Northeastern University College of Engineering

Data-Centric Networking: Theory, Algorithms and Applications
2022-04-29 15:00 - 16:15 (America/New_York) | MA
Professor Edmund Yeh will give an invited seminar titled “Data-Centric Networking: Theory, Algorithms and Applications” in the Electrical Engineering Seminar Series at Harvard University John A. Paulson School of Engineering and Applied Sciences.
Friday, Apr 29, 2022
3:00 pm to 4:15 pm | Harvard John A. Paulson, School of Engineering and Applied Science, SEC LL2.221 or Virtually
In the era of big data, domain experts in various engineering and science fields are facing unprecedented challenges in data access, distribution, processing and analysis, and in the coordinated use of limited computing, storage and network resources. To meet this challenge, data-centric network design approaches such as Named Data Networking (NDN) have been proposed, which focus on enabling end users to obtain the data they want, rather than simply on communication between specific nodes.
In this talk, we present new frameworks for the optimization of key functionalities supported by data-centric networking, which are broadly applicable to content delivery networks, peer-to-peer networks, wireless heterogeneous networks, and distributed computing networks. The frameworks enable joint (in-network) caching, request routing, and congestion control, for optimizing metrics including routing costs, data retrieval delay, and content-based fairness. We meet the challenge of the underlying NP-hard problems by exploiting submodularity, matroid structure, DR-submodularity, and by leveraging tools including concave relaxation, stochastic gradient ascent, continuous greedy and Lagrangian barrier algorithms. We develop polynomial-time approximation algorithms with proven optimality guarantees, with particular emphasis on adaptive and distributed implementations. We further discuss the extension of these frameworks for jointly optimal wireless user association, interference management and content caching in wireless heterogeneous networks, and for jointly optimal computation scheduling, caching and request forwarding in distributed computing networks.
Finally, we discuss an ongoing project which applies the optimization frameworks and algorithms to facilitate data distribution and computation in the Large Hadron Collider (LHC) high-energy physics network, one of the largest data applications in the world.

CHME Department Award Ceremony
2022-04-29 17:00 - 18:30 | Blackman Auditorium, 360 Huntington Ave, Ell Hall, Boston, MA, 02115, United States
Chemical Engineering is hosting its annual Department Award Ceremony in Blackman Auditorium on Friday, April 29, 2022, 5:00-6:30 pm.

Forge Showcase
2022-05-02 19:00 - 20:30 | 010 Hayden Hall, 010 Hayden Hall, 360 Huntington Ave, Boston, MA, 02115, United States
Forge is Northeastern’s home of builders, entrepreneurs, and leaders. No matter your major, we want to help you learn how to make impactful products and become an entrepreneur.
Showcase celebrates each team’s accomplishments from the semester, with pitching, tabling and refreshments. Come by The Sherman Center’s Makerspace located in the basement of Hayden Hall (right under the Dunkin Donuts) from 7:00 – 8:30pm on May 2nd, to see celebrate with us!

Generate Showcase
2022-05-04 18:30 - 20:30 | Alumni Center, 716 Columbus Ave, 6th Floor, Boston, MA, 02120, United States
Generate is Northeastern’s student led design studio aimed at fostering entrepreneurial engineering on campus.
Showcase is Generate’s semester end event that celebrates our teams and their progress throughout the term. Stop by the Alumni Center on Wednesday, May 4th from 6:30-8:30 pm to listen in on presentations, hear from speakers, and take a look at all the hard work our teams have put in this semester. We hope to see you there!

Applying for the NSF Graduate Research Fellowship Program: Grad Student Panelists and Faculty Share Advice and Insights
2022-05-17 15:00 - 16:00 | MA
Join the College of Engineering CommLab and the Khoury College Graduate Program, for a virtual panel discussion revealing the experiences and insights of doctoral students who have been awarded fellowships and the faculty who have helped support them through the process. The six panelists, responding to questions from the moderator and audience, will discuss such topics as the application or nomination process, tips for effective application statements and supporting materials, and the place of a fellowship award in one’s doctoral career.
Join through Zoom

Gordon Institute Virtual Information Session
2022-05-18 09:00 - 10:00, 2022-05-25 17:30 - 18:30, 2022-06-08 09:00 - 10:00, 2022-06-22 17:30 - 18:30, 2022-07-12 17:30 - 18:30 | MA
Learn how you can earn a Graduate Certificate in Engineering Leadership as a stand-alone certificate or in combination with one of twenty Master of Science degrees offered through Northeastern’s College of Engineering, College of Science, or Khoury College of Computer Sciences.
The National Academy of Engineering recognized The Gordon Institute of Engineering Leadership (GIEL)for its innovative curriculum that combines technical education, leadership capabilities, and the “Challenge Project”: an opportunity for students to receive master’s level credit while working in industry.
By aligning technical proficiency with leadership capabilities, GIEL accelerates the development of high-potential engineers and prepares them to lead complex projects early in their careers. Upon 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.

Modeling Quantum Information and Quantum Materials Problems
2022-05-19 15:00 - 16:00 | MA
JOINT SPECIAL COLLOQUIUM BY COLLEGE OF SCIENCE AND COLLEGE OF ENGINEERING
Modeling Quantum Information and
Quantum Materials Problems
Prof. Hai-Ping Cheng, University of Florida
Thursday, May 19, 2022; 3:00 to 4:00 p.m.
Hosts: Arun Bansil and Swastik Kar
Zoom meeting link
I will discuss how large-scale computational efforts based on model Hamiltonians informed by first-principles simulations with inputs from experiments have allowed us to gain unprecedented clarity in the microscopic mechanisms underlying macroscopic properties of materials. Such an approach is critically important for exploration of viable qubit candidates beyond the current technologies which are based on superconductors and ion traps. Covering six orders of magnitude in energy scales, magnetic couplings present a fundamental challenge to first-principles calculations. In this talk, I will highlight some of our recent work concerning inter- and intra-molecular spin-spin couplings, magneto-electric couplings, magnetic molecules on substrates and in junctions, and decoherence. I will also show results from simulations of interfaces and vertical junctions that consist of two topological insulators and 2D materials.
Dr. Hai-Ping Cheng is a Professor of Physics and Director of the Quantum Theory Project at the University of Florida. She currently leads the DOE Energy Frontier Research Center (EFRC) for Molecular Magnetic Quantum Materials (M2QM). She received her Ph.D. from Northwestern University (1988), was a postdoctoral researcher at the University of Chicago (1989-1991), and a research scientist at the Georgia Institute of Technology (1992-1994). Her research interests include magnetic molecules for quantum information sciences, interface phenomena and transport across tunneling junctions, and reduction of thermal noise in amorphous oxides.

Women in Engineering
2022-05-25 09:00 - 10:00 | MA
A celebration of women in engineering with a panel of faculty, alumnae, and current student.

Ziyue Xu's PhD Proposal Review
2022-06-08 09:30 - 10:30 | 432 ISEC, 360 Huntington Ave, Boston, MA, 02115, United States
“High Efficiency RF Energy Harvesting and Power Management Circuits Techniques for IoT Application”
Abstract:
As the number of Internet of Things (IoT) devices is continuing to grow, there is a need that a significant percentage these devices operate at ultra-low power (ULP) levels, either using harvested energy or using a small battery with a long lifetime. Energy harvesting techniques can help to achieve long lifetimes, but the system should be able to operate efficiently with a small amount of harvested energy and often from low voltages. Energy harvesting from solar, thermal, vibration, and radio-frequency (RF) are increasingly being used to realize batteryless operation for IoT and biomedical applications. A typical multi-input energy harvesting system including multiple energy transducers, maximum power point tracking (MPPT), matching network (MN), and DC-DC converter. Solar cells and thermoelectric generators have a few mV to hundreds of mV open-circuit voltage that require maximum power tracking to make sure the optimal power extraction is achieved. The piezoelectric transducer is modeled as AC source with internal resistance from 10s Ω to kΩ that requires AC-DC conversion, known as rectification to better use the energy. And the following DC-DC regulation stage is to regulate the output voltage to deal with the sudden change of the load or the input voltage drop. Among these techniques, RF energy harvesting system is particularly promising for biomedical and IoT devices where other sources are not readily available. Several of these applications are utilizing widely used WiFi and Bluetooth low-energy (BLE) communication standards. These applications along with the wirelessly-powered neural implantable medical devices (n-IMD) for neural stimulation and recording are also benefiting from ultra-low power (ULP) circuits and systems design advancements. Since the available RF power decreases rapidly with distance, it is desirable to design rectifiers that are able to operate with low incident power. This Ph.D. proposal presents a simplified design approach and analysis of RF energy harvesting rectifiers for different design objectives. The proposal also includes the design of a new self-biased gate (SBG) rectifier with a non-linear gate biasing technique. At lower power levels, the SBG rectifier drops the entirety of output voltage to create a higher gate bias. However, to address the issue of leakage at higher input power levels, the gate-biasing technique drops only a fraction of the output voltage. This approach helps to realize high efficiency across input power range. The fully integrated, high-efficiency SBG-based RF energy harvesting circuit can also provide a high output voltage of 9.3 V with a 30% end-to-end efficiency (PHE). Further, to enhance the available RF energy to a remotely located RF energy receiver, the proposal presents a highly efficient distributed RF beamforming technique. To improve the power delivery in the downstream power management circuits, a boost converter architecture that can reduce switching noise injection by changing its switching frequency is also presented. The associated power management system includes a boost converter operating in DCM, FVC and a digital control loop. The system is capable of providing a stable 1V supply for RF receiver front-ends with very low performance impact.

Committee Members:
Prof. Aatmesh Shrivastava (Advisor)
Prof. Marvin Onabajo
Prof. Nian X. Sun

FPP Virtual Fair STEM - LATAM
2022-06-09 15:00 - 19:00 | MA
Representatives from Northeastern University Graduate School of Engineering will be participating in this event.

Hussein Hussein's PhD Proposal Review
2022-06-16 12:00 - 13:00 | MA
“Parametric Circuits for Enhanced Sensing and RF Signal Processing”
Abstract:
Massive deployments of wireless sensor nodes (WSNs) that continuously detect physical, biological or chemical parameters are needed to truly benefit from the unprecedented possibilities opened by the Internet‑of‑Things (IoT). Just recently, new sensors with higher sensitivities have been demonstrated by leveraging advanced on‑chip designs and microfabrication processes. Yet, WSNs using such sensors require energy to transmit the sensed information. Consequently, they either contain batteries that need to be periodically replaced or energy harvesting circuits whose low efficiencies prevent a frequent and continuous sensing, even impacting the maximum range of communication. Here, we discuss a new battery-less and harvester-free remote sensing tag, namely the subharmonic tag (SubHT), leveraging unique nonlinear characteristics to fundamentally break any previous paradigms for passive WSNs. SubHT can sense and transmit information without requiring supplied or harvested DC power. Also, it transmits the sensed information at a difference frequency from the one of its interrogation signal, rendering its reader immune from multi-path, from clutter and from its own self‑interference. Also, even though SubHT may not require any advanced and expensive manufacturing, its unique nonlinear response enables extraordinary high sensitivities and dynamic ranges that can even surpass those achieved by the most advanced on-chip sensors. More interestingly, SubHT can be even configured to operate in a “threshold sensing” mode, making it able to respond to any interrogation signal only when the sensed parameter has exceeded a remotely reprogrammable threshold, as well as to memorize any violation in a sensed parameter without requiring any memory components. In this talk, the first SubHT prototypes for temperature sensing will be showcased. Even more, we will show how including high quality factor (Q) resonators in a SubHT’s network allows to implement even more functionalities, such as the long-range identification or tracking of any items or localization and navigation in a GPS denied environment. Yet, the dynamics exploited by SubHT can also be leveraged to address various needs along radio-frequency (RF) chains. In this regard, we show how the SubHT’s nonlinear dynamics can be leveraged to build components, such as parametric filters, frequency selective limiters and signal to noise enhancers, that improve the stability of RF frequency synthesizers and instinctually suppress co-site or self-interferes, paving an unprecedented path towards integrated radios with improved performance and longer battery-life time.
Committee:
Prof. Cristian Cassella (Advisor)
Prof. Marvin Onabajo
Prof. Matteo Rinaldi
Prof. Andrea Alù

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

Xiaolong Ma's PhD Dissertation Defense
2022-06-27 14:00 - 15:00 | MA
“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

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)
Join Zoom Meeting
https://northeastern.zoom.us/j/91205821278
Meeting ID: 912 0582 1278
One tap mobile
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Join by Skype for Business
https://northeastern.zoom.us/skype/91205821278

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

Graduate School of Engineering Campus Tour
2022-07-02 14:00 - 15:00 | MA
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!

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

Sara Garcia Sanchez's PhD Dissertation Defense
2022-07-07 13:00 - 14:00 | MA
“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

Alexandria Will-Cole's PhD Proposal Review
2022-07-07 13:00 - 14:00 | MA
“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

CommLab: Discussing Your Poster
2022-07-07 17:00 - 18:00 | MA
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.

Bengisu Ozbay's PhD Proposal Review
2022-07-11 10:00 - 23:00 | MA
“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.

Zulqarnain Qayyum Khan's PhD Dissertation Defense
2022-07-12 15:30 - 16:30 | MA
“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

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.

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!

Shuangjun Liu's PhD Dissertation Defense
2022-07-18 10:00 - 11:00 | MA
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

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.
