Northeastern University College of Engineering

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
“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 | 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 | 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 | 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, 2022-07-12 17:30 - 18:30, 2022-06-22 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 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
“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
“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
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
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
“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
“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
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 | 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
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 | 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!

Leonardo Bonati's PhD Dissertation
2022-07-13 11:00 - 12:00 | 432 ISEC, 360 Huntington Ave, Boston, MA, 02115, United States
“Softwarized Approaches for the Open RAN of NextG Cellular Networks”
Abstract:
The 5th and 6th generations of cellular networks (5G and 6G), also known as NextG, will bring unprecedented flexibility to the wireless cellular ecosystem. Because of a typically closed and rigid market, the telco industry has incurred high costs and non-trivial obstacles for delivering new services and functionalities that satisfy the requirements and the demands of NextG networks. To break this trend the industry is now moving toward open architectures based on softwarized approaches, which afford network operators flexible control and unprecedented adaptability to heterogeneous conditions, including traffic and application requirements. Now, by simply expressing a high-level intent, operators will be able to instantiate bespoke services on-demand on a generic hardware infrastructure, and to adapt such services to the current network conditions. Through disaggregation, network elements will split their functionalities across multiple components—possibly provided by different vendors—interconnected through well-defined open interfaces. The separation of control functions from the hardware fabric, and the introduction of standardized control interfaces, will ultimately enable the definition and use of softwarized control loops, which will bring embedded intelligence and real-time analytics to effectively realizing the vision of autonomous and self-optimizing networks.
This dissertation work focuses on the design, prototyping and experimental evaluation of softwarized approaches for the Open Radio Access Network (RAN) of NextG cellular networks. We analyze the architectural enablers, challenges, and requirements for a programmatic zero-touch control of the very many network elements and propose practical solutions for its realization. We prototype solutions by leveraging open-source software implementations of cellular protocol stacks and frameworks, and heterogeneous virtualization technologies, including the srsRAN and OpenAirInterface cellular implementations, and the O-RAN framework. The contributions of this work include (i) the first demonstration of O-RAN data-driven control loops in a large-scale experimental testbed using open-source, programmable RAN and RAN Intelligent Controller (RIC) components through xApps of our design; (ii) CellOS, a zero-touch cellular operating system that automatically generates and executes distributed control programs for simultaneous optimization of heterogeneous control objectives on multiple network slices starting from a high-level intent expressed by the operators; (iii) OpenRAN Gym, the first publicly-available research platform for the design, prototyping, and experimentation at scale of data-driven O-RAN solutions, and (iv) OrchestRAN, a network intelligence orchestration framework for Open RAN that automates the deployment of data-driven inference and control solutions. The effectiveness of our solutions in achieving superior control and performance of the RAN is demonstrated at scale on state-of-the-art experimental facilities, including software-defined radio-based laboratory setups and open access experimental wireless platforms, such as Colosseum, Arena, and the POWDER and COSMOS platforms from the U.S. PAWR program.

Zulqarnain Qayyum Khan's PhD Dissertation Defense
2022-07-12 15:30 - 16:30
“Interpretable Machine Learning for Affective Psychophysiology and Neuroscience”
Abstract:
In this thesis, we leverage existing Machine Learning (ML) models where appropriate and develop novel models to advance the understanding of affective psychophysiology and neuroscience. Additionally, considering the increased use of ML as a toolbox, we highlight underlying assumptions and limitations of basic ML methods to help better contextualize the conclusions drawn from application of ML in this domain. Similarly, given the increasingly opaque ML models, the resulting rise of methods to explain these models, and the importance of explainability to interdisciplinary research, we investigate theoretical properties of these explainers.
Affective pyschophysiology research typically uses supervised analyses which leave little room for exploration. Studies of motivated performance tasks often focus on two states of threat and challenge, exhibiting somewhat inconsistent physiological properties. Using unsupervised analysis of physiology data, we find evidence for the presence of a third state for the first time, that may help explain these inconsistencies. Similarly, prototypical view of emotion often searches for consistency and specificity, as opposed to constructionist account of emotion which proposes emotion categories as populations of situation-specific variable instances. In results supportive of this constructionist view, we find large variability in both the number and nature of clusters in unsupervised analyses of ambulatory physiological data. Similarly, in functional neuroimaging a largely unsolved challenge is to develop models that appropriately account for the commonalities and variations among participants and stimuli, scale to large amounts of data, and reason about uncertainty in an unsupervised manner. Such models are needed to investigate important neuroscientific phenomena such as individual variation and degeneracy. We develop Neural Topographic Factor Analysis (NTFA), a novel ML model for fMRI data with a deep generative prior that teases apart participant and stimulus driven variation and commonalities, and demonstrate its potential in investigating individual variation and degeneracy.
We further utilize this interdisciplinary research experience to shed light on assumptions and limitations of some of the basic ML methods commonly used in the sciences (especially psychological science). These methods are often used as software packages. We argue that researchers need to be more mindful of their underlying assumptions when drawing conclusions. Along the same lines, ML methods themselves are becoming increasingly blackbox, making it harder to reason about underlying assumptions. This has led to an increased focus on explainers, which provide interpretability to ML methods that is critical for interdisciplinary research. The theoretical properties of these explainers, however, remain understudied. We further the research in this direction by defining explainer astuteness as a measure of robustness and theoretically demonstrate that smooth classifiers lend themselves to more astute explanations.
Committee:
Prof. Jennifer Dy (Advisor)
Prof. Lisa Feldman Barrett
Prof. Dana Brooks
Prof. Karen Quigley
Prof. Octavia Camps

Bengisu Ozbay's PhD Proposal Review
2022-07-11 10:00 - 23:00
“Fast Identification via Subspace Clustering and Applications to Dynamic and Geometric Scene Understanding”
Abstract:
More and more data is needed in order to build new machine learning and computer vision techniques. Using human operators to identify these vast datasets would be too expensive, hence the use of unsupervised learning has grown more common. Piecewise linear or affine models can be used in a broad range of applications connected to system identification and computer vision.
In this proposal, we suggest an efficient method that only requires singular value decomposition of matrices whose size is unaffected by the total number of points. This method only has to be performed (number of clusters) times. We discovered that it is feasible to find the polynomials that represent the hyperplanes by doing a singular value decomposition (SVD) on the empirical moments matrix containing the data. In this approach, the notion of using polynomials and Christoffel functions to conduct SVDs in order to partition data into sets, each of which originates from a different cluster, is central. Data may be segmented and then the parameters of each group can be extracted using application-specific techniques. In particular, the problems that are taken into consideration in this proposal include identification of Auto-regressive with Extra Input (SARX) models, affine linear subspace clustering, two-view motion segmentation, and identification of a group of nonlinear systems known as Wiener systems.
This proposal is structured as follows: to begin with, we offer a semi-algebraic clustering framework for locating reliable subsets from the data, which belongs in a union of varieties and segments the data sequentially using Christoffel polynomials. We employ this strategy for switched system identification and affine subspace clustering challenges. In both instances, the data resides in linear affine varieties. To expand the given approach beyond linear affine arrangements, we reformulate it for quadratic surfaces and further apply it to the two-view motion segmentation task. Finally, using this suggested semi-algebraic formulation, we are able to detect a class of nonlinearities, namely Wiener systems with an even nonlinearity, which is indeed an NP-hard issue.

CommLab: Discussing Your Poster
2022-07-07 17:00 - 18:00
Join the CommLab for a virtual interactive workshop, where we will be discussing best practices for presenting your posters. Bring your poster-even if it is just a draft- and we can help you ensure your story will reach every audience member. We will also provide tips on how to reduce information overload for your audience, helping you more effectively convey your research. Register here by Zoom.

Alexandria Will-Cole's PhD Proposal Review
2022-07-07 13:00 - 14:00
“Morphology, Magnetism, and Transport in Nanomaterials and Nanocomposites”
Abstract:
Magnetic thin film materials and bilayer composites enable unprecedented new applications, ranging from magnetic-based microelectromechanical systems (magnetoelectric sensors, ultracompact magnetoelectric antennas, etc.), terahertz emitters, to spin-orbit-torque driven magnetic memories. Here we focus on two subdisciplines within magnetics – magnetoelectrics and spintronics heterostructures.
The first aspect of the talk is focused on magnetoelectrics. Strain-mediated magnetoelectric coupling (i.e., voltage/electric field control of magnetism, or magnetic field control of electrical polarization) in bilayer composites has received heightened attention in the research community for applications in memory, motors, sensors, communication etc. The composite ME effect is dependent on the magnetostrictive effect (magnetic-mechanical coupling) and the piezoelectric effect (electrical-mechanical coupling), and therefore to improve the composites each constituent phase needs to be optimal. Here we demonstrate the feasibility of machine learning, specifically Bayesian Optimization methods, to optimize ferromagnetic materials, specifically (Fe100−y Gay)1−xBx (x=0–21 & y=9–17) and (Fe100−y Gay)1−xCx (x=1–26 and y=2–18) to demonstrate optimization of structure-property relationships, specifically the compositional effect on magnetostriction and ferromagnetic resonance linewidth. Following the materials optimization study, we present voltage control of ultrafast demagnetization in ME heterostructure of (Fe81Ga19)88B12/ Pb(Mg1/3Nb2/3)O3–PbTiO3. Previous studies implement multiple strategies to tune ultrafast demagnetization namely via the laser pump wavelength, fluence, polarization, and pulse duration as these control the total absorbed energy into the film. Here we present an alternate strategy to tune ultrafast demagnetization with application of an electric field in the ME heterostructure to induce magnetic axis rotation. Additionally, we studied magnetic anisotropy changes and E-field tuning behavior following ultrafast demagnetization.
The second aspect of this talk is focused on spintronics heterostructures, namely ferromagnetic (FM)/topological insulator (TI) or ferrimagnetic insulator (FI)/topological insulator (TI) bilayer composites, and TI sputter growth and characterization. Bilayer FM/TI and FI/TI heterostructures are promising for spintronic memory applications due to their low switching energy and therefore power efficiency. TIs have been grown with molecular beam epitaxy (oriented, epitaxial films) and RF magnetron sputtering (amorphous to crystalline oriented films) and have demonstrated large spin-to-charge conversion efficiencies. However, the reactivity of TIs with FM films is often overlooked in the spin-orbit-torque literature, even though there are reports that it is energetically favorable for topological insulators to react with metals and form interfacial layers. Here we present the interfacial reaction and antiferromagnetic phase formation between MBE-grown Sb2Te3 and sputtered Ni80Fe20 films. Since FM/TI interfaces are highly reactive and form novel interfacial phases, which can encourage spin memory loss, it is critical to explore heterostructures with cleaner interfaces. Recently, we synthesized chemically stable Y3Fe5O12/Bi2Te3 films, which should have a chemically sharp interface. We present preliminary structural and magnetic characterization, followed by proposed experiments to study proximity induced magnetization in these bilayer composites. Concurrent to our investigation spintronic heterostructures, we seek to optimize sputter deposition of TIs. However, sputtering TIs requires enhanced control over defects/stoichiometry as these influence bulk transport. We present preliminary results and propose experiments to elucidate structure-transport relationships, such that we can provide strategies to controllably suppress bulk conduction to access topologically protected surface states.
Committee:
Prof. Nian X. Sun (advisor)
Prof. Don Heiman (co-advisor)
Prof. Yongmin Liu
Dr. A. Gilad Kusne
Dr. Todd Monson

Sara Garcia Sanchez's PhD Dissertation Defense
2022-07-07 13:00 - 14:00
“Learning and Shaping the Wireless Environment: An Integrated View of Sensing, Computing and Communication”
Abstract:
The explosive growth in Internet of Things (IoT) deployments and anticipated data volumes that will be generated within future autonomous devices require collecting and processing large amounts of data, generally transmitted over the wireless channel. Rigid infrastructure deployment that does not adapt to the changing wireless environment is not well suited to handle these new demands. To address this limitation, this dissertation takes a hands-on approach to equip communication systems with technology to learn from, interact with and actuate within the environment. Specifically, we build (i) accurate physics-based predictive models and multimodal sensing techniques to gain awareness of the existing channel, as well as (ii) novel multidisciplinary approaches to intelligently shape the wireless channel towards enhancing the communication link.
We first prove that combining wireless channel modeling, multimodal sensing and robotics provides significant link performance gains. To this extent, we adopt a systems approach to study how millimeter wave (mmWave) radio transmitters on Unmanned Aerial Vehicles (UAVs) provide high throughput links under typical hovering conditions. Based on sensing and modeling efforts, we propose techniques to exploit the information contained in the spatial and angular domains of empirically collected data from GPS, cameras and RF signals. We demonstrate how to mitigate the impact of hovering by (i) selecting near-to-optimum transmission parameters as compared to the mmWave standard IEEE 802.11ad, and (ii) proposing corrective coordinated actions at the UAVs from the robotic controls. These methods achieve mmWave beam-tracking and robust link deployment under event(s) impacting link performance, such as hovering or blockage in the light of sight between transmitter and receiver.
Finally, we experimentally demonstrate how the wireless environment can be interactively shaped through the use of Reconfigurable Intelligent Surfaces (RIS). First, we propose AirNN, a system capable of partially offloading computation into the wireless domain by realizing analog convolutions with over-the-air computation. We demonstrate that such computation is accurate enough to substitute its digital equivalent in a Convolutional Neural Network (CNN). Second, we propose a RIS-based spatio-temporal signal modification approach for channel hardening (i.e., ensure low power fluctuations in the received signal) in a Single-Input Single-Output link and under rich multipath, which is common for IoT 5G+ deployments. We prove that our approach achieves channel hardening similar to a classical Single-Input Multiple-Output (SIMO) system while only using a single antenna element at the receiver end.
All the above theoretical advances are validated with rigorous analysis and experimentation.
Committee:
Prof. Kaushik Chowdhury (Advisor)
Prof. Stefano Basagni
Prof. Josep Jornet

The Future of Manufacturing
2022-07-06 12:00 - 13:00
The Roux Institute presents: The Future of Manufacturing
Featuring Jack Lesko, Director of Engineering Research

Graduate School of Engineering Campus Tour
2022-07-02 14:00 - 15:00
Interested to learn more about the Graduate School of Engineering on the Northeastern 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 locations on campus specific to Engineering, and answer your questions about the Boston campus. Please complete the registration form linked below to select the date and time that works best for you. Tours are open to both admitted and prospective students. We can’t wait to meet you!

A Conversation on ECE and BioE programs in Portland
2022-06-30 08:00 - 09:00
A conversational regarding the Master’s in ECE and Bioengineering program at the Roux Institute in Portland, Maine.

CILS Seminar & Demo: Nanosurf Drive AFM
2022-06-29 12:00 - 14:30 | 136 ISEC, 360 Huntington Ave, 136 ISEC, Boston, MA, 02115, United States
Come learn about Nanosurf’s DriveAFM, a tip-scanning atomic force microscope used for all areas of applications from materials to life science.
An instrument demonstration will follow in the CILS Core Facility in the ISEC basement, 090 from 1:30-2:30pm.
The DriveAFM overcomes drawbacks of other tip-scanning instruments and provides atomic resolution together with fast scanning, fast force spectroscopy, and large scan sizes up to 100 µm.

Topic: CILS Seminar & Demo: Nanosurf DriveAFM
Time: Jun 29, 2022 12:00 PM Eastern Time (US and Canada)
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https://northeastern.zoom.us/j/91205821278
Meeting ID: 912 0582 1278
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Xiaolong Ma's PhD Dissertation Defense
2022-06-27 14:00 - 15:00
“Towards Efficient Deep Neural Network Execution with Model Compression and Platform-specific Optimization”
Abstract:
Deep learning or deep neural network (DNN), as one of the most powerful machine learning techniques, has become the fundamental element and core enabler of the artificial intelligence. Many incredible, bleeding-edge applications, such as community/shared virtual reality experiences and self-driving cars, will crucially rely on the ubiquitous availability and real-time executability of the high-quality deep learning models. Among the variety of the AI-associated platforms, mobile and embedded computing devices have become key carriers of deep learning to facilitate the widespread of machine intelligence. In this talk, I will first focus on a compression-compilation co-design method that deploy a unique sparse model on an off-the-shelf mobile device with real-time execution speed. This method advances the state-of-the-art by introducing a new dimension, fine-grained pruning patterns inside the coarse-grained structures, revealing a previously unknown point in the design space. The designed patterns are interpretable, and can be obtained by a fully automatic pattern-aware pruning framework that achieves pattern library extraction, pattern assignment (pruning) and weight training simultaneously. With the higher accuracy enabled by fine-grained pruning patterns, the unique insight is to use the compiler to re-gain and guarantee high hardware efficiency. We take a step forward by considering a more practical scenario, that the deployment-execution mode for AI tasks no longer satisfy the user preference, and enabling edge training becomes inevitable since it promotes much better personalized intelligent services while strengthen users’ privacy by avoiding data egress from their devices. To this end, I will demonstrate my approaches that use sparsity to achieve fast and efficient training on the edge devices. I will evaluate the static lottery ticket sparse training, and then demonstrate a high-accuracy and low-cost dynamic sparse training framework that makes the edge training possible. It successfully incorporates the pattern-based sparsity into sparse training, and also exploit the data-level sparsity to further improve the acceleration. I will conclude by using our sparse training method on a distributed training scenario, which demonstrates the state-of-the-art accuracy and great flexibility for modern AI model training.
Committee:
Prof. Yanzhi Wang (Advisor)
Prof. Xue Lin
Prof. David Kaeli

A Conversational Webinar on MS DAE in Vancouver
2022-06-23 10:00 - 11:00
A conversational webinar regarding the Masters in Data Analytics & Engineering program in Vancouver.
