Northeastern University College of Engineering

GSE Wonder Week Series: Learn more about Bioengineering Graduate Programs
2023-04-26 09:00 - 09:45 (America/New_York) | MA
Join us to learn more about the Graduate Programs offered by the Bioengineering Department.
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GSE Wonder Week Series: Learn more about WiOT and Wireless & Network Engineering
2023-04-26 10:00 - 10:45 (America/New_York) | MA
Join us to learn more about WiOT and Wireless & Network Engineering.
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CareerEco Engineering Grad School Virtual Fair
2023-04-26 11:00 - 15:00 (America/New_York) | MA
Join us virtually to learn about Northeastern’s graduate Engineering programs. Admissions representatives will be available to answer your questions and provide application fee waivers! Register at the link below.

Rui Huang's MS Thesis Defense
2023-04-26 16:00 - 17:00 (America/New_York) | MA
“Sputter Deposition and Characterization of Highly Textured BixTe1-x Thin Films”
Committee Members:
Prof. Nian-Xiang Sun (Advisor)
Prof. Marvin Onabajo
Prof. Yongmin Liu
Abstract:
The discovery of topological insulators (TIs) provides a direction for scientists to understand the Quantum Spin Hall Effect (QSHE) and Spin-Orbit Coupling (SOP) in condensed matter physics. After a decade, people found that after the introduction of magnetism into TI, the Time Reversal Symmetry (TSR) is broken, producing Magnetic Topological Insulators (MTI). Meanwhile, with the deposition of TI on the Magnetic Insulator (MI), the Spin-Orbit Torque was found in TI/MI structures. Introducing dopants into TI is another method to produce MTI. Mn-doped, Cr-doped, and Ni-doped TI thin films have been explored recently. Thus, the 3D TI, Bi2Te3, and MTI, Ni: Bi2Te3, thin film-based materials have been applied to some energy-efficient spintronic devices. However, according to the Bi-Te phase diagram, Bi2Te3 is one of the Bi-Te family. The narrow range of the Bi2Te3 phase is a challenge for people to deposit the correct phase on the InP (111) wafer due to the potential effect of defects. In this Master thesis, the textured BixTe1-x and Ni-doped BixTe1-x thin films are deposited on the InP (111) substrate through the RF Magnon Sputtering Tool with a Te capping layer under different deposition powers, temperatures, and post-annealing time. After the X-ray diffraction measurement on three samples with various conditions, the textured Bi8Te7, Bi8Te9, and Ni: Bi8Te7 thin films are concluded based on the comparison between the theoretical XRD results with the experimental ones.

The Student World Postgraduate Virtual Fair - Latin America
2023-04-26 16:00 - 20:00 (America/New_York) | MA
Chat with a COE Admissions Representative to learn more about our graduate engineering programs. This event is free to register. An application fee waiver code will be available for attendees who connect with our rep!

GSE Wonder Week Series: Learn more about Civil & Environmental Engineering Graduate Programs
2023-04-27 08:00 - 08:45 (America/New_York) | MA
Join us to learn more about the Graduate Programs offered by our department of Civil & Environmental Engineering Department.
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GSE Wonder Week Series: Learn More about MS in Telecommunication Networks and MS in Cyber-Physical Systems
2023-04-27 09:00 - 09:45 (America/New_York) | MA
Join us to learn more about the MS in Telecommunication Networks and the MS in Cyber-Physical Systems.
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The Student World Postgrad Virtual Fair - Europe
2023-04-27 10:00 - 14:00 (America/New_York) | MA
Chat with a COE Admissions Representative to learn more about our graduate engineering programs. Register and attend for a chance to win a scholarship through The Student World! An application fee waiver code will be available for attendees who connect with our rep!

GSE Wonder Week Series: Learn more about Chemical Engineering Graduate Programs
2023-04-28 08:30 - 09:30 (America/New_York) | MA
Join us to learn more about the Graduate Programs offered by the Department of Chemical Engineering.
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GSE Wonder Wonder Week Series: Learn More About Graduate Co-op for Multidisciplinary Engineering Programs
2023-04-28 10:00 - 10:45 (America/New_York) | MA
Join us to learn more about co-op experiences available to those who enroll in our Graduate Multidisciplinary Engineering Programs!
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GSE Wonder Week Series: Learn More About Our Graduate Multidisciplinary Engineering Programs
2023-04-28 11:00 - 11:45 (America/New_York) | MA
Join us to learn more about our Graduate Multidisciplinary Engineering Programs including: Data Architecture &n Management, Information Systems, Information Systems – Bridge, and Software Engineering Systems.
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GSE Wonder Week Series: Learn More About Graduate Co-operative Education - Disciplinary Engineering Programs
2023-04-28 12:00 - 12:45 (America/New_York) | MA
Join us to learn more about co-op opportunities available in our Graduate Disciplinary Engineering Programs.
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Balaji Sundareshan's MS Thesis Defense
2023-04-28 13:00 - 15:00 (America/New_York) | MA
“Cross-View Action Recognition using Transformers”
Committee Members:
1. Prof. Octavia Camps (Advisor)
2. Prof. Mario Sznaier
3. Prof. Huaizu Jiang
Abstract:
Cross-view action recognition (CVAR) seeks to recognize a human action when observed from a previously unseen viewpoint. This is a challenging problem since the appearance of action changes significantly with the viewpoint. Applications of CVAR include surveillance and monitoring of assisted living facilities where is not practical or feasible to collect large amounts of training data when adding a new camera. In this thesis, we propose a method to perform cross-view action recognition from 2D skeleton data using Transformers. First, we understand the interpretability of the basline network and its submodules by visualizing the saliency map. Next, we integrate Transformers at different parts of the network for both single-clip and multi-clip and understand the impact on the performance. In the end, we also discuss the necessity of pretraining sub-modules in the network and their impact on the performance.

Co-op Student Spotlight: Data Architecture and Management and Software Engineering Systems
2023-05-02 10:00 - 11:00 (America/New_York) | MA
The College of Engineering is excited to host a Co-op Student Spotlight: Data Architecture and Management and Software Engineering Systems event on May 2, 2023, at 10:00am ET. We’d love to see you there.
All qualified Graduate School of Engineering students are encouraged to participate in Northeastern’s cooperative education program. Students who complete co-ops can work in the industry for 4, 6, or 8 months during the academic program. During this webinar, participants will learn more about the co-op program from co-op faculty with a spotlight on Software Engineering Systems and Data Architecture and Management students.
Topics this event will cover include:

Participating in Co-op: Each student is supported by co-op faculty who help to make the process easy for students while they participate.
Co-op Partners and Opportunities: The College of Engineering has over 1000 co-op industry partners across the United States and around the globe.
Current Student Highlights: Current students will share their current experiences including their co-op journey, their specific opportunity, and the support they received on co-op.

Reserve your spot today and join us on May 2, 2023, at 10:00am ET.

Intellectual Property Awareness Summit (IPAS)
2023-05-02 10:00 - 18:00 (America/New_York) | MA
The sixth annual Intellectual Property Awareness Summit® (IPAS 2023) will be held on May 2nd in Boston, in conjunction with Northeastern University’s Center for Research Innovation (CRI).
IPAS is a gathering of IP owners, creators, educators, lawyers, organizations and investors, all with a common goal – to explore ways to make the benefits of IP rights, and the issues surrounding them, more apparent to people and society.
​In Person Registration
Virtual Registration

Building Habits for Professional Success: A CommLab Workshop Series
2023-05-04 16:00 - 17:00 (America/New_York) | 206 Egan, 360 Huntington Ave, Boston, MA, 02115, United States
Join the CommLab for our Building Habits for Professional Success, where we’ll focus on building positive habits. Developing good habits can be a powerful tool for achieving success in various areas of life, whether it’s improving your career, increasing productivity, or enhancing your wellbeing. Don’t miss this opportunity to gain valuable insights and practical tools for creating lasting positive changes in your daily routine. Join us every other week for an engaging and informative workshop!

ODSC East Data Science and AI Conference
2023-05-09 - 2023-05-11 | MA
Join COE Graduate Admissions representatives at the ODSC East Data Science and AI Conference. Learn about our graduate Engineering programs and receive an application fee waiver code for attending and asking one of our reps a question.

CommLab Linked In Workshop Series
2023-05-09 16:00 - 17:00 (America/New_York) | 206 Egan, 360 Huntington Ave, Boston, MA, 02115, United States
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 Tuesday on Zoom or in 206 Egan. Drop-in’s welcome!

Using LaTeX for CV/Resumes: A CommLab Workshop Series
2023-05-11 16:00 - 17:00 (America/New_York) | 206 Egan, 360 Huntington Ave, Boston, MA, 02115, United States
Looking to create a standout CV or resume that showcases your skills and experience in a professional way? 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 206 Egan. This is a reoccurring community of practice and will meet every other Thursday.

Research Spotlight: Chemical Engineering
2023-05-16 10:00 - 11:00 (America/New_York) | MA
The College of Engineering is excited to host a Research Spotlight: Chemical Engineering event on May 16, 2023, at 10:00am ET. We’d love to see you there.
As a Research 1 university, Northeastern University students are exposed to some of the highest-quality research offerings in the United States. In this webinar, our Chemical Engineering faculty members will walk you through their current research projects and offer insight on how current students may have the opportunity to be involved in those research projects during their time in the program.
Topics this event will cover include:

Faculty Introduction: Faculty will have the opportunity to introduce themselves and their role with graduate students.
Faculty Projects: Faculty will introduce their projects and outline the works of their labs with specific attention to their research thrusts.
How to Participate: Attendees will learn how they can be involved in research at the academic and co-curricular levels.

Reserve your spot today and join us on May 16, 2023, at 10:00am ET.

Data Visualization by Coding: A CommLab Workshop Series
2023-05-16 16:00 - 17:00 (America/New_York) | 206 Egan, 360 Huntington Ave, Boston, MA, 02115, United States
Join the CommLab to work on visualizing your data using different coding methods. We will use Python as well as other methods. Join us to tell us which methods you are interested in working on.

Northeastern in Seattle: Build Your Tech Talent
2023-05-17 17:00 - 19:00 (America/Los_Angeles) | MA
Whether you’re entirely new to computer science or have dabbled in coding, you can advance your career with Northeastern University in Seattle’s monthly Build Your Tech Talent workshops. These free events give anyone looking to develop coding skills the chance to learn from industry experts.
Our upcoming May workshop includes:
Thematic Analysis of Song Lyrics with Shanu Sushmita (on campus)

Date: Wednesday, May 17, 2023
Time: 5:00 p.m. – 7:00 p.m. PT

Register

Gordon Institute Virtual Information Session
2023-05-17 17:30 - 18:30 (America/New_York) | MA
Learn how you can earn a Graduate Certificate in Engineering Leadership as a stand-alone certificate or in combination with one of twenty 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.

Designing a Research Poster: A CommLab Series
2023-05-18 16:00 - 17:00 (America/New_York) | 335 CSC, 360 Huntington Ave, CSC, Boston, MA, 02115, United States
Join the CommLab to work on your research poster. Whether you are designing, editing or delivering a poster, this series will help you through whichever stage you are in. May 18th will focus on design, May 25 and June 1 will focus on editing and June 8 will focus on presenting. This series will be hybrid. Join us in 335 Curry Student Center or on Zoom.

Mengting Yan's PhD Dissertation Defense
2023-05-22 10:30 - 11:30 (America/New_York) | MA
“Integrated Circuit Design Methods for Temperature-based Hardware Trojan Detection and Parametric Frequency Division in Next-Generation Systems-on-a-Chip”
Committee Members:
Prof. Marvin Onabajo (Advisor)
Prof. Yong-bin Kim
Prof. Yunsi Fei
Abstract:
New needs for next-generation systems-on-a-chip (SoC) are emerging as the trend of globalization in the semiconductor industry becomes increasingly ubiquitous and the demand for low-power Internet-of-Things (IoT) devices continues to soar. Among various research directions, this dissertation focuses on enhancing hardware security and on providing low-noise frequency sources for next-generation SoCs. Within this scope, the described research addresses the challenge to improve on-chip anomaly detection capabilities, and separately lays a foundation for the design of circuits to reduce the phase noise of on-chip oscillators.
In the first part of this dissertation, an on-chip temperature-based Hardware Trojan (HT) detection system is introduced. The approach to detect inserted HTs relies on thermal profiling of the circuit under test (CUT) and side-channel analysis of the obtained temperature data. On-chip electrothermal coupling is modeled as part of a simulation technique that associates local thermal activities with circuit-level power consumption using a standard electrical simulator. To monitor the thermal profiles on chips with high sensitivity to local temperature changes as well as to enhance the resilience to flicker noise, a fully-differential temperature sensor equipped with a chopping mechanism has been designed in 130-nm complementary metal-oxide-semiconductor (CMOS) technology, which has a sensitivity of 840 V/◦C. The simulated temperature sensor output in the presence of noise and process variations is quantized by an analog-to-digital converter (ADC) model and processed using principal component analysis (PCA), which allows to determine the minimum detectable Trojan power and the design requirements for the on-chip ADC. With a modeled 8-bit ADC, simulations of the HT detection system reveal a detection rate of 100% with a Trojan power down to 2.4 μW within the thermal profile of a CUT consuming 508 μW. A prototype 8-bit 1 MS/s successive approximation register (SAR) ADC for such a system was designed in 130-nm CMOS technology, fabricated, and tested. The measured effective number of bits (ENOB) is 7.27 bits up to the Nyquist frequency, with a power consumption of 103.2 μW from a 1.2 V supply. Furthermore, a 3-step analog calibration loop has been designed to compensate for the voltage offsets within the sensor circuits in the presence of device mismatches and process-temperature variations. The calibration loop settles within 300 μs to complete the offset calibration, such that the input-referred offset has a standard deviation of 5.86 μV based on Monte Carlo simulations.
In the second part of this dissertation, the on-chip realization of a parametric frequency divider (PFD) is explained. The low-power 2:1 frequency division at sub-6 GHz plays a critical role in on-chip phase noise reduction systems that exhibit nonlinear operations, indicating promise for future integration into radio frequency (RF) SoCs. In particular, the first current-driven PFD with an output frequency of 2.4 GHz is introduced, which consists of three major blocks: (1) a custom PFD driver stage with a buffer to ease input driving, (2) a purely passive PFD core with inductor-capacitor (LC) resonators, and (3) an output driving stage with embedded band-pass filtering that suppresses undesirable output harmonics. A prototype PFD chip was fabricated in standard 65-nm CMOS technology, and the corresponding measurement results are presented to characterize the performance of the new PFD. The minimum required supply voltage for the PFD driver is 1.4 V with an input frequency of 4.8 GHz, whereas the PFD has an operating frequency range from 4.5 GHz to 5.1 GHz with a supply voltage of 1.5 V. To the best of the author’s knowledge, the proposed PFD is the first on-chip CMOS implementation for sub-6 GHz applications, which balances the trade-offs among frequency range, power consumption, and chip area constraints.

Yuezhou Liu's PhD Proposal Review
2023-05-26 09:00 - 10:00 (America/New_York) | MA
Committee Members:
Prof. Edmund Yeh (Advisor)
Prof. Stratis Ioannidis
Prof. Lili Su
Prof. Carlee Joe-Wong
Abstract:
Significant advances in edge and mobile computing capabilities enable machine learning to occur at geographically diverse locations in networks, e.g., cloud, edge, and mobile devices. The training data needed in those learning tasks may not be fully generated locally. Moreover, some promising distributed learning paradigms enable devices to collaboratively train a model, which requires communication among the devices for exchanging necessary information. Thus, optimizing network strategies for the transmission/exchange of ML/AI ingredients (e.g., input data, model parameters, gradients) is important for facilitating efficient in-network distributed ML. While there exist many works that use ML to optimize network operation strategies, few works study optimized networks that boost ML performance. This dissertation tries to fill the gap by studying several network optimization problems for distributed ML. Different from classic network optimization problems for data delivery or edge computing that optimize energy consumption, delay, throughput, etc., we also pay attention to ML-related metrics such as model accuracy and training convergence time.
We first propose an experimental design network paradigm, wherein learner nodes train possibly different ML models via consuming data streams generated by data source nodes over a network. We formulate this problem as a social welfare optimization problem in which the global objective is defined as the sum of experimental design objectives of individual learners, and the decision variables are the data transmission strategies subject to network constraints. We show that, assuming Bayesian linear regression models and Poisson data streams in steady state, the global objective is continuous DR-submodular, which enables the design of efficient approximate algorithms with approximation guarantees. We will further extend our framework to incorporate more practical ML applications, such as ML with arbitrary nonlinear models.
The second half of this dissertation studies network optimization for Federated learning (FL), a distributed paradigm for collaboratively learning models without having clients disclose their private data. We propose to use caching for improving FL efficiency concerning the total model training time for convergence. Instead of having all clients download the latest global model from a parameter server, we select a subset of clients to access, with smaller delays, a somewhat stale global model stored in caches. We propose CacheFL — a cache-enabled variant of FedAvg, and provide theoretical convergence guarantees in the general setting where the local data is imbalanced and heterogeneous. With this result, we determine the caching strategies that minimize total wall-clock training time at a given convergence threshold for both stochastic and deterministic communication/computation delays.

Guillem Reus Muns' PhD Dissertation Defense
2023-05-26 12:30 - 13:30 (America/New_York) | Admissions Visitor Center (West Village F)
“AI for communication and sensing in RF environments”
Committee Members:
Prof. Kaushik Chowdhury (Advisor)
Prof. Stratis Ioannidis
Prof. Hanumant Singh
Abstract:
The recent growth of Internet of Things (IoT), as well as other new
revolutionary applications utilizing wireless spectrum are leading the way towards the realization of next-generation wireless systems that jointly utilize communications and sensing. However, such systems offer many degrees of freedom, and optimizing them for a specific task is difficult to accomplish with deterministic and classical approaches. For this reason, data-driven and AI-based methods have been pursued actively by the research community, as they are able to find solutions that often come close to or exceed the performance of the deterministic counterparts with fractional design complexity. This thesis presents, through real systems and with experimental validation, our progressive efforts in four broad areas, where AI enables the operation of aerial and terrestrial systems that combine sensing and communications. The following key use cases with distinct contributions are investigated:
i) Sensing-aided communications for air and ground systems. First, we present a UAV communication method that defines constellation points in space that map to transmitter frequency bands and are detected at the Base Station using millimeter wave sensors. Second, we explore alternative vehicle-to-infrastructure mmWave beamforming methods, leveraging a) vehicle position and velocity estimation using in-band standard compliant 802.11ad radar and b) camera images and GPS location information.
ii) Signal classification using communication signals, where we propose a) a UAV classification method using uniquely UAV-transmitted signals and b) an RF fingerprinting technique that improves class separation by combining triplet loss with regular classification techniques.
iii) ‘SenseORAN’, a revolutionary architectural design that aims to reuse the cellular infrastructure for sensing purposes in order to address spectrum access challenges in the CBRS band. This is enabled by Open Radio Access Network (O-RAN), a cellular architecture concept that promotes virtualized RANs where disaggregated components are connected via open interfaces and supports intelligent controllers running custom logic. iv) ‘AirFC’, an over-the-air computation method that implements fully connected neural networks inference leveraging multi-antenna wireless systems.

SASE Taiyaki and Trivia
2023-05-31 15:00 - 17:00 (America/New_York) | MA
The Society of Asian Scientists and Engineers (SASE) is hosting “Taiyaki and Trivia” to celebrate AAPI Heritage Month.
Join us in Snell Quad, catered by Taiyaki NYC.

Cheng Gongye's PhD Proposal Review
2023-06-02 11:00 - 12:00 (America/New_York) | MA
“Hardware Security Vulnerabilities in Deep Neural Networks and Mitigations”
Committee Members:
Prof. Yunsi Fei (Advisor)
Prof. Xue Lin
Prof. Xiaolin Xu
Abstract:
Over 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.
This 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.
For 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.
For 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.
Regarding 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.

MIE Seminar - Dr. Andrew Akbashev
2023-06-02 11:00 - 12:00 (America/New_York) | 168 SN, 360 Huntington Ave, Boston, MA, 02115, United States
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”.
He 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.
Please 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.
