Northeastern University College of Engineering

Graduate School of Engineering Campus Tour (In Person)
2023-06-03 10:00 - 11:00 (America/New_York)
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!

Can Qin's PhD Dissertation Defense
2023-06-05 11:00 - 12:30
“Unveiling the Power of Transfer Learning in Data-Driven AI”
Committee Members:
Prof. Raymond Fu (Advisor)
Prof. Octavia Camps
Prof. Huaizu Jiang
Abstract:
The 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.
(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.
(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.
(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.

Civil and Environmental Engineering Alumni Feature
2023-06-06 10:00 - 11:00
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.
As 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.
Topics this event will cover include:

Time 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.
Transition 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.
Alumni Network: Northeastern University has alumni across the country and around the world. Learn how those connections remain beneficial to you after graduation.

Reserve your spot today and join us on June 6, 2023, at 10:00am ET.

The Hague Space Diplomacy Symposium
2023-06-12 08:00 - 10:00
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.
Discussions 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.
View the agenda and RSVP to attend

Art and Science: Art exhibit on bones at Needham Public Library
2023-06-12 19:30 - 20:30
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.
Registration required (free): https://needhamma.assabetinteractive.com/calendar/collaboration-of-an-artist-and-a-scientist-exploring-bones/

Ignorance Is Bliss: A Career Retrospective
2023-06-13 17:30 - 19:15 | 102 ISEC, 360 Huntington Ave, 102 ISEC, Boston, MA, 02115, United States
Dean Gregory D. Abowd will present his SIGCHI Lifetime Research Award Acceptance Lecture
Date: Tues., June 13, 2023
Time: 5:30 to 7:15 PM, reception following Dean Abowd’s talk
Place: In-Person and Livestream
BostonCHI meeting at Northeastern University in ISEC Auditorium (102 ISEC), and reception in ISEC Atrium
Registration is appreciated but not required. View BostonCHI for more information.
Presentation 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.
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Gregory 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.

Bioengineering Research Symposium
2023-06-14 09:00 - 17:00 | ISEC Auditorium, 805 Columbus Ave, Boston, MA, 02115, United States
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.
All are welcome!

Gordon Institute Virtual Information Session
2023-06-14 17:30 - 18:30, 2023-07-12 17:30 - 18:30
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.
The 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.
By 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.
Our Director of Admissions will answer your application questions for Fall 2023.
You 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.

Jiacheng Shi’s PhD Dissertation Defense
2023-06-16 14:00 - 15:00 | 232 ISEC, 360 Huntington Ave, Boston, MA, 02115, United States
Title:
Towards a Programmable, High Speed, and Robust Internet of Underwater Things
Location:
ISEC 232
Committee Members:
Prof. Tommaso Melodia (Advisor)
Prof. Stefano Basagni
Prof. Kaushik Chowdhury
Abstract:
Increasing 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.
Towards 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.
We 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.

Reconnect Workshop 2023: Risk Assessment
2023-06-18 08:00 - 2023-06-23 17:00
Where: The Omni Hotel, Providence, Rhode Island
This 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.
The application deadline is March 15, 2023, or until all slots are filled. Click here to apply.
Organizers: 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

CommLab Writing Group
2023-06-19 14:00 - 16:00 | Curry Student Center, 360 Huntington Ave., Boston, MA, 02115, United States
Join us weekly in the Curry Student Center, room 433.
Setting 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).
COE 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.

2023 AEESP Conference Hosted at Northeastern University
2023-06-20 - 2023-06-23
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.
The 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.
Unique 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.
Registration is open through June 5 for the 2023 AEESP Conference!

Alfred P. Navato's PhD Dissertation Defense
2023-06-20 08:00 - 17:00
Title:
Enabling Anomaly Detection in Complex Chemical Mixtures Through Multimodal Data Fusion
Date:
6/26/2023
Time:
10:00:00 AM
Location:
SH 210,
Committee Members:
Prof. Mueller (Advisor)
Prof. Erdogmus
Prof. Ioannidis
Prof. Onnis-Hayden
Abstract:
Recently 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.

Chang Liu's PhD Dissertation Defense
2023-06-20 13:00 - 14:00
“Unleashing the Potential of Transfer Learning for Visual Applications”
Committee Members:
Prof. Raymond Fu (Advisor)
Prof. Sarah Ostadabbas
Prof. Zhiqiang Tao
Abstract:
The 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.
In 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.

Cooper Loughlin's PhD Dissertation Defense
2023-06-23 10:00 - 11:00
“Deep Generative Models for High Dimensional Spatial and Temporal Data Analysis”
Committee Members:
Prof. Vinay Ingle (Advisor)
Dr. Dimitris Manolakis
Prof. Purnima Ratilal-Makris
Abstract:
Data 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.
Analysis 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.
We 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.
We 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.

2023 ASEE Conference- Baltimore, MA
2023-06-26 - 2023-06-28
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.

Deniz Unal's PhD Proposal Review
2023-06-26 08:30 - 09:30
Title:
Software-Defined Underwater Acoustic Networks
Committee Members:
Prof. Tommaso Melodia (Advisor)
Prof. Stefano Basagni
Prof. Kaushik Chowdhury
Dr. Emrecan Demirors
Abstract:
The 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.

Zifeng Wang's PhD Dissertation Defense
2023-06-29 17:00 - 17:30
Title: Effective and Efficient Continual Learning
Committee Members:
Prof. Jennifer Dy (Advisor)
Prof. Stratis Ioannidis
Prof. Yanzhi Wang
Abstract:
Continual 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.
In 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.
We 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.
Secondly, 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.
Finally, 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.
Comprehensive experimental results demonstrate the effectiveness and efficiency of our methods over the state-of-the-art methods on multiple CL benchmarks.

CommLab Linked In Workshop
2023-07-03 15:00 - 16:00
COE graduate students are invited to join the CommLab’s interactive LinkedIn workshop where we’ll be optimizing your profile and learning how to effectively connect with people on the platform to build a strong professional network. This workshop meets every other Monday on Zoom or in 306 Egan. Drop-in’s welcome!

Using LaTeX for CV/Resumes: A CommLab Workshop Series
2023-07-06 15:00 - 16:00
Looking to create a standout CV or resume that showcases your skills and experience in a professional way? COE graduate students are invited to join the CommLab’s LaTeX workshop! Our workshop provides support in CV/resume building using LaTeX, and will give you a better and clearer understanding of different aspects of the LaTeX code. You’ll learn how to customize templates, incorporate graphics and images, and create tables and bibliographies. Plus, you’ll have the opportunity to connect with a community of like-minded individuals who are interested in LaTeX and its potential applications. Don’t miss this chance to support, learn, and grow with The CommLab! Register to join us virtually on Zoom or in person in 306 Egan.

NSF Graduate Research Fellowship Program Writing Group
2023-07-11 15:00 - 16:00
The CommLab and the Khoury College Graduate Program are running a 7-week summer writing group for graduate students applying for the NSF Graduate Research Fellowship Program (GRFP). The virtual Zoom meetings begin Tuesday, July 11 at 3pm ET and conclude on Tuesday, August 22. Find out more and RSVP here: https://forms.gle/WLc4ZKBaGSEJLxah6

Poster Design and Editing: A CommLab Workshop Series
2023-07-17 15:00 - 17:00 | Curry Student Center, 360 Huntington Ave., Boston, MA, 02115, United States
COE Graduate students are invited to learn techniques, develop, edit and practice delivering your poster. Join the CommLab’s weekly meetings to improve your poster design and communication skills. Drop in for some or all of the time. This is a hybrid opportunity. Join us on Zoom or in the Curry Student Center. We will be in room 435 on Monday, July 17 and in room 335 for the rest of July and August.

Small Talk for Networking: A CommLab Workshop Series
2023-07-19 13:00 - 14:00
Looking to improve your conversational skills and effortlessly connect with others? This workshop series will equip you with the essential tools, techniques, and practice to connect with others, whether you are attending networking events, social gatherings, or simply looking to strike up conversations with new acquaintances. Join us for these fun-filled sessions where we’ll explore icebreakers, conversation starters, and strategies to keep the dialogue flowing naturally. Don’t miss out on this opportunity to boost your confidence and unlock new connections. This is a virtual workshop series, please register and join us Wednesdays on Zoom from 1-2pm ET.

Qing Jin's PhD Dissertation Defense
2023-07-20 13:00 - 14:00
Title:Decoupling Efficiency-Performance Optimization for Modern Neural Networks
Date:
7/20/2023
Committee Members:
Yanzhi Wang (Advisor); Prof. David Kaeli; Prof. Sunil Mittal; Prof. Jennifer Dy
Abstract:
Deep learning has achieved remarkable success in a variety of modern applications, but this success is often accompanied by inefficiency in terms of storage and inference speed, which can hinder their practical use on resource-constrained hardware. Developing highly efficient neural networks that maintain high prediction accuracy is crucial and challenging. This dissertation explores the potential for simultaneously achieving high efficiency and high prediction accuracy in neural networks, and can be broadly divided into three sections. (1) In Section One, we explore the implementation of highly efficient generative adversarial networks (GANs) capable of generating high-quality images within a predefined computational budget. The key challenge lies in identifying the optimal architecture for the generative model while simultaneously preserving the quality of the generated images from the compressed model, despite its reduced computational cost. To achieve this, we propose a novel neural architecture search (NAS) algorithm and a new knowledge distillation technique. (2) In Section Two, we explore the challenge of quantizing discriminative models without relying on high-precision multiplications. To address this issue, we present an innovative approach to determine the optimal fixed-point formats for both weights and activations based on their statistical properties. Our results demonstrate that high accuracy in quantized neural networks can be achieved without the need for high-precision multiplications. (3) In Section Three, we delve into the challenge of training neural networks for innovative computing platforms, specifically processing-in-memory (PIM) systems. Through a detailed mathematical derivation of the backward propagation algorithm, we facilitate the training of quantized models on these platforms. Additionally, through a thorough theoretical analysis of training dynamics, we ensure convergence and propose a systematic solution for quantizing neural networks on PIM systems.

Daniel Uvaydov's PhD Dissertation Defense
2023-07-21 14:00 - 15:30 | 432 ISEC, 360 Huntington Ave, Boston, MA, 02115, United States
Title: Real-Time Spectrum Sensing for Inference and Control
Committee Members:
Prof. Tommaso Melodia (Advisor)
Prof. Kaushik Choudhury
Prof. Francesco Restuccia
Abstract:
Through growing cellular innovations, the usage and congestion of the wireless spectrum is increasing at incredible speeds. High demand and limited supply pose a resource issue known as the “spectrum crunch”. With the high diversity of users sharing a large portion of the spectrum to request and receive diverse services, spectrum coordination becomes very difficult. Large scale device synchronization for spectrum coordination requires high overhead and more wireless transmissions further reducing spectrum resources. However, by monitoring the spectrum, otherwise known as spectrum sensing, we can develop mechanisms where users can opportunistically take action based on the current state of the spectrum, without need for direct coordination between devices. Spectrum sensing can enable the next generation of wireless applications ranging from opportunistic spectrum access to cognitive radio networks. The key unaddressed challenges of spectrum sensing are that (i) it requires very extensive and diverse datasets; (ii) it has to be performed with extremely low latency over varying bandwidths and must guarantee strict real-time processing constraints; (iii) 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. This dissertation focuses on addressing these challenges in multiple wireless applications by utilizing Deep Learning (DL) techniques as the main vehicle of spectrum sensing for both inference and control. Algorithmic spectrum sensing has generally been model-based which limits its performance in diverse settings and environments, for this reason we explore data-driven spectrum sensing algorithms. Mainly, this work takes a holistic approach to address spectrum sensing problems from multiple directions with the overarching goal of developing the core building blocks for the next generation of intelligent, AI-driven, efficient spectrum sharing systems. By leveraging mechanisms such as data augmentation, channel attention, voting, and segmentation we are able to push beyond the capabilities of existing DL techniques and create generalizable spectrum sensing algorithms. Furthermore we deploy different spectrum sensing solutions in real testbeds for over the air evaluations and applicable proof-of-concepts. The contributions of this work includes (i) multiple datasets and implementations for DL enabled spectrum sensing with applications in radio frequency and underwater; (ii) a method for tackling the core issue of dataset generation in supervised learning algorithms for spectrum sensing via a novel data augmentation technique; (iii) a study into one of the first ever semi-unsupervised approaches for wideband multi-class spectrum sensing.

Batool Salehihikouei Phd Proposal Review
2023-07-25 13:00 - 14:00 | 532 ISEC, 360 Huntington Ave, Boston, MA, 02115, United States
Title:
Leveraging Deep Learning on Multimodal Sensor Data for Wireless Communication: From mmWave Beamforming to Digital Twins
Committee Members:
Prof. Kaushik Chowdhury (Advisor)
Prof. Hanumant Singh
Prof. Josep Jornet
Dr. Mark Eisen
Abstract:
With the widespread Internet of Things (IoT) devices, a wide variety of sensors are now present in different environments. For example, self-driving vehicles and automated warehouses depend on sensor information for navigation and management of the robots, respectively. In this dissertation, we present a paradigm, where these sensors are re-purposed to assist network management in wireless communication, especially when classic approaches fall short to provide the required quality of service (QoS). This thesis presents data-driven and AI-based methods, where the multimodal sensor information is used for beamforming at the mmWave band, and envisions a systematic framework for joint optimization of the navigation and network management in factory floor environments. In particular, the contributions in this dissertation are as follows. First, we present deep learning fusion algorithms, where the inputs from a multitude of sensor modalities such as GPS (Global Positioning System), camera, and LiDAR (Light Detection and Ranging) are combined towards predicting the optimum beam at the mmWave band. We prove that fusing the multimodal sensor data improves the prediction accuracy compared to using single modalities. Second, we study the trade-off between the accuracy and cost of different learning strategies for multimodal beamforming. In this regard, we make a case for using federated learning for beamforming at the mmWave band and demonstrate that it is the most successful learning strategy, with respect to the communication overhead. Finally, we take measures to further optimize the computation and communication overhead, by incorporating a pruning strategy tailored to the disturbed nature of the federated learning systems. In the proposed research work, we suggest using digital twins to overcome the challenges of scarcity of data and close-world assumption in deep learning algorithms. A digital twin is a replica of a real world entity, which is typically used for studying the impact of any configuration settings in a safe, digital environment. In this dissertation, we propose using digital twins for generating training data for multimodal beamforming, in unseen scenarios. Moreover, we study a robotic industrial setting, where the path planning policy is continuously updated by monitoring the dynamics of the real world, constructing the digital twin, and updating the policy.

LinkedIn, CV, Resume: A CommLab Workshop Series
2023-07-26 17:00 - 18:00 | 206 Egan, 360 Huntington Ave, Boston, MA, 02115, United States
Join our empowering LinkedIn, CV, Resume Workshop any Wednesday from 5 pm to 6 pm ET, starting the 26th July. This collaborative space offers valuable tips and peer feedback to enhance your online profile and professional presence. Whether you’re a student or seasoned professional, our community will help you optimize your LinkedIn profile, CV, and resume to stand out in today’s job market. Don’t miss this chance to learn, grow, and build a strong network together! This is a hybrid workshop, join virtually on Zoom or in person in room 206 Egan.

Huan Wang's PhD Proposal Review
2023-08-01 10:00 - 11:30
Title:
Towards Efficient Deep Learning in Computer Vision via Sparsity and Distillation
Committee Members:
Prof. Yun Fu (Advisor)
Prof. Octavia Camps
Prof. Zhiqiang Tao
Abstract:
AI, empowered by deep learning, has been profoundly transforming the world. However, the excessive size of these models remains a central obstacle that limits their broader utility. Modern neural networks commonly consist of millions of parameters, with foundation models extending to billions. The rapid expansion in model size introduces many challenges including training cost, sluggish inference speed, excessive energy consumption, and negative environmental implications such as increased CO2 emissions.
Addressing these challenges necessitates the adoption of efficient deep learning. This thesis focuses on two overarching approaches, network sparsity and knowledge distillation, to enhance the efficiency of deep learning models in the context of computer vision. Network sparsity focuses on eliminating redundant parameters in a model while preserving the performance. Knowledge distillation aims to enhance the performance of the target model, referred to as the “student,” by leveraging guidance from a stronger model, known as the “teacher”. This approach leads to performance improvements in the target model without reducing its size. In the proposal, I will start with the background and major challenges of leveraging these techniques towards efficient deep learning. Then, I shall present the potential solutions in various tasks (e.g., image classification, image super-resolution, neural rendering, and text-to-image generation), with preliminary results to justify the efficacy of the proposed approaches. Finally, a comprehensive outlook of the future work will conclude this proposal.

Gordon Institute Virtual Information Session
2023-08-02 17:30 - 18:30
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.
The 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.
By 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.
Our Director of Admissions will answer your application questions for Fall 2023.
You 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.
