate dataset recording for artificial intelligence research.
URL:https://coe.northeastern.edu/event/deniz-unals-phd-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230629T170000
DTEND;TZID=America/New_York:20230629T173000
DTSTAMP:20260507T152914
CREATED:20230626T173009Z
LAST-MODIFIED:20230626T173036Z
UID:37275-1688058000-1688059800@coe.northeastern.edu
SUMMARY:Zifeng Wang's PhD Dissertation Defense
DESCRIPTION:Title: Effective and Efficient Continual Learning \nCommittee Members:\nProf. Jennifer Dy (Advisor)\nProf. Stratis Ioannidis\nProf. Yanzhi Wang \nAbstract:\nContinual Learning (CL) aims to develop models that mimic the human ability to learn continually without forgetting knowledge acquired earlier. While traditional machine learning methods focus on learning with a certain dataset (task)\, CL methods adapt a single model to learn a sequence of tasks continually. \nIn this thesis\, we target developing effective and efficient CL methods under different challenging and resource-limited settings. Specifically\, we (1) leverage the idea of sparsity to achieve cost-effective CL\, (2) propose a novel prompting-based paradigm for parameter-efficient CL\, and (3) utilize task-invariant and task-specific knowledge to enhance existing CL methods in a general way. \nWe first introduce our sparsity-based CL methods. The first method\, Learn-Prune-Share (LPS)\, splits the network into task-specific partitions\, leading to no forgetting\, while maintaining memory efficiency. Moreover\, LPS integrates a novel selective knowledge sharing scheme\, enabling adaptive knowledge sharing in an end-to-end fashion. Taking a step further\, we present Sparse Continual Learning (SparCL)\, a novel framework that leverages sparsity to enable cost-effective continual learning on edge devices. SparCL achieves both training acceleration and accuracy preservation through the synergy of three aspects: weight sparsity\, data efficiency\, and gradient sparsity. \nSecondly\, we present a new paradigm\, prompting-based CL\, that aims to train a more succinct memory system that is both data and memory efficient. We first propose a method that learns to dynamically prompt (L2P) a pre-trained model to learn tasks sequentially under different task transitions\, where prompts are small learnable parameters maintained in a memory space. We then improve L2P by proposing DualPrompt\, which decouples prompts into complementary “General” and “Expert” prompts to learn task-invariant and task-specific instructions\, respectively. \nFinally\, we propose DualHSIC\, a simple and effective CL method that generalizes the idea of leveraging task-invariant and task-specific knowledge. DualHSIC consists of two complementary components that stem from the so-called Hilbert Schmidt independence criterion (HSIC): HSIC-Bottleneck for Rehearsal (HBR) lessens the inter-task interference and HSIC Alignment (HA) promotes task-invariant knowledge sharing. \nComprehensive experimental results demonstrate the effectiveness and efficiency of our methods over the state-of-the-art methods on multiple CL benchmarks.
URL:https://coe.northeastern.edu/event/zifeng-wangs-phd-dissertation-defense/
END:VEVENT
END:VCALENDAR