ECE PhD Dissertation Defense: Flavius Pop
2021-12-01 12:00 - 13:00 (America/New_York)
PhD Dissertation Defense: Intrabody Communication for Real-Time Monitoring of Implanted Medical Devices based on Piezoelectric Micromachined Ultrasonic Transducers
Flavius Pop
Location: Zoom Link
Abstract: Nowadays when we think about medical devices and patient monitoring, we can easily imagine ourselves laying down in a hospital bad, wires coming out of everywhere, and being looked after by nurses and physicians. Scary and not that comfortable! For this reason, medical wearable devices are becoming more popular for at-home monitoring and transmitting data back to the hospital. Sometimes wearables are not enough, this is why Implanted Medical Devices (IMDs) are still required to monitor many vital signs (blood flow, insulin level, neurons reading etc.) and act upon these readings (nerve stimulation, heart defibrillation, insulin pumping etc.). In order to be minimally invasive, reduce the risk of infection and rejection from the body, and last a long time (avoiding any further surgery) the IMDs require robust wireless communication technology to communicate with the external world. In this presentation I am going to show how we can implement an ultrasonic wireless communication link based on Piezoelectric Micromachined Ultrasonic Transducers (pMUTs) arrays. PMUT arrays can be integrated with existing IMDs, used for wireless power charging, and can enable communication links for receiving and transmitting data. During the first part of the presentation I will show the modeling and design of the pMUT arrays, followed by the fabrication process and the device’s characterization for system level validation. At this point, the communication link is implemented with arrays implanted in a tissue phantom and the channel is characterized at several distances. During the second part of the presentation I will show novel techniques to improve the ultrasonic communication link such as duplexing matching networks for bandwidth definition and direct modulation for implantation depth increase and direct bitstream feeding. In the future I envision that the number of IMDs are going to increase, and therefore I developed a scanning protocol that will allow medical doctors to find all implanted devices. This is the equivalent of an “ultrasonic stethoscope”. Given the small form-factor of the IMDs these will have little to no space for a battery, limiting the operation lifetime. For this reason, I developed an Ultrasonic Wakeup Receiver (UWuRx) based and on the direct modulation system and on a Micromachined Electro-Mechanical System (MEMS) switch which allows for near zero-power consumption in the idle state. This UWuRx enabled on-demand device usability and limited the idle power consumption, which leads to battery life extension.

ECE PhD Proposal Review: Zulqarnain Qayyum Khan
2021-12-01 15:30 - 16:30
PhD Proposal Review: Interpretable Machine Learning for Affective Neuroscience and Psychophysiology
Zulqarnain Qayyum Khan
Location: Zoom Link
Abstract: In this thesis, we leverage Machine Learning to investigate questions of interest in affective psychophysiology and neuroscience . We argue for and apply appropriate existing methods where possible and analyze the results they provide. Where existing methods fail to provide an answer we propose and build new models. We demonstrate the use of Hierarchical Clustering to investigate autonomic nervous system reactivity during an active coping stressor task, revealing physiological indices of challenge and threat. Similarly, we leverage Dirichlet Process Gaussian Mixture Modelling (DP-GMM) to reveal the variation in affective experience during a context-aware experience sampling study and to investigate the relationship between emotional granularity and cardiorespiratory physiological activity using resting state data for participants in the same study. We propose and develop Neural Topographic Factor Analysis (NTFA), a novel factor analysis model for fMRI data with a deep generative prior that teases apart participant and stimulus driven variation and commonalities and learns a latent space that can shed light on important neuroscientific phenomenon such as individual variation and degeneracy.
Based on the work we have already done, we propose three further lines of research that we intend to include in this thesis. First, NTFA can essentially be viewed as a family of models, where appropriate modifications can be made depending on what questions are needed to be answered. Leveraging this, we propose explicitly adapting NTFA to tackle the question of degeneracy in neural responses. This involves introducing another latent space which can be used to capture and visualize the interaction of each participant with each stimulus in a given fMRI study. The arrangement of inferred embeddings in this latent space can then suggest presence or absence of different types of degeneracy in neural responses among participants in response to the presented stimuli. Second, during the course of this interdisciplinary research we realized that there is a need for a comprehensive work that sheds light on the assumptions and limitations of some of the most popular machine learning methods used commonly in the sciences (specially psychology), and provide recommendations on how researchers can be more mindful of the underlying assumptions machine learning methods make. This can then equip users of ML methods to draw more appropriate conclusions from the results they get. We intend to include this in our thesis. Third, continuing along the same lines, there is also a need for better explanation models for the increasingly complicated ML models in use today. This is especially true in health sciences where the knowledge of why an ML model made a particular decision is almost as important as that decision being accurate. To this end we propose a theoretical work that ties the reliability of explanation models to the robustness of the models they are trying to explain.

Beyond the Pandemic – Transformative Engineering
2021-12-02 12:00 - 13:00
In the College of Engineering, we prepare the next generation of engineers to solve real-world global challenges. Join Dean Gregory D. Abowd for a panel discussion on the ways in which the pandemic has influenced our engineering curriculum and driven our students and faculty to innovate new solutions to issues related to health, sustainability, security and more.
This event is complimentary but registration is required.
All registrants will receive an email with information on accessing this virtual event.
Register Now
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ECE PhD Proposal Review: Tong Jian
2021-12-02 15:00 - 16:00
PhD Proposal Review: Robust Sparsified Deep Learning
Tong Jian
Location: Zoom Link
(ID: 75807284369, Passcode: [REDACTED]
Abstract: In this thesis, we investigate and address robustness concerns about DNN-based real-life applications on resource constrained systems, environment adaptation, and adversarial learning, respectively. We propose a means of compressing a Radio Frequency (RF) 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. Under only negligible accuracy loss (less than 1%), we can achieve at most 27.2x pruning rate for 50-device classification. We demonstrate the efficacy of our approach over multiple edge hardware platforms and our method yields significant inference speedups, 11.5x on the FPGA and 3x on the smartphone, as well as high efficiency.
Furthermore, 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 an ADMM-based weight pruning strategy. Moreover, a novel selective knowledge sharing scheme is integrated seamlessly into the ADMM optimization framework to address knowledge reuse. We show that our approach achieves significant improvement over the state-of-the-art methods on multiple real-life datasets.
Finally, we investigate the HSIC bottleneck as regularizer (HBaR) as a means to enhance adversarial robustness. We show that the HSIC 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. Our experiments on multiple benchmark datasets and architectures demonstrate that incorporating an HSIC bottleneck regularizer attains competitive natural accuracy and improves adversarial robustness, both with and without adversarial examples during training.

The Power of Alternative Data: an Entrepreneur's Tale
2021-12-02 18:30 - 20:00 | Raytheon Amphitheater (240 Egan), 360 Huntington Ave, 240 Egan, Boston, MA, 02115, United States
Please join the Galante Engineering Business Program in welcoming Yiannis Tsiounis as he reviews how cellphone location data helps investors monitor companies; how real estate owners understand the demographics and occupancy ratio of their existing or competitive/prospective properties; and how insurance companies price risk. Yiannis will also share his entrepreneurial journey building and growing companies.
Yiannis is the CEO of Advan Research, which he founded in 2015, and an acting investor and advisor to several startups. He founded and led BQuotes, a fixed income price discovery platform in 2005. BQuotes was acquired by Moody’s in 2008. From 2003-2005 he was a Partner at Etolian Capital, a Fixed Income hedge fund. He was a consultant for Concord EFS, a payments processor, from 2002-2003, and prior to that, he was the co-founder and CTO of InternetCash, an internet payments platform, from 1999 to 2001. He was a senior member of the research staff at GTE Labs from 1997-1999. Yiannis holds a Ph.D. in Cryptography with a thesis in Anonymous Electronic Cash and a Master’s in Computer Science from Northeastern University, and a Bachelor’s in Mathematics from the University of Athens. He has published 14 peer-refereed papers in Cryptography and Privacy, and has been an invited speaker at MIT, NIST, RSA, Sandia National Laboratories, and Ecole Normale Superieure, among others.

First Year Engineering Expo
2021-12-03 08:30 - 17:00
Come by the Curry Student Center Indoor Pit to see the projects that our first-year engineering students have been working on this term.

ECE PhD Dissertation Defense: Murphy Wonsick
2021-12-03 10:00 - 11:00
PhD Dissertation Defense: Supervisory Control for Humanoid Robots Through Virtual Reality Interfaces
Murphy Wonsick
Location: ISEC 655
Abstract: Recent advancements in robotics have allowed robots to become capable enough to be used in a wide variety of domains that are dangerous for humans to operate in, such as disaster relief operations, exploration of extraterrestrial planets, bomb disposal, or nuclear decommissioning efforts. However, current supervisory control interfaces that allow humans to explore and interact in these environments through remote presence and teleoperation are complex and often require expert operators. Virtual reality provides a medium to create immersive and easy-to-use teleoperation interfaces. Virtual reality allows operators to visualize and interact with 3D data in a 3D environment that is not possible with traditional interfaces that make use of 2D devices, such as monitors, keyboards, mice, tablets, and/or game controllers. Yet, development of supervisory control virtual reality interfaces for robot operation is still very limited. Most present work in virtual reality interfaces focuses on direct teleoperation and not on high-level control that supervisory control interfaces can provide. In this dissertation, we focus on developing virtual reality supervisory control interfaces for remote robot operation. We specifically focus on high degree-of-freedom robots, such as humanoid robots or mobile manipulator robots, as they are the most suited types of robots for remote operation. To accomplish this, we first look to better understand and define humanoid robot capabilities using NASA’s humanoid robot, Valkyrie. Following, we synthesize the current state-of-the-art supervisory control interfaces for humanoid robots to create our own supervisory control interface using traditional devices. We then use this information to create a virtual reality supervisory control interface for Valkyrie. Finally, we look to improve virtual reality interfaces for robot operation through a user-centered design approach to inform future development on virtual reality interfaces.

Bioengineering PhD Student Seminar Series
2021-12-03 12:00 - 13:00 | 101 Churchill, 360 Huntington Ave, Boston, MA, 02115, United States
Join us Friday, December 3 at 12:00 PM in Churchill Hall 101 for the Bioengineering PhD Student Seminar Series! Our first presenter will be Bioengineering PhD student Hector Millan Coto “Longitudinal effects of electronic cigarette smoking on lung mechanics on Apoe Mice”. Our second presenter will be Amber Williams “Non-Invasive, Real-Time detection of Circulating Tumor Cell Clusters using Diffuse Light”.

Dialogue of Civilization in Turkey (Info Session)
2021-12-03 12:00 - 13:00
We are excited to announce the DOC program in Turkey in Summer 1 2022 (link). This program includes two courses:

IE 4512: Engineering Economy: Explores economic modeling, financial analysis, and decision-making approaches
IE 4699: ST in Industrial Engineering: A variety of topics such as sports, politics, finance, entrepreneurship, business development, healthcare, manufacturing, machine learning, religion, culture, art, history in the Turkish environment will be covered.

Program facts:

The program will take ~ 4 weeks, in two (2) cities (Istanbul, and Izmir), and be hosted by three universities.
This program covers a NUPath, “Interpreting Culture”.

If you are interested, you can attend the Info Session details planned for next week. By attending this meeting, you can familiarize yourself with the program, its itinerary, and learn about host universities, program activities, and kickstart your application process. You can find the meeting details below:
DOC in Turkey Info Session:

Date/Time: Friday Dec 3rd, 12:00 pm -1:00 pm
Location: In-person (SL 119) or via Zoom
Please RSVP by Wed, Dec 1st using this link. (Required)
Pizza will be served at the meeting, so just bring your soda
Please feel free to circulate this email to your eligible friends (in Engineering, Business, Finance, Economy, etc.) who might be interested

All students are highly encouraged to attend the Info Session in person. If you are not able to attend, please use the RSVP link to provide your info and the recorded session will be sent to you afterward.

ECE PhD Dissertation Defense: Mehdi Nasrollahpourmotlaghzanjani
2021-12-06 08:00 - 09:00
PhD Dissertation Defense: RFICs for Biomedical Magnetic and Magnetoelectric Microsystems
Mehdi Nasrollahpourmotlaghzanjani
Location: Zoom Link
Abstract: Design and analysis of the advanced biomedical circuit and systems in wide variety of applications has emerged a significant interest. Not only in different engineering disciplines, but also in a variety of applications such as neuroscience, COVID-19, etc. In this study, we are proposing an implantable device, handheld device for detecting different diseases and the RFIC design for the ME antenna and passive devices and sensor evaluations to diagnose different diseases.
First, we show a miniaturized implantable device for deep brain implantation that provides wireless power transfer efficiency (PTE) of 1 to 2 orders of magnitude higher than the reported micro-coils for brain stimulation. The proposed device will simultaneously measure the as magnetic field activity when neurons are firing. The proposed rectangular ME antenna wireless power transfer efficiency is 0.304 %, which is considerably higher than that of micro-coils. Measurements results show that the maximum achievable power transfer of a ME antenna outperforms that of an on-silicon coil by approximately 7 times for a Tx-Rx distance of 0.76 cm and 3.3 times for a Tx-Rx distance of 2.16 cm.
In the second part we will go over the RFIC design for the bio-implant devices, evaluation of the ME antennas for communication purposes and the circuit interface to measure the ME and GMI sensors. A low-noise amplifier (LNA) topology with tunable input matching and noise cancellation utilized in a Bluetooth receiver frontend is introduced and described in this study, which was designed and optimized to interface with a magnetoelectric (ME) antenna in a 0.35 µm MEMS-compatible CMOS process. Input matching at the LNA-antenna interface is controlled with a circuit that varies the effective impedance of the gate inductor using a control voltage. Tunability of 455 MHz around 2.4 GHz is achieved for the optimum S11 frequency with a control voltage range of 0.3 V to 1.2 V. Besides, a miniaturized CMOS oscillator using microelectromechanical system (MEMS) resonating at 159 MHz frequency is designed and simulated to drive ME sensors. The proposed oscillator provides a phase noise as low as -131.3 dBc/Hz at 10 kHz and -137.9 dBc/Hz at 100 kHz offset frequencies while consuming 2.24 mW power.
For final part, we will discuss the handheld device design for early diagnosis of different diseases such as, lung cancer, Alzheimer, Covid-19, etc through exhaled breath on the molecularly imprinted polymer (MIP) gas sensors. A novel gas sensor has been developed that might be applied to diagnose Covid-19 from the exhaled breath instantly. The handheld device is designed to read the sensor activities and send the data to the android phone to show if the patient is at risk or not. For this purpose, a lock-in amplifier is designed to read the resistance in ac domain and transmit the digitized data through Bluetooth communication link.

Experiential Entrepreneurship Intersession Opportunity
2021-12-07 08:00 - 2021-12-10 17:00
Want to hone your entrepreneurial skills over winter break while working directly with tech startups? Registration is now open for the Experiential Entrepreneurship Intersession course running from January 3rd through January 14th.
Offered virtually or in-person through the Roux Institute at Northeastern University, students will learn about the venture creation process and work hand-in-hand with tech startups that have emerged from the accelerator and residency programs at the Roux Institute.
Past guest lecturers have included:

Ali Goldstein Norup, co-founder of kpiReady and current Head of VC and Startup Ecosystem, North Americas at Google Cloud
Ben Chesler, co-founder of Imperfect Foods and current Associate Director of Entrepreneurship at the Roux Institute
Jesse Bardo, co-founder of EverTrue and current Director at Silicon Valley Bank

And, if you register by December 8th, you will receive an invite to the Techstars Demo Day in Portland, Maine. The event will gather the Maine startup community for an in-person presentation from each of the 10 companies selected for the inaugural Roux Institute Techstars Accelerator class. Following you’ll be invited for a reception at the Roux Institute where guests will meet and mingle with the startups, investors, and community members.
To view the course:

Visit Banner and select the term, Spring 2022 Semester. Even though Intersession Term courses meet between semesters, they have been administratively assigned to Spring 2022 semester.
Click Advanced Search on the Browse Classes page.
In the attribute field, choose Intersession Term Course. All the Intersession Term offerings will appear.

Registration for intersession will close Friday, December 10th at 11:59 (EST).

ECE PhD Dissertation Defense: Sara Banian
2021-12-07 14:00 - 15:00
PhD Dissertation Defense: Content-Aware AI-Driven Design Assistance Frameworks for Graphic Design Layouts
Sara Banian
Location: Zoom Link
Abstract: Designing user interfaces (UIs) for mobile interaction is widespread but still challenging. It is important for the overall user satisfaction and application success. During the design process, designers express their requirements through images describing the UI’s layout, structure, and content. Designers, however, encounter key challenges throughout the design process. For example, searching for inspiring design examples is challenging because current search systems rely on only text-based queries and do not consider the UI structure and content. Furthermore, these systems often focus on overall page-level layout over individual UI components. Also, creating wireframe templates is difficult for many designers as it necessitates an understanding of different design guidelines. Therefore, it is critical to support designers by developing effective design tools to help them be more productive and creative.
In this dissertation, I aim to explore how to develop design assistance methodologies to augment the process of UI layout design, with a particular focus on visual search and layout generation. Specifically, for this exploration, I seek to investigate the use of advanced deep learning models in the context of mobile UI layout design. Processing layouts differs from processing pixel-level images in that it necessitates processing both the semantic (e.g., labels) and spatial (e.g., coordinates) content of the layout to model the data properly. To achieve this, I explore the design problems from both the data and the model side. First, I present a large-scale UI dataset that accurately specifies the interface’s view hierarchy (i.e., UI components and their location). Second, I contribute the VINS framework, which is composed of three systems LayVis, CompVis, and TransVis that addresses layout-based visual search, component-based visual search, and layout generation, respectively.
First, I introduce LayVis, an object-detection layout-based retrieval model. It takes as input a UI image and retrieves visually similar design examples. Next, I introduce CompVis, a component-based visual search system to easily retrieve individual UI components via convolutional neural networks (CNNs). Specifically, for a given query, the system allows to retrieve (1) text label synonyms, (2) similar UI components, and (3) design examples containing such components. Finally, I present TransVis, a transformer-based generative framework that investigate how to generate UI layouts according to user specifications and following design practices. It specifically models UI layouts as an ordered sequence of elements based on spatial and semantic relationships for (1) generating complete UI layouts, (2) auto-completing existing UI layouts seamlessly, and (3) supporting many design elements per layout.
Overall, the work presented in this dissertation contributes to augmenting the UI layout design. Through quantitative and qualitative evaluation of VINS, we conclude the following: (1) Advanced deep learning models can aid in the development of design assistance methodologies for layout design; and (2) Designers perceive the use of VINS inspiring and useful. Such insights, combined with the open-sourced large-scale dataset, can help the research community develop more effective AI-based data-driven design tools. This work presents future opportunities to investigate different deep learning models within the context of layout design and how designers interact with these AI-based models.

ChE Seminar Series: Catalytic Oxidation of Methane, the “Other” Greenhouse Gas
2021-12-08 12:00 - 13:00
ChE Seminar Series Presents:
Dr. Michael Harold
Department of Chemical & Biomolecular Engineering, University of Houston
Abstract:
The abundant domestic natural gas resources has motivated the accelerated development of natural gas powered vehicles and stationary engines. With the primary constituent of abundant NG being methane (CH4), NG has a higher H:C ratio than gasoline or diesel and therefore its combustion produces less CO2. However, CH4 is itself a more potent greenhouse gas (GHG) than CO2 with a GHG potential about 85 times that of CO2. Uncombusted CH4 must be eliminated in order to clear the way for the growth in the NG engine market. Current state-of-the-art Platinum Group Metal (PGM) catalysts are ineffective in eliminating methane. Our research is focused on the study and development of a new class of cost effective structured catalysts with reduced PGM loadings for both stoichiometric and lean methane oxidation. For stoichiometric oxidation we show that the combination of spinel mixed metal oxide (AB2O4) addition and lean-rich feed modulation results in significant enhancement in the catalyst performance. Detailed study of feed modulation parameters (frequency, amplitude), catalyst design (composition, architecture) and spatiotemporal reactor features provide insight into and optimization of the underlying mechanism. The enhancement is attributed to the transient oxidation of methane conversion inhibitors CO and H2 by the spinel. Up to a 30% reduction in PGM loading is possible with negligible loss in performance. For lean oxidation we study and develop an in situ method to regenerate methane oxidation catalysts. Periodic reductant (H2, CO) pulsing mitigates the detrimental water poisoning of Pd-Pt catalyst. The pulsing is able to regenerate the catalyst deactivated by water by removal of OH-groups from the catalysts surface, but also promoted its activity after repeated application of pulsing for several hours. This state of high activity is stable for several hours under the tested lean conditions.
Bio:
Mike Harold is the Cullen Engineering Professor in the Department of Chemical and Biomolecular Engineering at the University of Houston. With expertise in catalysis and reaction engineering, Harold is the author of more than 180 peer-reviewed papers and book chapters and has given over 350 presentations and invited lectures. Harold received his BS at Penn State and PhD from the University of Houston (UH). He joined the faculty at University of Massachusetts at Amherst in 1985 where he became Associate Professor. In 1993 Harold joined DuPont Company, where he held technical and managerial positions. In 2000 Harold became the Dow Chair Professor and Department Chair at UH, a position he held for 16 years. Mike was appointed Editor-in-Chief of the AIChE Journal in 2012 and will soon end his 10 year term. His honors include the Excellence in Applied Catalysis from the Southwest Catalysis Society in 2019, the Ester Farfel Award at UH in 2013, and AIChE Fellow in 2014.
Please contact a.ramsey@northeastern.edu for the seminar link.

ECE PhD Dissertation Defense: Arjun Singh
2021-12-09 15:00 - 16:00
PhD Dissertation Defense: Design, Modeling, and Operation of Plasmonic Devices for Smart Communication Systems in the Terahertz Band
Arjun Singh
Location: 332 ISEC or Teams Link
Abstract: The terahertz (THz) band is an attractive spectral resource for realizing future communication systems, with the potential of supporting very high-speed data rates and increasingly dense networks. However, the lack of a well-developed technology that operates at these frequencies has remained a challenge for the scientific community. The very high propagation losses at THz frequencies and the decimating impact of everyday objects on THz wave propagation necessitate an up-haul of the conventional communication link, with smart control over the radiation, propagation, and detection of THz signals. Additionally, device physics at THz frequencies, among them the very high gain requirement and large electrical size, may render the often-held assumptions of the propagation model invalid. An interdisciplinary approach spanning device design and operation, and wireless propagation and signal processing is required.
To this end, the proposed research herein addresses the facilitation of an end-to-end communication link with graphene plasmonics as the cornerstone of the fundamental device physics. The devices designed can be utilized to effectively overcome the limited communication distance –The grand challenge of the THz band. Different from other undertakings, every attempt is made to ac-knowledge and accommodate the complex trade-offs in the design process. First, a novel graphene based plasmonic array architecture is proposed, explained, and modeled. The fundamental radiating element of the array architecture, called the plasmonic front-end, consists of a self-sufficient plasmonic source, a plasmonic modulator that acts as a phase controller, and a plasmonic nano-antenna for effective radiation. The designed array is compact and provides complete beamsteering support, with a new tailored algorithm developed for beamforming weight selection. Numerical evaluations and full-wave finite difference frequency domain (FDFD) simulations with COMSOL Multi-physics are utilized to verify array operation. Exploiting these properties, a multi-beam array design is presented next, where orthogonal spatial filters are utilized to provide support for spatial multiplexing towards the realization of ultra-massive MIMO (UM-MIMO). Taking this further, the design considerations of an interleaved plasmonic array are presented, in which the beamsteering capability is utilized to simultaneously achieve radio frequency interference (RFI) mitigation with channel capacity maximization for multi-user scenarios. Additionally, to realize the vision of a smart communication system with a programmable wireless environment, a hybrid reflectarray is presented. The fundamental element is modeled as a jointly designed and integrated metal-graphene patch. Numerical and simulation results are utilized to demonstrate the attractive properties of the reflectarray as compared to other proposed counterparts, including an independence from the incoming angle of the impinging wave, dynamic phase control capability, and strong reflection efficiency. The requirements of a THz communication link and their impact on the common communication protocols are considered next. It is shown that certain scenarios may render regular array operation invalid, motivating codebook designs that function in the massive near-field Fresnel zone of electrically large THz devices. Numerical simulations and theoretical analysis are presented to highlight their potential in improving system performance and capacity while reducing the system complexity. Finally, the significant milestones in the fabrication process of these devices are also presented.

ECE PhD Proposal Review: Michele Pirro
2021-12-10 11:00 - 12:00
PhD Proposal Review: Scandium-doped Aluminum Nitride for new MEMS technologies
Michele Pirro
Location: 432 ISEC
Zoom Link Meeting ID: 938 0086 0379 https://northeastern.zoom.us/u/abZS2SmtT1
Abstract: The increasing demand for data is pushing the MEMS industry to more performant and area-efficient systems to be used in IOT nodes as sensors and RF-components. In this market, AlN plays a pivotal role thanks to the piezoelectric properties accompanied with good stability over power and temperature in miniaturized devices. In fact, AlN is already present in different commercial MEM systems, such as duplexers, ultrasound generators, energy harvesters and so on, proving a mature mass-production process flow. The required more stringent specifications in terms of bandwidth, losses and efficiency are pushing towards piezoelectric materials with higher coupling coefficient, but still in a compatible post-CMOS process flow. Luckily, recent works showed how it is possible to enhance the piezoelectric effect by doping AlN with Scandium, allowing up to 400% increase in the d33 piezoelectric coefficient. The enhanced acoustic transduction along with the recent demonstration of ferroelectric switching and the post-IC compatibility, is making Sc-doped AlN a new material with the potential not only to replace AlN, but also to integrate different functionalities within the same component. Academy and industry all over the world are actively researching the actual potential of the material but there is still a lack of information on high-Sc concentration, which would allow lower-voltage switching along with higher d33. This work has the main objective to show Sc-concentration > 28% and their piezo/ferroelectric response for a new class of microelectromechanical devices.
The proposal will discuss the advance in the process flow of high Sc- concentrations, showing the impact of the deposition parameters on the material properties. Thin films with good crystallinity on IC-substrate and enhanced d33 are reported, along with first attempts to resonator-devices. An in-depth ferroelectric characterization will show how coercive field and leakage current are the main limiting factors the material is facing to integrate its memory effect. For this purpose, the work will present how tuning of Sc-concentration, substrate-rf and bulk stress can ease these limiting factor, opening to new acoustic devices with memory functionalities. The last part will focus on the co-integration of acoustic properties with ferroelectric switching for tunable filters and ultrasonic generators in post-IC compatible substrates.

ECE PhD Proposal Review: Leonardo Bonati
2021-12-10 13:45 - 14:45
PhD Proposal Review: Softwarized Approaches for the Open RAN of NextG Cellular Networks
Leonardo Bonati
Location: 532 ISEC
Abstract: The 5th (5G) and 6th generations (6G) of cellular networks, also known as NextG, will bring unprecedented flexibility to the wireless cellular ecosystem. Because of its typically closed and rigid market, the telco industry has incurred high costs and non-trivial obstacles in delivering those new services and functionalities to satisfy the requirements and the demand of NextG networks. To break this trend the industry is now moving towards open architectures based on softwarized approaches, which will 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 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 new 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, and (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. The effectiveness of our solutions in achieving superior control and performance of the RAN is demonstrated 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-RENEW platform from the U.S. PAWR program.

CILS Seminar: arivis™, Imaging Software
2021-12-14 12:00 - 13:00
Join this seminar to learn how to make microscopy image analysis more straightforward. An arivis™ representative will be presenting on topics such as image segmentation, multiview registration, storyboard, colocalization, and image processing (denoise, decon, etc).

ECE PhD Proposal Review: Jaehyeon Ryu
2021-12-16 09:30 - 10:30
PhD Proposal Review: Engineering Functional Nanomesh for Advanced Neuroelectronics
Jaehyeon Ryu
Location: Zoom Link
Abstract: Transparent electronics have emerged as promising platforms for neural interfacing by enabling simultaneous electrophysiological recording and optical measurements. Also, there are high demands for stretchable devices due to their low modulus and compatible interface with irregular and soft neural tissue. However, current transparent, stretchable approaches are usually limited by their scalability for neuroelectronic applications. Here, I present multi-functional nanomesh as an approach to achieve stretchable, transparent microelectrode arrays (MEAs) with excellent scalability. By stacking mechanical supporting polymer, gold, and conductive polymer in a nanomesh structure on elastomer substrate, multilayer nanomesh-based MEAs show excellent stretchability, transparency, and electrochemical properties with single neuron scale dimensions. The performance of these multi-functional nanomesh-based MEAs has been characterized through bench testing, and I plan to perform in vivo validation in the remaining period of my thesis. These highly stretchable and transparent multilayer nanomesh MEAs are promising for applications ranging from neuroscience to biomedical devices.

Distinguished Speaker Series in Robotics: Gregory S. Chirikjian
2021-12-16 14:00 - 15:00 | ISEC Auditorium, 805 Columbus Ave, Boston, MA, 02115, United States
Distinguished Speaker Series in Robotics
Gregory S. Chirikjian: Robot Imagination: Affordance-Based Reasoning about Unknown Objects
Location: ISEC Auditorium or Zoom Link
Abstract: Today’s robots are very brittle in their intelligence. This follows from a legacy of industrial robotics where robots pick and place known parts repetitively. For humanoid robots to function as servants in the home and in hospitals they will need to demonstrate higher intelligence, and must be able to function in ways that go beyond the stiff prescribed programming of their industrial counterparts. A new approach to service robotics is discussed here. The affordances of common objects such as chairs, cups, etc., are defined in advance. When a new object is encountered, it is scanned and a virtual version is put into a simulation wherein the robot “imagines’’ how the object can be used. In this way, robots can reason about objects that they have not encountered before, and for which they have no training using. Videos of physical demonstrations will illustrate this paradigm, which the presenter has developed with his students Hongtao Wu, Meng Xin, Sipu Ruan, and others.
Bio: Gregory S. Chirikjian received undergraduate degrees from Johns Hopkins University in 1988, and a Ph.D. degree from the California Institute of Technology, Pasadena, in 1992. From 1992 until 2021, he served on the faculty of the Department of Mechanical Engineering at Johns Hopkins University, attaining the rank of full professor in 2001. Additionally, from 2004-2007, he served as department chair. Starting in January 2019, he moved the National University of Singapore, where he is serving as Head of the Mechanical Engineering Department, where he has hired 14 new professors so far. Chirikjian’s research interests include robotics, applications of group theory in a variety of engineering disciplines, applied mathematics, and the mechanics of biological macromolecules. He is a 1993 National Science Foundation Young Investigator, a 1994 Presidential Faculty Fellow, and a 1996 recipient of the ASME Pi Tau Sigma Gold Medal. In 2008, Chirikjian became a fellow of the ASME, and in 2010, he became a fellow of the IEEE. From 2014-15, he served as a program director for the US National Robotics Initiative, which included responsibilities in the Robust Intelligence cluster in the Information and Intelligent Systems Division of CISE at NSF. Chirikjian is the author of more than 250 journal and conference papers and the primary author of three books, including Engineering Applications of Noncommutative Harmonic Analysis (2001) and Stochastic Models, Information Theory, and Lie Groups, Vols. 1+2. (2009, 2011). In 2016, an expanded edition of his 2001 book was published as a Dover book under a new title, Harmonic Analysis for Engineers and Applied Scientists.

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Presented by the Institute for Experiential Robotics

ECE PhD Dissertation Defense: Jinghan Zhang
2021-12-16 15:00 - 16:00
PhD Dissertation Defense: Domain Design Space Exploration: Designing a Unified Platform for a Domain of Streaming Applications
Jinghan Zhang
Location: ISEC 362 or Zoom Link
Abstract: Many demanding streaming applications share functional and structural similarities with other applications in their respective domain, e.g., video analytics, software-defined radio, and radar. This opens the opportunity for specialization to achieve the needed efficiency and/or performance.
Platforms integrating many accelerators (ACCs) is a primary approach for efficient, high-performance stream computing.
However, designing one platform for each application is not economical due to the high costs of nonrecurring engineering (NRE) and time-to-market (TTM).
To this end, the concept of domain platforms is proposed, which takes advantage of similarities across applications and designs one unified platform to accelerate a domain of applications instead of focusing on a single reference application.
This dissertation approaches designing domain platforms from a function-level (kernel-level) acceleration through a heterogeneous ACC-rich platform, where each ACC is specialized to accelerate a particular function.
There is a great challenge to select ACCs allocated in the domain platform, considering the large design space and performance balance across many applications.
However, current Design Space Exploration (DSE) tools only focus on an individual application in isolation (e.g., one particular vision flow) for allocating a platform, but not a set of similar applications.
This dissertation introduces Greedy Guided Mutation (GGM) to speed up the mutation in the GIDE algorithm, which calculates an ACC score according to current allocation to guide mutation.
Alternatively, Rapid Domain Platform Performance Prediction (RDP^3) methods are introduced to replace a large number of the slow platform assessment in domain DSE, which avoids the complex application binding exploration.
In the experiments, GGM reduces 84.8% of exploration time with a 0.23% loss of the final OpenVX domain platform’s performance.
RDP^3 using a machine learning method yields an even more significant speedup, saving 90.8% of exploration time with only 0.0003% performance loss.
DmDSE is a milestone to broaden DSE scope from individual applications to the domain level. It tremendously pushes the domain platform design from manually and engineering experience guided into a general automatic process.

ADSE- End of year cookie swap and pizza party
2021-12-17 12:00 - 13:00 | Curry Student Center, 360 Huntington Ave., Boston, MA, 02115, United States
Good afternoon Northeastern Graduate Students,
Please join the Alliance for Diversity in Science and Engineering and your fellow grad students for our end-of-year event on December 17th from 12-1 pm in Curry 440! We will have pizza and will be hosting a cookie swap, so bring a dozen or two of your favorite homemade (or not, we’re not going to judge) cookies! Please don’t feel pressured to bring cookies, but certainly, bring yourself and a friend for a final get-together this year. Please fill out the link below to RSVP so we can get an idea of how much pizza we should bring as well, and look forward to seeing everyone there!
https://tinyurl.com/ycksfmb8
ADSE@NEU

ECE PhD Proposal Review: Anahita Moradmand
2021-12-20 11:00 - 12:00
PhD Proposal Review: Robust Observer Structures and Control Design for Linear and Nonlinear Dynamical Systems with Applications
Anahita Moradmand
Location: Zoom Link
Abstract: This thesis focuses on special class of observers and controllers for different and seperable nonlinear systems. In the first step, a robust fault detection approach using observers for linear systems is proposed where we combine the unknown inout observer UIO and an extended proportional integral observer PIO, which has a fading term for robust fault detection. The integrated observer is called proportional integral fading unknown input observer (PIFUIO).
Furthermore, we extend our result to nonlinear systems with special structures as the design of nonlinear observer had limitation for general types of nonlinear systems. In the second step, analysis and design of positive systems is considered whereby positive stabilization and the design of positive unknown input observer (PUIO) are introduced. Also, the robust stability analysis of this class of systems is studied in which the robust stability is formulated in terms of LMI. the class of separable positive nonlinear systems is also analyzed and the design of observer and controller are provided. Finally, we extend our desgin from Lipschitz type nonlinearity to state-dependent type by focusing on interconnected systems where we propose a distributed control architecture to take advantage of the global performance similar to centralized control and leverage the benefits of decentralized control.

ECE PhD Dissertation Defense: Sungho Kang
2022-01-11 11:00 - 17:00
PhD Dissertation Defense: Plasmonically Enhanced Infrared Sensing Microsystems
Sungho Kang
Location: Zoom Link
Abstract: Infrared (IR) spectroscopic sensing has become a key technique in multidisciplinary environments such as military applications, industrial safety control, and smart homes, by providing an accurate and non-disruptive analysis of the target objects. Recently the demand for high performance and compact IR spectroscopy systems has been steadily growing due to the advent of Internet of Things and the burgeoning development of miniaturized sensors. The key challenge lies in realizing high performance IR detectors that have low noise, high IR throughput, and spectral sensitivity in a miniaturized form factor. This challenge has been tackled in the study of micro-electromechanical sensing systems and metamaterial absorbers, in which the ultra-high resolution sensing capability and the near-perfect IR absorption properties can be simultaneously exploited in a minimized footprint. The metal-insulator-metal (MIM) IR absorbers, in particular, are characterized by the near-unity absorptance with lithographically tunable peak absorption wavelength and spectral selectivity in an ultra-thin form factor, suitable for the implementation of miniaturized spectroscopic IR microsystems. The exceptional IR absorption characteristics realized by the MIM IR absorbers and their sub-wavelength form factor allow for seamless integration with the existing IR sensing microsystem and the unprecedented IR sensing performance for the next generation IoT sensing solutions. In this defense, novel development of miniaturized IR spectroscopic sensor and maintenance-free wireless human sensors based on the two key technologies are presented: (1) multispectral resonant IR detector array and (2) plasmonically-enhanced long-wavelength infrared micromechanical photoswitch. This study shows that the demonstrated technologies can replace the traditional IR sensors with the new generation IR sensing microsystems that are characterized by their high performance, compact form factor, power efficiency and low cost.

ECE PhD Dissertation Defense: Andac Demir
2022-01-14 14:00 - 15:00
PhD Dissertation Defense: Automated Bayesian Network Exploration for Nuisance-Robust Inference
Andac Demir
Location: Zoom Link
Abstract: A fundamental challenge in the analysis of physiological signals is learning useful features that are robust to nuisance factors e.g., inter-subject & inter-session variability, and achieve the highest nuisance-invariant classification performance. Towards resolving this problem, we introduce 3 frameworks: AutoBayes, which is an AutoML approach to conduct neural architecture search for research prototyping, and GNN based frameworks: EEG-GNN and EEG-GAT.
The ultimate goal of the AutoBayes framework is to identify the conditional relationship between a physiological dataset, associated task labels, nuisance variations and potential latent variables in order to robustly infer the task labels invariant of nuisance factors. Nuisance factors in the case of physiological datasets could be variations in subjects or sessions, but we only focus on subject variations in the experiments. AutoBayes enumerates all plausible Bayesian networks between data, labels, nuisance variations and potential latent variables, detects and prunes the unnecessary edges according to Bayes-Ball Algorithm, and then trains the resulting DNN architectures for different hyperparameter configurations in an adversarial / non-adversarial or a variational / non-variational setting to achieve the highest validation performance. Instead of hyperparameter tuning for model optimization, AutoBayes concentrates on the architecture search of plausible Bayesian networks, and achieves state-of-the-art performance across several physiological datasets. Furthermore, we ensemble several Bayesian networks by stacking their posterior probability vectors in a higher level learning space, train a shallow MLP as a meta learner, and measure the task and nuisance classification performance on a hold-out dataset. We observe that exploration of different inference Bayesian networks has a significant benefit in improving the robustness of the machine learning pipeline, and the parallel activity of vast assemblies of different Bayesian network models significantly reduces variation across subjects in the cross-validation setting.
In the second part of the dissertation, we benchmark the performance of EEG-GNN and EEG-GAT against the AutoBayes framework. CNN’s have been frequently used to extract subject-invariant features from EEG for classification tasks, but this approach holds the underlying assumption that electrodes are equidistant analogous to pixels of an image and hence fails to explore/exploit the complex functional neural connectivity between different electrode sites. We overcome this limitation by tailoring the concepts of convolution and pooling applied to 2D grid-like inputs for the functional network of electrode sites. Furthermore, we develop various GNN models that project electrodes onto the nodes of a graph, where the node features are represented as EEG channel samples collected over a trial, and nodes can be connected by weighted/unweighted edges according to a flexible policy formulated by a neuroscientist. The empirical evaluations show that our proposed GNN-based framework, EEG-GNN, outperforms standard CNN classifiers across ErrP and RSVP datasets, as well as allowing neuroscientific interpretability and explainability to deep learning methods tailored to EEG related classification problems. Besides that, EEG-GAT employs multi-head attention mechanism in conjunction with the GNN architecture to learn the graph topology of observations instead of utilizing a graph shift operator that is heuristically constructed by a domain expert. This implicitly allows the exploration of the functional neural connectivity peculiar to a cognitive task between pairs of EEG electrode sites as well as EEG channel selection, which is critical for reducing computational cost, and designing portable EEG headsets.

COE Global Co-op Info Session
2022-01-19 18:00 - 19:00 | Raytheon Amphitheater (240 Egan), 360 Huntington Ave, 240 Egan, Boston, MA, 02115, United States
Join the College of Engineering Global Co-op team in learning about global co-op opportunities for Summer II/Fall 2022.
Topics discussed will include:

Search techniques and global positions in your field
What to consider when interested in a global co-op
Logistics for moving and living abroad
Tips and resources for self-developing global positions

Attendance to one of these sessions is required if you plan to do a global co-op in Summer II/Fall 2022.
RSVP on the NUworks Events Calendar.
Please reach out to Sally Conant, Global Co-op Coordinator, s.conant@northeastern.edu or Kristina Kutsukos, Global Co-op Coordinator, k.kutsukos@northeastern.edu for additional information

ECE PhD Proposal Review: Vedant Sumaria
2022-01-20 11:00 - 12:00
PhD Proposal Review: Exploring Micro-Machined Glass Shell Resonators For Sensor Application
Vedant Sumaria
Location: Zoom
Abstract: Optical resonators have been playing an important role in modern optics. They are fundamental in any laser device, etalon for optical filtering, accurate measurement for non-linear optics. Bulk optical resonators that use two or more mirrors are usually used in all branches of modern linear and non-linear optics. There are many limitations in using such systems because they cannot provide high performance (high quality (Q) factor) and their size, weight, and alignment, creates stability problems. To solve these problems, there was an emerging class of miniaturized dielectric cavity based optical resonators that exploited the light confinement phenomenon through internal reflection. These resonators have a circular symmetry, and they sustain modes known as the Whispering Gallery Modes (WGM) that is nothing but electromagnetic waves that circulate and are confined within the structure. Fabrication of these dielectric optical resonators is simpler and comparatively inexpensive. They demonstrate higher mode stability and higher performance.
In this proposal review, I will discuss the working principles of a WGM resonator and study the various loss mechanisms to improve the quality factor. Further I will discuss the fabrication of on chip glass-blown microspherical shell resonators. These on-chip spherical glass shells are micrometers to millimeters in diameter with ultra-smooth surfaces and micrometer wall thicknesses which can sustain optical resonance modes with high Q-factors up to 50 million. Further we discuss various methods used to etch the backside silicon to create a liquid core optical resonator. This etching leads to increase in the surface roughness leading to loss of resonance. We optimized etching methods and parameters to keep the resonance as high as 18 million. By etching the silicon resonator’s temperature sensitivity is improved from -1.15 GHz/K to 2.23 GHz/K. This optical WGM sensor is then novel biosensor consisting of a chip-scale whispering gallery mode resonators with High-Q factor and a micro-caloric system. The silicon released shell resonator is elastically coupled to a kapton tubing system. Temperature change in the system induces thermal expansion and thermorefractive changes which can be sensitively monitored through changes in the optical resonance characteristics. We demonstrate a measurement resolution less than 10mK and a method of measuring temperature change to eliminate background noise that shows a great potential for detection of various biomolecules such as urea. We also discuss the possibility to use the sensor as an extremely sensitive IR sensor. Finally, we talk about the future work in immobilization of urease and glucose oxidase to test for analytes like urea and glucose with concentrations in micro-mole.

ECE PhD Proposal Review: Hamed Mohebbi Kalkhoran
2022-01-20 15:30 - 16:30
PhD Proposal Review: Machine Learning Approaches for Classification of Myriad Underwater Acoustic Events Over Continental-shelf Scale Regions with Passive Ocean Acoustic Waveguide Remote Sensing
Hamed Mohebbi Kalkhoran
Location: Zoom Link
Abstract: Underwater acoustic data contain a myriad of sound sources that include bioacoustics related to marine life such as marine mammals and fishes; man-made such as ships, sonar, and airguns; as well as natural geophysical processes such as earthquake, hurricane, and volcanic eruption. 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 (intra-class variation), and high similarity between the calls of some species. In this thesis, we investigate 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.
Humpback whale behavior, population distribution and structure can be inferred from long term underwater passive acoustic monitoring of their vocalizations. Here we employ machine learning approaches to classify humpback whale vocalizations into song and non-song calls. 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 by their calls, 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. Best results were obtained with Random forest classifier, which achieved 95% accuracy, and 85% F1 score. To detect transient sound sources, first we applied Per-Channel Energy Normalization (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.

Disability rights with Mrs. Christine Griffin
2022-01-21 15:00 - 16:00
Learn about disability rights with a Nationally recognized lawyer on Friday, 21st January at 3 p.m. in ISEC 655, 6th floor or virtually through zoom – https://northeastern.zoom.us/j/95320296228

ECE Seminar: Nathan Lazarus
2022-01-24 13:00 - 14:00
ECE Seminar: Stretchable Magnetics for Soft Robotics
Nathan Lazarus
Location: Zoom Link
Abstract: Recent innovations in making robots from softer biofriendly materials have opened broad new applications ranging from medicine to agriculture. Due to the reliance of much of the field on pneumatic actuation, heavy and rigid pumps, and control circuitry for driving pressure chambers have become a major limitation for fully soft, untethered soft robots. In my talk, I will discuss all aspects of creating soft electromagnets, inductors and power circuits for electromagnetic actuation and power management in stretchable systems. Using unconventional materials like room temperature liquid metals and ferrofluids, we demonstrate record performance for a stretchable inductor. These stretchable inductors are then used to create flexible and stretchable pumps with flow rates nearly two orders of magnitude higher than past demonstrations in the literature and integrated into a simple soft robot demonstrator.
Bio: Nathan Lazarus has worked extensively in areas ranging from mixed signal electronics to MEMS fabrication, with his Ph.D. at Carnegie Mellon culminating in 2012 with the demonstration of the highest recorded fractional sensitivity to date for a capacitive chemical sensor topology integrated with CMOS electronics. Since joining US Army Research Laboratory in May 2012, Dr. Lazarus’s research has focused on stretchable power electronics, soft robotics and 3D printing. He has received numerous awards including ARL’s Honorary Award for Engineering and the Rookie of the Year Excellence in Federal Career Award (Gold) from the Baltimore Federal Executive Board. In 2019, Dr. Lazarus was selected for the Presidential Early Career Award for Scientists and Engineers (PECASE), the highest honor given by the US government for researchers beginning their independent research careers.

COE Global Co-op Info Session
2022-01-25 12:00 - 13:00 | Curry Student Center, 360 Huntington Ave., Boston, MA, 02115, United States
Join the College of Engineering Global Co-op team in learning about global co-op opportunities for Summer II/Fall 2022.
Topics discussed will include:

Search techniques and global positions in your field
What to consider when interested in a global co-op
Logistics for moving and living abroad
Tips and resources for self-developing global positions

Attendance to one of these sessions is required if you plan to do a global co-op in Summer II/Fall 2022.
RSVP on the NUworks Events Calendar. Location- Curry Student Center 333.
Please reach out to Sally Conant, Global Co-op Coordinator, s.conant@northeastern.edu or Kristina Kutsukos, Global Co-op Coordinator, k.kutsukos@northeastern.edu for additional information.
