Northeastern University College of Engineering

EducationUSA South Asia Graduate Fair
2022-09-03 18:00 - 21:00 (Asia/Kolkata)
Northeastern University’s Graduate School of Engineering will be attending the EducationUSA Virtual South Asia Graduate Fair on Saturday, September 3rd from 6:00 PM – 9:00 PM IST. Learn more about Northeastern’s Graduate Engineering programs!

QS Graduate Fair - Sao Paulo, Brazil
2022-09-03 15:00 - 17:30 (America/Sao_Paulo)
Join the Graduate Admissions team for an IN-PERSON recruiting event in Sao Paulo, Brazil! The event will take place at Hotel Transamerica from 3:00 PM to 5:30 PM BRT. Registration and event details may be found at the website below. We look forward to seeing you there!

QS Virtual Masters Event -Philippines
2022-08-31 18:00 - 21:30 (Asia/Manila)
An admissions representative, Emily DeRosa, Recruiting Specialist, will be present at the QS Virtual Masters Event – the Philippines taking place on Monday, August 31st from 6:00 PM – 9:00 PM PHT (6:00 AM – 9:30 AM EST). Learn more about the programs offered at Northeastern University’s Graduate School of Engineering.

Cornell 2022 Graduate and Professional School Fair
2022-08-29 08:00 - 17:00 (America/New_York)
Join the Graduate Admissions team for this VIRTUAL recruiting event! The event will take place from 1:00 PM to 4:00 PM EST. Registration and event details may be found at the website below. We look forward to seeing you there!

India Study Expo
2022-08-27 - 2022-08-28
Northeastern University will be visiting Hyderabad and Mumbai on August 27 and 28 to host in-person events. This is the first in-person event in 3 years. We look forward to meeting you then.

Library Webinar: Intro to R/RStudio (Virtual)
2022-08-26 12:00 - 13:30 (America/New_York)
Want to learn R and RStudio but don’t know where to start?
We’ll walk you through the steps of creating your first graph, beginning with opening RStudio for the first time and ending with tips on how to continue learning in order to pursue your own projects.
No prior experience with R, RStudio, or computer programming is required.
Installation of R and RStudio beforehand is required.

To install R, download and run this R installer for Windows or this R installer for macOS. If you run into any problems, please read further instructions on R’s website.
To install RStudio, download and run this RStudio installer for Windows or this RStudio installer for macOS. If you run into any problems, please read further instructions on RStudio’s website.

This workshop is presented by Kate Kryder, Data Analysis and Visualization Specialist at Northeastern University Library.
Please register at the library events calendar.
Please note: This session will not be recorded. If you’re interested in learning the material but don’t plan to attend, please don’t register for the session as seats are limited.

Library Bite-Sized Webinar: How do I (responsibly) edit Wikipedia?
2022-08-26 12:00 - 12:30 (America/New_York)
At some point in your education, you’ve probably heard that you should never use Wikipedia “because anyone can edit it.” But have you ever actually tried editing it yourself? This webinar will provide an introduction to Wikipedia’s editing tools and its dedicated community of editors, who voluntarily cultivate and maintain the free encyclopedia according to a set of five “pillars of Wikipedia.” Learn how and why you can—and should!—contribute to the world’s largest reference work.
This webinar is pre-recorded. To receive a captioned copy of the webinar to watch at your convenience, please register at the library events calendar and it will be sent to you at the time noted above.

Library Bite-Sized Webinar: How can I tidy my spreadsheet data?
2022-08-26 12:00 - 12:30 (America/New_York)
Everyone uses spreadsheets, but not everyone sets them up in the same way. This short video will share some good practices to keep spreadsheets tidy by using standard, predictable patterns to record data. These tips can help you avoid getting bogged down in the logistics of entering your data, and instead focus your time and energy on analyzing it.
This webinar is pre-recorded. To receive a captioned copy of the webinar to watch at your convenience, please register at the library events calendar and it will be sent to you at the time noted above (EST).

Library Bite-Sized Webinar: How can I navigate journal publication fees?
2022-08-26 12:00 - 12:30 (America/New_York)
What can you do when a journal in which you wish to publish charges a fee? In this session you will learn what article processing charges (APC) are and how to navigate APC, including: where you can look for funds to pay for APC, how the Northeastern University Library is helping to lower and eliminate APC, and tools for searching for open access journals which do not require APC.
This webinar is pre-recorded. To receive a captioned copy of the webinar to watch at your convenience, please register at the library events calendar and it will be sent to you at time noted above (EST).

Library Bite-Sized Webinar: How do I add a Creative Commons license to my work?
2022-08-25 15:00 - 15:30 (America/New_York)
You’ve probably heard about copyright, but are you familiar with Creative Commons (CC)? CC licenses allow you to retain the copyright to your work, even while granting advanced permissions to others who may want to use or build upon it.
In this session, you’ll learn the basics of the 6 Creative Commons licenses and how to actually apply a license to your work. We will also outline some considerations and questions to help you decide whether a CC license is right for you – and which one. Presented by Arts, Humanities, and Experiential Learning Librarian, Regina Pagani.
Registration is required. Please register at the library events calendar.
This live webinar will be held in EST. The webinar will be recorded, captioned and sent out to registrants. To receive a copy, please register at the library events calendar.

Tarik Kelestemur's PhD Dissertation Defense
2022-08-25 14:00 - 15:00 (America/New_York)
Location: ISEC 532
“Combining Classical and Learning-based Methods for Visual and Tactile Manipulation”
Abstract:
Robots that operate in dynamic and ever-changing environments need to make sense of their surroundings and act in them safely and efficiently. This requires the integration of multiple sensory modalities such as visual and tactile. Humans can naturally fuse different feedbacks from the environment or substitute them with one another to perform everyday tasks. For example, to use a computer mouse, we first locate the mouse using vision and then use touch feedback from our fingers to precisely localize the buttons. Ideally, we would like robots to have human-level perception and control of the environment to achieve various tasks. This dissertation address two significant problems toward this overarching goal.
The first problem we consider in this dissertation is figuring out how to use tactile information in conjunction with visual feedback. Robotic manipulators that interact with objects and environments are often equipped with visual sensors such as RGB and depth cameras. They estimate the state of their environment using these sensors and act upon the estimated state. While a large body of previous work has shown that we can achieve impressive results only with visual sensors, more precise and delicate tasks require touch information which gives direct feedback from the environment. To this end, we propose methods for efficiently combining the tactile and visual information to leverage the advantages of these modalities.
The second problem we investigate is how to build visual and tactile manipulation methods that can generalize over the different novel environments and objects. The rise of deep learning has enabled robots to solve challenging perception and control problems using visual and tactile observations while generalizing to novel objects and environments. However, a common issue among deep learning-based methods is that these methods usually work only within the distribution of the training data and do not perform well when they are presented with unseen examples. Furthermore, they cannot distinguish whether they are dealing with in or out-of-distribution data. We propose to address this issue by combining well-established and principled algorithmic priors with the generalization capabilities of deep learning.
In the first part of this dissertation, we investigate the problem of pose estimation of the robotic grippers with respect to the environment and objects. The proposed framework introduces a learnable Bayes filter that can estimate the position of a gripper in a single image of the environment. We learn the observation and motion models of the Bayes filter using modern neural network architectures and use recursive belief updates for tracking the position of the gripper over time. Later, the belief estimation is used as an input to policies where the aim is to solve manipulation tasks using tactile feedback. In the second part, we look at the problem of estimating shapes from partial observations. We propose a method called DeepGPIS that combines a powerful deep learning-based implicit shape representation with a non-parametric inference approach model for implicit surfaces (GPIS) which allows us to generate complete shapes of novel objects and estimate their predictive uncertainties.
Committee:
Prof. Taskin Padir (Advisor)
Prof. Robert Platt (Advisor)
Prof. David Rosen (Advisor)

2022 EducationUSA Virtual Fair
2022-08-24 18:00 - 21:00 (America/New_York)
Join the Graduate School of Engineering Admissions team at the 2022 EducationUSA Virtual Fair to learn about studying at the graduate level in the U.S. and our graduate Engineering programs!
Register for the event may be found through the website below.

Library Bite-Sized Webinar: How can I use citation tracking to find relevant research?
2022-08-24 15:00 - 15:30 (America/New_York)
This webinar is an introduction to ways of extending your research discovery using “cited by” and “highly cited” references found in Google Scholar, Web of Science, and Scholar OneSearch. Supercharge your results with forward and backward citation tools, analyze the data in your results, and learn the strengths and weaknesses of each one of these resources for tracking scholarly conversations through time. Presented by Head of Arts, Social Sciences, and Humanities at Northeastern University Library, Karen Merguerian.
Registration is required. Please register at the library events calendar.
This live webinar will be held in EST. The webinar will be recorded, captioned and sent out to registrants. To receive a copy, please register using this link.

Library Bite-Sized Webinar: How do I draw in PowerPoint?
2022-08-23 15:00 - 15:30 (America/New_York)
Need to annotate a figure or create a basic visual such as a Venn diagram? Have no time to learn a new, artsy drawing tool? We’ll share tips for using PowerPoint’s Shape tools to get the job done! This webinar will be presented by Data Analysis and Visualization Specialist, Kate Kryder.
Registration is required. Please register at the library events calendar.
This live webinar will be held in EST. The webinar will be recorded, captioned and sent out to registrants. To receive a copy, please register using this link.

Hamed Mohebbi Kalkhoran's PhD Dissertation Defense
2022-08-22 15:00 - 16:00 (America/New_York)
“Machine learning approaches for classification of myriad underwater acoustic events over continental-shelf scale regions with passive ocean acoustic waveguide remote sensing”
Abstract:
Underwater acoustic data contain a myriad of sound sources. Among underwater acoustic events, marine mammal vocalization classification is one of the most challenging problems due to their transient broadband calls, high variation in the calls of a specie, and high similarity between the calls of some species. Here, we developed machine learning approaches for classifying marine mammal vocalizations for real-time applications. We utilize acoustic data from a 160-element coherent hydrophone array and employ the passive ocean acoustic waveguide remote sensing technique to enable sensing and detections over instantaneous wide areas more than 100 km in diameter from the array. A variety of computational accelerating approaches, combining hardware and software, that make the methods desirable for real-time applications are also developed.
The humpback whale vocalizations can be divided into two classes, song and non-song calls. Here we use wavelet signal denoising and coherent array processing to enhance the signal-to-noise ratio. To build features vector for every time sequence of the beamformed signals, we employ Bag of Words approach to time-frequency features. Finally, we apply Support Vector Machine (SVM), Neural Networks, and Naive Bayes to classify the acoustic data and compare their performances. Best results are obtained using Mel Frequency Cepstrum Coefficient (MFCC) features and SVM which leads to 94% accuracy and 72.73% F1-score for humpback whale song versus non-song vocalization classification.
To classify a large variety of whale species vocalizations, we extracted time-frequency features from Power Spectrogram Density (PSD) of the beamformed signals. Then we used these features to train three classifiers, which are SVM, Neural Networks, and Random forest to classify six whale species: Fin, Sei, Blue, Minke, Humpback, and general Odontocetes. We also trained a set of Convolutional Neural Networks (CNN) to detect and classify each of these six whale vocalization categories directly using Per-Channel Energy Normalization (PCEN) spectrograms. Best results were obtained with Random forest classifier, which achieved 95% accuracy, and 85% F1 score. To detect transient sound sources, first we applied PCEN on the PSD of the beamformed signals. We applied thresholding on the PCEN data followed by morphological image opening to find potential sound sources and reduce noisy detections. Then we applied connected component analysis to obtain the final detected sounds for each bearing. To estimate the Direction of Arrival (DoA) of detected sounds, we applied non-maximum suppression (NMS), which is widely used in object detection applications in computer vision, on the detected sounds. We used mean power of each detected sound as the scores for NMS. To speed up the data processing, we investigated a variety of accelerating approaches, such as analyzing the effect of floating point precision, applying parallel processing, and implementing fast algorithms to run on GPU. During an experiment in the U.S. Northeast coast on board the US research vessel RV Endeavor in September 2021, we utilized the software and hardware advances developed here to record underwater acoustic data using Northeastern University in-house fabricated large aperture 160- element coherent hydrophone array with sampling frequency of 100 kHz per element.
Committee:
Prof. Purnima Ratilal (Advisor)
Prof. Themistoklis Sapsis
Prof. Devesh Tiwari

Mengshu Sun's PhD Dissertation Defense
2022-08-17 12:00 - 13:00 (America/New_York)
“Deep Learning Acceleration on Edge Devices with Algorithm/Hardware Co-Design”
Abstract:
As deep learning has succeeded in a broad range of applications in recent years, there is an increasing trend towards deploying deep neural networks (DNNs) on edge devices such as FPGAs and mobile phones. However, there exists a significant gap between the extraordinary accuracy of state-of-the-art DNNs and efficient implementations on edge devices, due to their limited resources for DNNs with high computation and memory intensity. With the target of simultaneously accelerating the inference and maintaining the accuracy of DNNs, efficient implementations are investigated of deep learning on low-power and resource-constrained devices, by presenting algorithm/hardware co-design frameworks that incorporate hardware-friendly DNN compression algorithms with hardware design optimizations.
First, the DNN compression algorithms are explored, leveraging quantization and weight pruning techniques. As for quantization, intra-layer mixed precision/scheme weight quantization is proposed to boost utilization of heterogeneous FPGA resources and therefore improving the FPGA throughput, by assigning multiple precisions and/or multiple schemes at the filter level within each layer and maintaining the same ratio of filters across all the layers for each type of quantization assignment. As for weight pruning, novel structured and fined-grained sparsity schemes are proposed and obtained with the reweighted regularization pruning algorithm, and then incorporated into acceleration frameworks on FPGAs to make the acceleration rate of sparse models approach the pruning rate of the number of operations.
Second, the hardware implementations are studied, proposing an automatic DNN acceleration framework to generate DNN accelerators to satisfy a target frame rate (FPS). Unlike previous approaches that start from model compression and then optimizing the FPS for hardware implementations, this automatic framework will provide an estimation of the FPS with the FPGA resource utilization analysis and performance analysis modules, and the bit-width is reduced until the target FPS is met and the mixing ratio for quantization precisions/schemes is automatically determined to guide the quantization process and the accelerator implementation on hardware. A resource utilization model is developed to overcome the difficulty in estimating the LUT consumption, and a novel computing engine for DNNs is designed with various optimization techniques in support of DNN compression to improve the computation parallelism and resource utilization efficiency.
Committee:
Prof. Xue Lin (Advisor)
Prof. Miriam Leeser
Prof. Xiaolin Xu

Mithun Diddi's PhD Dissertation Defense
2022-08-17 11:00 - 12:00 (America/New_York) | 432 ISEC, 360 Huntington Ave, Boston, MA, 02115, United States
“Multiple UAVs for Synchronous – Shared Tasks and Long-term Autonomy”
Abstract:
This thesis focuses on the use of multiple unmanned aerial vehicles(UAVs) in a distributed framework from a systems perspective to synchronously perform shared tasks such as aerial beamforming and coordinated mapping and to enhance the reliability of performing periodic (mapping) tasks at remote locations for long-term autonomous(LTA) missions. We present an autonomy stack for multiple, heterogeneous UAVs with a simulation framework. We implemented the end-to-end pipelines for perception and communication applications involving multiple UAVs.
Repeated deployments in harsh-weather, real-world locations are challenging and are limited by the need for human operators. These infrastructure-poor, remote locations pose unique challenges to long-term autonomous missions. In these locations, harvesting power onboard using solar panels may be a viable alternative for recharging batteries.
In the first part of the thesis, we focus on hardware architecture for UAVs to enable reliable LTA missions with minimal human intervention. We developed a Size, Weight, and Power(SWaP) constrained Smart charging stack to minimize hotel loads seen during the recharging process and enable efficient charging of batteries. This leads to the design of a standalone, solar rechargeable quadcopter.
Real-world applications such as reconstructing a dynamic scene from multiple viewpoints and distributed aerial beamforming require multiple robots(agents) to coordinate and synchronously act to accomplish shared tasks. These tasks require spatially distant, multiple UAVs to have time, phase, and frequency synchronization. We demonstrate a Synchronous UAV(S-UAV) architecture for wireless synchronization based on GPS disciplined oscillators and the associated software framework needed for temporal registration of data across multiple UAVs.
We have built four S-UAVs and demonstrate the ability to 3D reconstruct a dynamic scene from overlapping viewpoints. Dynamic baseline camera arrays formed using multiple S-UAVs are used to synchronously capture a dynamic environment with people moving around. A single-time instance of synchronously captured images of the scene is used to 3D reconstruct the dynamic environment while preserving static scene assumptions of Structure from Motion(SFM).
In the second part of the thesis, we focus on multi UAV autonomy framework for real-world applications of UAVs in perception, wireless communications, and reliable LTA missions. We present ‘Simplenav,’ a navigation stack for heterogeneous, multiple UAVs, and ‘OctoRosSim,’ a computationally lightweight multi-UAV simulation framework for validating the multi-UAV planning and autonomy pipeline. We demonstrate this framework with novel applications of end-to-end autonomy pipelines developed for a coordinated swarm of UAVs.
Committee:
Prof. Hanumant Singh (Advisor)
Prof. Kaushik Chowdhury
Prof. Taskin Padir

Adaptable Communication for Leading, Teaching and Mentoring: A CommLab and Lead 360 Workshop
2022-08-16 16:30 - 17:45 (America/New_York) | Curry Student Center, 360 Huntington Ave., Boston, MA, 02115, United States
Who is your audience, what do you want to say, and what do they want to hear? Many graduate students serve in roles of leadership, teaching and mentoring. This free workshop will give you the tools you need to be an effective leader, teacher and mentor. We will practice using these tools by role playing common challenging situations designed to improve your adaptable communication skills.
RSVP for this workshop brought to you by The CommLab and Lead 360 on Engage: https://neu.campuslabs.com/engage/event/8160180
Location: Curry Student Center 333

Nikita Mirchandani's PhD Dissertation Defense
2022-08-15 14:00 - 15:00 (America/New_York) | 432 ISEC, 360 Huntington Ave, Boston, MA, 02115, United States
“Ultra-Low Power and Robust Analog Computing Circuits and System Design Framework for Machine Learning Applications”
Abstract:
As the scaling of CMOS transistors has almost halted, performance gains of digital systems have also started to stagnate. There is a renewed interest in alternate computing techniques such as in-memory computing, hybrid computing, approximate computing, and analog computing. In particular, analog computing has reemerged as a promising alternative to save power and improve performance specifically for machine-learning (ML) applications. Power and chip area efficiency make analog computing highly appealing for implementing deep learning algorithms on-chip, computing circuits for the internet-of-things (IoT) devices, and implantable and wearable biomedical devices. However, compared to digital computing, analog computing methods have not nearly been utilized to their fullest potential due to longstanding challenges related to reliability, programmability, power consumption, and high susceptibility to variations.
The subject of this dissertation research is to develop robust ultra-low power analog hardware suitable for machine learning applications. First, a robust analog design methodology is presented to address issues of variability in analog circuits. A constant transconductance design technique using switched capacitor circuits is presented. The design approach is then applied to build circuits for ML applications. An analog vector matrix multiplier (VMM) is designed to be used in the convolutional layer in an ML analog computing vision hardware platform. Computing circuits are tested as part of an image classification DNN algorithm on the MNIST dataset and can achieve a classification accuracy of 96.1%.
The design approach is also used to design an analog computing system architecture for detection of seizures using EEG signals. A conventional EEG monitoring system includes an analog front-end (AFE), ADC, digital filtering stage, EEG feature extraction engine, and SVM classification. Such systems suffer from high power and chip area requirements. The corresponding analog architecture is composed of AFE amplifiers to provide gain for the incoming signal. The AFE is followed by an analog filtering stage, where spectral power from each of the bands is used as a feature for seizure classification. The output of each filter is applied to a corresponding feature extraction circuit to continuously monitor the onset of a seizure in an ultra-lower power mode with sub-threshold analog processing. The system level architecture is first modeled to obtain classification accuracy of seizures. Simulation times for the design of such complex analog systems can be prohibitively long, particularly when the impacts of nonidealities such as noise, nonlinearity, and device mismatches have to be considered at the system level. The simulation time is reduced by building accurate models of the analog blocks for faster simulations. The analog models help to define the required specifications for each block in order to achieve a specified system-level classification accuracy.
Infrastructure circuits like oscillators and voltage regulators for the proposed SoC are presented. A 254 nW 21 kHz on-chip RC oscillator with 21.5 ppm/oC temperature stability is presented to provide stable clock source for the proposed SoC. Finally, novel lightweight hardware security primitives are described to equip individual IoT device with side-channel resistant crypto-implementations, and unique ID or key
generation.
Committee:
Prof. Aatmesh Shrivastava (Advisor)
Prof. Marvin Onabajo
Prof. Yong-Bin Kim

Gordon Institute Virtual Information Session
2022-08-09 17:30 - 18:30 (America/New_York)
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 completion of the program, more than 88% of the 2020 class reported increased leadership responsibility, while more than 50% of the 2020 class reported being promoted within one year of graduation.
Our Director of Admissions will be directly answering your application questions for Fall 2022.
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.

Mahdiar Sadeghi's PhD Dissertation Defense
2022-08-05 09:30 - 10:30 (America/New_York)
“Model-based decision making in life sciences”
Abstract:
Mathematical models are key tools in rational decision-making processes. A “good” model is expected to reproduce experimental observations, which enables predictions outside the previous experimental settings. The accuracy of predictions depends on the assumptions used to model the system. The objective of this study is to explore possible approaches to deploy models in order to generate new hypotheses in life sciences. A few biological systems relevant to protein translation, chemotherapy, immunotherapy, and epidemics are considered. Models are analyzed numerically/analytically to optimize a new decision/control. In protein translation processes, it is discovered that no switching policy is better than constant rates to maximize ribosome flow. In a particular experimental setting of chemotherapy, a new dosing plan for chemotherapy is identified and predicted to result in maximum shrinkage of the tumor volume. In immunotherapy, key features of binding kinetics of T-cell engagers in pre-clinical experiments are discussed. Moreover, epidemic models under social distancing guidelines are studied. Considering a single-interval social distancing based on the start time and the duration of the social distancing shows a surprising linear relationship. Some of the results presented in this dissertation are shown to be valid in multiple applications.

Committee:
Prof. Eduardo Sontag (Advisor)Dr. Irina KarevaProf. Carey RappaportProf. Bahram ShafaiProf. Mark Niedre

Tong Jian's PhD Dissertation Defense
2022-08-04 14:00 - 15:00 (America/New_York)
“Robust Sparsified Deep Learning”
Abstract:
This dissertation studies robustness issues around DNN deployments on resource constrained systems, under both environmental and adversarial input adaptation. We propose a means of compressing a Radio Frequency deep neural network architecture through weight pruning, and provide a systems-level analysis of the implementation of such a pruned architecture at resource-constrained edge devices. In particular, we jointly train and sparsify neural networks tailored to edge hardware implementations.
Next, we propose a new learn-prune-share (LPS) algorithm for achieving robustness to environment adaptation in the field of lifelong learning. Our method maintains a parsimonious neural network model and achieves exact no forgetting by splitting the network into task-specific partitions via a weight pruning strategy optimized by the Alternating Direction Methods of Multipliers (ADMM). Moreover, a novel selective knowledge sharing scheme is integrated seamlessly into the ADMM optimization framework to address knowledge reuse.
Furthermore, we investigate the Hilbert-Schmidt Information Bottleneck as regularizer (HBaR) as a means to enhance adversarial robustness. We show that the Hilbert-Schmidt Information bottleneck enhances robustness to adversarial attacks both theoretically and experimentally. In particular, we prove that the HSIC bottleneck regularizer reduces the sensitivity of the classifier to adversarial examples.
Finally, we propose a novel framework Pruning-without-Adversarial-training (PwoA) for the purpose of achieving adversarial robustness on resource-constrained systems. PwoA can efficiently prune a previously trained robust neural network while maintaining adversarial robustness, without further generating adversarial examples. We leverage concurrent self-distillation and pruning to preserve knowledge in the original model as well as regularizing the pruned model via the HBaR.
Committee:
Prof. Stratis Ioannidis (Advisor)
Prof. Jennifer Dy
Prof. Kaushik Chowdhury
Prof. Yanzhi Wang

Library Webinar: Python for Absolute Beginners (Virtual)
2022-08-01 15:00 - 16:00 (America/New_York)
Want to learn Python, but don’t know where to start?
We’ll walk you through everything you need to start using Python, beginning with writing code for the very first time. In this interactive workshop, you will learn about and practice core Python skills such as writing functions, troubleshooting errors, and installing packages. We will also cover tips for continuing to learn Python on your own and in your classes.
No prior coding knowledge required.
No installation required.
Just come ready to learn.
Registration is required. Please register at the library events calendar.
Please note: This session will not be recorded. If you’re interested in learning the material but don’t plan to attend, please don’t register for the session as seats are limited.
This workshop is presented by Kate Kryder, Data Analysis and Visualization Specialist at Northeastern University Library.

AME Academy 2022 Summer Session
2022-07-29 09:00 - 17:00 (America/New_York)
ADDITIVELY MANUFACTURED ELECTRONICS (AME) – THE NEXT GENERATION OF ELECTRONIC DEVICES AND CIRCUITS FROM 2D TO 3D
AME Academy will be delivering a 1 day event at Northeastern University, Boston MA on July 29, 2022.
Join the team of experts led by electronics industry veteran Mr. Gene Weiner, member of the IPC’s Raymond E. Pritchard Hall of Fame, who will present “The Impact of AME Technology and Other Technical Advances for Manufacturing Electronic Circuits.”
He will be joined by other leading AME experts from industry and academia from around the world to talk about packaging, materials, testing, and CAD tools. The applications cases to be presented will include biomedical, RF/mmWave, and robotics devices and systems.
You can attend in-person or online at no charge.
Refreshments and lunch will be provided. All times are in US East coast time.
Register and Agenda

Kimia Shayestehfard's PhD Proposal Review
2022-07-27 14:30 - 15:30 (America/New_York)
“Permutation Invariant Graph Learning”
Abstract:
Graphs are widely used in many areas such as biology, engineering, and social sciences to model sets of objects and their interactions and relationships. Tasks addressed by applying machine learning to graphs, known as graph learning, include node and graph classification, edge prediction, transfer learning, and generative modeling/distribution sampling, to name a few.
Due to high prevalence and multitude of applications of graphs across different fields, graph neural networks have been developed in the past few years. Graph neural networks have shown tremendous success at producing node embeddings that capture structural and relational information of a graph and are discriminative for downstream tasks. However, graph learning algorithms still deal with a major challenge, namely, the lack of permutation invariance: In a dataset of sampled graphs, nodes may be ordered arbitrarily, and aligning them is combinatorial and computationally expensive. Moreover, many graph distance algorithms do not satisfy metric properties, which can significantly hamper the fidelity of the downstream tasks. In this work we address the challenges posed by permutation invariance via combining fast and tractable metric graph alignment methods with graph neural networks. We propose a tractable, non-combinatorial method for solving the graph transfer learning problem by combining classification and embedding losses with a continuous, convex penalty motivated by tractable graph distances. We demonstrate that our method successfully predicts labels across graphs with almost perfect accuracy; in the same scenarios, training embeddings through standard methods leads to predictions that are no better than random. Furthermore, we propose a framework that combines fast and tractable graph alignment methods with a family of deep generative models and are thus invariant to node permutations. These models can be learned by solving convex optimization problems. Our experiments demonstrate that our models successfully learn graph distributions, outperforming competitors by at least 66% in two relevant performance scores and improve the computation time up to 20 times over existing metric graph alignment methods.
Committee:
Prof. Stratis Ioannidis (Advisor)
Prof. Dana Brooks (Advisor)
Prof. Tina Eliassi-Rad

Siyue Wang's PhD Dissertation Defense
2022-07-21 14:30 - 15:30 (America/New_York)
“Towards Robust and Secure Deep Learning Models and Beyond”
Abstract:
Modern science and technology witness the breakthroughs of deep learning during the past decades. Fueled by the rapid improvements of computational resources, learning algorithms, and massive amounts of data, deep neural networks (DNNs) have played a dominant role in many real world applications. Nonetheless, there is a spring of bitterness mingling with this remarkable success – recent studies have revealed the limitations of DNNs which raise safety and reliability concerns of its widespread usage: 1) the robustness of DNN models under adversarial attacks and facing instability problems of edge devices, and 2) the protection and verification of intellectual properties of well-trained DNN models.In this dissertation, we first investigate how to build robust DNNs under adversarial attacks, where deliberately crafted small perturbations added to the clean inputs can lead to wrong prediction results with high confidence. We approach the solution by incorporating stochasticity into DNN models. We propose multiple schemes to harden the DNN models when facing adversarial threats, including Defensive Dropout (DD), Hierarchical Random Switching (HRS), and Adversarially Trained Model Switching (AdvMS). Besides, we also propose a stochastic fault-tolerant training scheme that can generally improve the robustness of DNNs when facing the instability problem on DNN accelerators without focusing on optimizations for individual devices.The second part of this dissertation focuses on how to effectively protect the intellectual property for DNNs and reliably identify their ownership. We propose Characteristic Examples (C-examples) for effectively fingerprinting DNN models, featuring high-robustness to the well-trained DNN and its derived versions (e.g. pruned models) as well as low-transferability to unassociated models. To better perform functionality verification of DNNs implemented on edge devices for on-device inference applications, we also propose Intrinsic Examples. Intrinsic Examples as fingerprinting of DNN can detect adversarial third-party attacks that embed misbehaviors through re-training. The generation process of our fingerprints does not intervene with the training phase and no additional data are required from the training/testing set.
Committee:
Prof. Xue Lin (Advisor)Prof. Yunsi FeiProf. Yanzhi Wang

Abhimanyu Sheshashayee's PhD Dissertation Defense
2022-07-21 09:00 - 10:00 (America/New_York)
Location: 532 ISEC
“Wake-up Radio-enabled Wireless Networking: Measurements and Evaluation of Data Collection Techniques in Static and Mobile Scenarios”
Abstract:
Multi-hop wireless networks such as Wireless Sensor Networks and in general, networks without the support of a fixed infrastructure, which enable most applications of the Internet of Things, are comprised of wirelessly communicating nodes that are often powered by batteries. In many relevant scenarios—ranging from precision agriculture to oceanographic surveillance—it is inconvenient or impossible to replenish or replace the energy systems of these nodes, which limits the operational lifespan of the network. One of the most significant sources of power consumption comes from idle listening on the node’s wireless transceiver (main radio). This consumption can be reduced by endowing the nodes with Wake-up Radio (WuR) technology: Nodes keep their main radio off while listening for a signal via an ultra-low-power auxiliary radio used only for wake-up purposes. When the appropriate signal is received, the node turns its main radio on, conducts the necessary exchange of packets, and then turns off its main radio. This strategy allows for a considerable reduction in power consumption.This dissertation investigates data collection approaches that leverage WuR technology to maximize the lifespan of multi-hop networks for data gathering, via routing and via a Mobile Data Collector (MDC). We analyze contemporary WuR technology, isolating the main criticalities of the state-of-the-art, including range and data rates. We use WuR prototypes with highly desirable characteristics to conduct experiments to measure effective communication ranges, in both static and mobile scenarios. We then examine the application of WuR technology to data collection based on multi-hop routing. We devise new techniques and evaluate the effects of different WuR characteristics on the performance of routing, considering for the first time what the network performance could be if we could overcome the limitation of current WuRs.The culmination of this dissertation focuses on mobile data collection protocols and approaches. We conduct a comprehensive survey of mobile data collection studies and protocols. We develop a robust taxonomy to set the framework for our analyses of various methodologies and elements of mobile data collection. Guided by our review of the literature, we define two collection strategies: a simple naïve strategy, and a novel AI-driven adaptive strategy. Both strategies leverage WuR technology to minimize the amount of time SNs remain awake. Considering both duty cycle-based and WuR based scenarios, we conduct extensive experiments with a quad-rotor UAV-MDC and a network of WuR-enabled wireless sensor motes. We replicate these experiments in our simulator, informed by the parameters and characteristics observed in our real-world experiments. Having validated our simulations, we proceed to execute exhaustive simulation-based experiments. We evaluate the effects of scale (namely, network size and deployment region size) on the performance of the naïve and adaptive strategies, and we contrast the energy efficiency. The WuR-based scenarios experience considerably lower time spent awake, which gives rise to longer network lifespan. The adaptive strategy minimizes the time taken for each collection cycle, thereby reducing the amount of time spent awake in the duty cycle-based scenarios. The adaptive strategy also results in a noticeable reduction in both the awake duration and latency for the WuR-based scenarios.
Committee:
Professor Stefano Basagni (Advisor)Professor Kaushik ChowdhuryProfessor Tommaso Melodia

Gordon Undergraduate Leadership Development Workshop
2022-07-20 17:00 - 18:00 (America/New_York) | 431 Stearns, 431 Stearns Center, 360 Huntington Ave, Boston, MA, 02115, United States
Enhance your co-op experience with the Gordon Undergraduate Leadership Development Workshop. This engineering leadership workshop is designed for Northeastern University undergraduate engineering juniors and seniors during their second or third co-op experience. Workshop sessions are designed to be completed in parallel with co-op.
The program includes a series of engineering leadership development activities focused on expanding leadership skills, engaging in more meaningful interactions with their supervisors, and taking active roles in shaping their overall co-op experiences.
The primary objective of the workshop is to enhance the value of Northeastern’s world-renowned cooperative education (co-op) program for Northeastern undergraduate engineering students. The workshop offers a supplementary curriculum that makes engineering leadership advancement a focus of the co-op experience.
Students that register for this leadership development workshop will attend a two-part engineering leadership workshop. The first workshop session will take place on July 20th and the second session will take place on July 27th, 2022 in The Stearns Center room 430.
In the first session, students complete a strengths finder, which awakens their curiosity about their own leadership styles and tendencies. In the second session, faculty members introduce engineering leadership in the context of personal leadership styles, power and influence, and situational leadership.
In the months that follow, interested participants complete a series of five self-directed modules intended to heighten opportunities for learning, growth, and interaction within their co-op organization. Upon completion of each module, students submit their work to program faculty members for review and feedback.

Shuangjun Liu's PhD Dissertation Defense
2022-07-18 10:00 - 11:00 (America/New_York)
Location: 532 ISEC
“United Human Pose: Integrating Domain Knowledge and Machine Learning”
Abstract:
Deep learning (DL) approaches have been rapidly adopted across a wide range of fields because of their accuracy and flexibility, but require large labeled training data. This presents a fundamental problem for applications with limited, expensive, or private data (i.e. Small Data Domains). There are two basic approaches to reduce data needs during model training: (1) incorporate domain knowledge in the learning pipeline through the use of data-driven or simulation-based generative models, and (2) decrease inference model learning complexity via data-efficient machine learning. This PhD research is unfolded around addressing small data relevant problems in the context of human pose estimation by leveraging the existing research and filling in key research gaps with original work. We started with introducing a specific human pose estimation problem, in-bed pose estimation and present our solutions to this problem in an increasing order of feasibility, that make use of (1) conventional non-deep inference models, (2) fine-tuning already trained deep model with limited data, and (3) building and training a pose estimation model from scratch using a novel dataset.
This practical application also introduced us new challenges such as 3D human pose estimation when no 3D pose data is available in the target domain (e.g. in-bed pose domain) and dense physical signal sensing from vision signals (e.g. contact pressure estimation).
In order to address the small data problem in a more general way,
we also explored estimating 3D human poses without using any real 3D pose data but only easy-to-get synthetic human models. We introduced a semi-supervised data augmentation approach via the use of 3D graphical engines and tested its effectiveness in training pose inference models against real human pose data.
Committee:
Prof. Sarah Ostadabbas (Advisor)
Prof. Raymond Fu
Prof. Octavia Camps

Science on Tap: Design and Perception of Heptic Devices for Social Communication
2022-07-15 15:00 - 17:00 (America/New_York) | 206 Egan, 360 Huntington Ave, Boston, MA, 02115, United States
COE PhD Council presents
SCIENCE ON TAP
Design and Perception of Heptic Devices for Social Communication
Dr. Cara Nunez
Postdoctoral Research Fellow, Harvard John A. Paulson School of Engineering and Applied Sciences Faculty Fellow
Assistant Professor (Incoming July 2023), Sibley School of Mechanical and Aerospace Engineering, Cornell University
Join us for free ice cream and a cool talk!
