Northeastern University College of Engineering

BioE Seminar Series Presents: Ryan Flynn
2022-11-16 12:00 - 13:00 (America/New_York)
Department of Bioengineering Seminar Series presents
Ryan Flynn, Ph.D
Assistant Professor, Boston Children’s Hospital’s Stem Cell Program and Harvard University’s Stem Cell and Regenerative Biology Department, Boston MA
“GlycoRNAs: bridging the gap between glycobiology and RNA biology”
Wednesday, November 16th, 2022
12:00 pm – 1:00 pm EST
105 Shillman Hall
ABSTRACT:
Glycans modify lipids and proteins to mediate inter- and intramolecular interactions across all domains of life. RNA is not thought to be a major target of glycosylation. Here, we challenge this view with evidence that mammals use RNA as a third scaffold for glycosylation. Using chemical and biochemical approaches, we found that conserved small noncoding RNAs bear sialylated glycans. These ‘‘glycoRNAs’’ were present in multiple cell types and mammalian species, in cultured cells, and in vivo. GlycoRNA assembly depends on canonical N-glycan biosynthetic machinery and results in structures enriched in sialic acid and fucose. Analysis of living cells revealed that the majority of glycoRNAs were present on the cell surface and can interact with anti-dsRNA antibodies and members of the Siglec receptor family. New insights into the molecular nature of glycoRNAs will be presented, which help to solidify the existence of a direct interface between RNA biology and glycobiology, and an expanded role for RNA in extracellular biology.
BIO:
Ryan is a New Jersey native who completed his undergraduate training at MIT, where he worked in the lab of Phillip Sharp on small noncoding RNA biology. Subsequently, he moved to Stanford where he completed his M.D. and Ph.D. with Howard Chang developing methods to study RNA-protein interactions. From this work, he won the Weintraub Graduate Student Award. As a post-doc, he changed fields to learn both chemistry and glycobiology with Carolyn Bertozzi at Stanford as a Damon Runyon Cancer Research Postdoctoral Fellow. At the beginning of 2021, the Flynn Lab opened at Boston Children’s Hospital in the Stem Cell Program and the Department of Stem Cell and Regenerative Biology at Harvard University. The Flynn Lab is currently focused on advancing methods and mechanisms surrounding the glycoRNA molecule, which operates at the interface of RNA biology and the secretory pathway.

Distinguished Seminar - David Horsley
2022-11-17 13:30 - 14:40 | 136 ISEC, 360 Huntington Ave, 136 ISEC, Boston, MA, 02115, United States
“Systems Based on Ultrasonic MEMS: Commercialization and Future Directions “
David Horsley
Professor, University of California, Davis Adjunct Professor, University of California, Berkeley

Abstract
The increasing maturity of thin-film piezoelectric materials and the MEMS manufacturing ecosystem has enabled the rapid development of sensor systems based on piezoelectric micromachined ultrasonic transducers (PMUTs). In this talk, I will describe work by my research group over the last decade to develop and commercialize PMUT-based systems for consumer electronics applications, starting with air-coupled PMUTs used for time-of-flight (ToF) range-finding and human presence sensing. These ToF sensors were commercialized by my startup, Chirp Microsystems (now part of TDK), and are used today in various products such as smart-locks, robot vacuum cleaners, and laptops. We subsequently developed an ultrasonic fingerprint sensor based on the monolithic integration of PMUTs with CMOS that is used for biometric authentication in consumer products today. A common feature of the ToF sensor and the fingerprint sensor is that they are systems that combine MEMS, integrated circuits, and algorithms. The ability to realize a complete ultrasonic system on chip (SoC) opens new research opportunities in areas such as portable medical imaging systems for point-of care ultrasound (POCUS) as well as wearable ultrasonic devices.
Biography
David A. Horsley received his PhD in Mechanical Engineering from the University of California, Berkeley, in 1998. He is a Professor of Mechanical and Aerospace Engineering at the University of California, Davis, and an Adjunct Professor of Mechanical Engineering at the University of California, Berkeley, where he is co-director of the Berkeley Sensor and Actuator Center (BSAC). He is also co-founder and CTO of Chirp Microsystems Inc. (a TDK Group Company), a manufacturer of ultrasonic sensors using MEMS technology. Dr. Horsley was Co-Chair of the 2016 IEEE Sensors Conference, Co-Chair of the 2017 Transducers Research Foundation Napa Microsystems Workshop, and Co-Chair of the 2020 IEEE MEMS Conference. Dr. Horsley is an IEEE Fellow, a Fellow of the National Academy of Inventors, is a recipient of the National Science Foundation’s CAREER Award, the UC Davis Outstanding Junior Faculty Award, the 2016 NSF I/UCRC Association’s Schwarzkopf Award for Technological Innovation, and the 2018 East Bay Innovation Award. He has authored or co-authored over 200 scientific papers and holds over 30 patents.

PhD Dissertation Defense Shivang Aggarwal
2022-11-18 11:00 - 12:00
Location: ISEC 332
“Towards Reliable, High Capacity mmWave Wireless LANs for Mobile Devices”
Abstract:
The IEEE 802.11ad standard, with its 14 GHz of unlicensed spectrum around 60 GHz, is touted as one of the key technologies for building the next generation of WLANs that will enable high throughput demanding mobile applications. However, there have been serious concerns regarding the susceptibility of mmWave links to mobility and blockage as well as smartphone energy consumption at Gigabit scale data rates.
In this dissertation, first, through extensive measurement campaigns with commercial off-the-shelf (COTS) devices as well as a highly configurable software-defined radio (SDR) based testbed, we characterize the performance and energy efficiency of mobile devices operating in 60 GHz WLANs and identify problems that prevent wide adoption of the mmWave technology in such devices. Then, using the insights from these measurement campaigns, we design solutions to tackle these problems and prototype them for real-world evaluation.
This dissertation makes the following contributions:
(i) We extensively study the performance and power consumption of 802.11ad on commercial smartphones. We focus on the specific aspects affected by unique smartphone features, e.g., antenna placement or user mobility patterns, and compare the performance against that achieved by 802.11ad laptops in previous studies. We also compare 802.11ad against its main competitors 802.11ac and 802.11ax. Overall, our results show that 802.11ad is better able to address the needs of emerging bandwidth-intensive applications in smartphones than its 5 GHz counterparts. At the same time, we identify several key research directions towards realizing its full potential.
(ii) We extensively study the two main link adaptation mechanisms in 802.11ad, rate adaptation (RA) and beamforming. We undertake a large measurement campaign using an SDR-based testbed giving us complete access to the PHY and MAC layers. We look at the two link adaptation mechanisms separately and study the effectiveness of a few RA and beamforming heuristics. Further, look at the interaction between the two link adaptation mechanisms, specifically, which mechanism should be triggered when and in what order. We design a practical, standard-compliant link adaptation framework that leverages ML and PHY layer information to determine when to trigger link adaptation and which adaptation mechanism to use.
(iii) To address the issues with mmWave link reliability, we explore the use of multiple frequency bands to couple the performance of 802.11ad with the reliability of legacy WiFi. To accomplish this, we develop a Multipath TCP (MPTCP) scheduler to efficiently use both interfaces simultaneously in order to achieve a higher overall throughput as well as seamlessly switch to a single interface when the other one fails. Further, we port MPTCP to a dual-band (5 GHz/60 GHz) smartphone, evaluate its power consumption, and provide recommendations towards the design of an energy-aware MPTCP scheduler.
(iv) To enable high user QoE, and maintain that in the face of ever-changing network conditions, applications such as virtual reality (VR) and video streaming perform quality adaptation. A key component of quality adaptation is throughput prediction. Thus, we extensively study the predictability of the network throughput of an 802.11ad WLAN in downloading data to an 802.11ad- enabled mobile device under varying mobility patterns and orientations of the mobile device.
(v) With a dramatic increase in throughput requirements of applications and AP-user density in the near future, multi-user multi-stream communication in the 60 GHz band is required. To this end, the IEEE 802.11ay standard, successor to the current 802.11ad standard, includes support for simultaneous transmission over multiple data streams. Using an SDR-based testbed, we extensively study the performance of SU- and MU-MIMO in 60 GHz WLANs in multiple environments, analyze the performance in each environment, identify the factors that affect it, and compare it against the performance of SISO. Finally, we propose two heuristics that perform both beam and user selection with low overhead while outperforming previously proposed approaches
Committee:
Prof. Dimitrios Koutsonikolas (Advisor)
Prof. Kaushik Chowdhury
Prof. Tommaso Melodia

COE FacDev Friday: Managing Your Finances and How COE can Help
2022-11-18 13:00 - 14:00

Interdisciplinary Women's Collaborative Hackathon
2022-11-19 08:00 - 22:00 | Curry Student Center, 360 Huntington Ave., Boston, MA, 02115, United States
The IWC will be hosting our first annual Hackathon! This event will take place on November 19th from 8am-10pm in the Curry Student Center. It is open to women and non-binary undergraduate and graduate students and we welcome all experience levels. There will be corporate sponsors, free swag, and food is provided. All members from a team must sign up, even if the team is tentative. Please sign up if this is of interest, and we will be in touch with teams in early November.
Sign up here

Research Initiation: Analyzing inequities in undergraduate workforce opportunities between biomedical and other engineering disciplines
2022-11-21 12:00 - 13:00
Monday, November 21st at 12pm (VIRTUAL)

Please Register:
https://bit.ly/3MxqA0u
All attendees receive a FREE BOOK!
Abstract: Biomedical Engineering majors have been shown to exhibit higher rates of transfer to different engineering majors, lower rates of internship and career employment offers, and lower average starting salary compared to other engineering majors. These inequities in undergraduate workforce opportunities are having adverse effects on the professional formation of Biomedical Engineers. This NSF study seeks to initiate a characterization of the challenges in the university-to-industry pipeline through investigating workforce opportunity between biomedical engineers and three other engineering majors at a large Midwestern University.
Speaker 1: Tanya Nocera
Clinical Associate Professor, Biomedical Engineering, The Ohio State University
Speaker 2: David Delaine
Assistant Professor, Engineering Education, The Ohio State University

Info Session: Dialogue in Turkey
2022-11-21 14:00 - 14:30
If you are interested in joining the Dialogue of Civilization program in Turkey (summer 1 2023), you can attend the Info Session which takes place on Mon 21st, from 2:00 pm – 2:30 pm. This event is hybrid and you can RSVP using this link.
Also, please visit the program brochure page for more information.

Mahshid Asri's Proposal Review
2022-11-22 11:00 - 12:00
“Development of Anomaly Detection and Characterization Algorithms Using Wideband Radar Image Processing for Security Applications”
Abstract:
Detection and characterization of suspicious body-worn objects is necessary for safe and effective personnel screening. In airports, developing a precise system that can distinguish threats and explosives from objects like money belt can reduce the pat-down significantly while maintaining effective security.
This work proposes two main algorithms which are developed for different millimeter-wave radar systems. The first project is a material characterization algorithm designed for a 30 GHz wideband multi bi-static radar system used for passenger screening in airports. The proposed algorithm can automatically distinguish lossless materials from lossy ones and calculate their thickness and permittivities. Starting from the radar reconstructed image showing a cross-section of the body, we extract the nominal body contour using Fourier series, separate body and object responses, categorize the object as lossy or lossless based on the depression and protrusion of the body contour, and finally predict possible values for the object’s permittivity and thickness. Our resulting classification is good, implying fewer nuisance alarms at check points. The second project is a metal detection algorithm designed to monitor pedestrians walking along a sidewalk for large, concealed metallic objects. Finite Difference Frequency Domain and SAR algorithms are used to simulate the images produced by this 6 GHz wideband radar system.
Committee:
Prof. Carey Rappaport (Advisor)
Prof. Charles DiMarzio
Prof. Edwin Marengo

Can Qin's PhD Proposal Review
2022-11-28 10:00 - 11:00
“Transfer Learning across Domains, Tasks and Models”
Abstract:
The big data stands as a cornerstone of deep learning, which has significantly improved a wide range of machine learning and computer vision tasks. Despite such a great success, data collection is time-consuming and costly, considering manual efforts and privacy restrictions. Transfer learning is a promising direction toward data-efficient AI by leveraging acquired data and pre-trained models as guidance. This dissertation focus on the feature and model transfer across different domains and tasks, which can be roughly summarized into three sections. (1) Section One focuses on Unsupervised Domain Adaptation (UDA) without any labels in the target domain. The technical challenge of UDA is the distribution mismatch across domains. I have presented a hierarchical alignment model as the solution. (2) Section Two extends UDA into semi-supervised domain adaptation (SSDA) with minimal target-domain labels, which is useful and effortless to acquire. To avoid overfitting toward labeled data, I have proposed structural regularization verified on different classification benchmarks. (3) Section Three mainly explores the model transfer, including teacher-student knowledge distillation and heterogeneous models infusion with a high potential for model compression and enhancement.
Committee:
Prof. Raymond Fu (Advisor)
Prof. Octavia Camps
Prof. Huaizu Jiang

Xuanyi Zhao's PhD Proposal Review
2022-11-28 12:00 - 14:00
“AlN/AlScN based Micro Acoustic Metamaterials for Radio Frequency Applications of the Next Generations”
Abstract:
In the last two decades‚ micro-acoustic resonators (μARs) have played a key role in integrated 1G-to-4G radios‚ providing the technological means to achieve compact radio frequency (RF) filters with low loss and moderate fractional bandwidths (BW<4%). More specifically‚ Aluminum Nitride (AlN) based filters have populated the front-end of most commercial mobile transceivers due to the good dielectric‚ piezoelectric and thermal properties exhibited by AlN thin-films and because their fabrication process is compatible with the one used for any Complementary Metal Oxide Semiconductor (CMOS) integrated circuits (ICs). Nevertheless‚ the rapid growth of 5G and the abrupt technological leap expected with the development of sixth-generation (6G) communication systems are expected to severely complicate the design of future radio front-ends by demanding Super-High-Frequency (SHF) filtering components with much larger fractional bandwidths than achievable today. In the meantime, as more acoustic filters replying on bulk waves which requests the devices to be physically-suspended to operate, thermal related nonlinearity has been a challenge which requests new designs to enhance the thermal linearity thus power handling for these acoustic components. Even more‚ the recent invention of on-chip nonreciprocal components‚ like the circulators and isolators recently built in slightly different CMOS technologies‚ has provided concrete means to double the spectral efficiency of current radios by enabling the adoption of full-duplex communication schemes. Nevertheless‚ for such schemes to be really usable in wireless systems‚ self-interference cancellation networks including wideband‚ low-loss and large group delay lines are needed. Yet‚ the current on-chip delay lines that are also manufacturable through CMOS processes‚ which rely on the piezoelectric excitation of Surface Acoustic Waves (SAWs) or Lamb Waves in piezoelectric thin films‚ have their bandwidth and insertion-loss severely limited by the relatively low electromechanical coupling coefficient exhibited by their input and output transducers. As a results‚ these components are hardly usable to form any desired self-interference cancelation networks. In order to overcome these challenges‚ only recently‚ new classes of microacoustic resonators and delay lines exploiting the high piezoelectric coefficient of Aluminum Scandium Nitride (AlScN) thin films and the exotic dispersive features of acoustic metamaterials (AMs) have been emerging. These devices rely on forests of locally resonant piezoelectric rods to generate unique modal distributions‚ as well as unconventional wave propagation features that cannot be found in conventional SAW and Lamb wave counterparts. In this presentation‚ the design‚ fabrication and performance of the first microacoustic metamaterials (μAMs) based resonators and delay lines will be showcased. Moreover, AMs based reflectors are invented and demonstrated providing new improving the linearity and power handling of the AlScN μARs. In addition to reviewing the current status of our work, we will propose several further explorations of using our AlN/AlScN based AMs in RF applications of the next generations.
Committee:
Prof. Cristian Cassella (advisor)
Prof. Matteo Rinaldi
Dr. Jeronimo Segovia-Fernandez

Research Presentations On Bendable Electronics and Sustainable Technologies (BEST)
2022-11-29 10:00 - 13:00 | 442 Dana, 360 Huntington Ave, 442 DA, Boston, MA, 02115, United States
Professor Ravinder Dahiya will be joining Northeastern’s ECE Department on January 2023. Please join us for an interactive mini-symposium featuring projects from the BEST Lab directed by Professor Dahiya.

The presenters are:
Dr. Dhayalan Shakthivel, Research Associate, Inorganic Nanowires for Flexible and Large Area Electronics
Dr. Gaurav Khandelwal, Post-doc, Functional Materials based Triboelectric Nanogenerators for Selfpowered Sensors and Systems
Dr. Fengyuan Liu, Post-doc, “Hebbian-like” learning in electronic skin
Dr. Abhishek S. Dahiya, Research Associate, Towards energy autonomous electronic skin using sustainable technologies
Ayoub Zumeit, PhD candidate, Inorganic nanostructures-based high-performance flexible electronics
Adamos Christou, PhD candidate, Novel Technologies for High-Performance Printed Electronics
Radu Chirila, PhD candidate, Electronic Skin and Holographic Systems for Socially Intelligent Robots
João Neto, PhD candidate, Hardware building for neuromorphic electronic skin
Luca De Pamphilis, PhD candidate, Nanowire-based electronic layers for flexible neuromorphic devices
Make sure to RSVP & specify inperson or virtual attendance. See you soon!

Prof. Hui Guan - "Towards accurate and efficient edge computing via multi-task learning "
2022-11-29 14:00 - 15:30 | 442 Dana, 360 Huntington Ave, 442 DA, Boston, MA, 02115, United States
“Towards accurate and efficient edge computing via multi-task learning ”

Abstract:

AI-powered applications increasingly adopt Deep Neural Networks (DNNs) for solving many prediction tasks, leading to more than one DNNs running on resource-constrained devices. Supporting many models simultaneously on a device is challenging due to the linearly increased computation, energy, and storage costs. An effective approach to address the problem is multi-task learning (MTL) where a set of tasks are learned jointly to allow some parameter sharing among tasks. MTL creates multi-task models based on common DNN architectures and has shown significantly reduced inference costs and improved generalization performance in many machine learning applications. In this talk, we will introduce our recent efforts on leveraging MTL to improve accuracy and efficiency for edge computing. The talk will introduce multi-task architecture design systems that can automatically identify resource-efficient multi-task models with low inference costs and high task accuracy.

Bio:

Hui Guan is an Assistant Professor in the College of Information and Computer Sciences (CICS) at the University of Massachusetts Amherst, the flagship campus of the UMass system. She received her Ph.D. in Electrical Engineering from North Carolina State University in 2020. Her research lies in the intersection between machine learning and systems, with an emphasis on improving the speed, scalability, and reliability of machine learning through innovations in algorithms and programming systems. Her current research focuses on both algorithm and system optimizations of deep multi-task learning and graph machine learning.

Summer 1, 2023 Panama DOC: International Applications of Fluid Mechanics – Info Session
2022-11-29 18:00 - 19:00
If you are interested in learning fluid mechanics through relevant examples in an international setting in a Dialogue Of Civilization (DOC) program this summer in Panama, please join the Zoom Info Session on Tuesday, November 29th at 6:00 pm. By participating in this program, you will gain an international perspective on the real-life applications of fluid mechanics, while learning about the culture and history of this burgeoning and diverse Latin America country. This program will take place in Summer 1, 2023 and will include travel to 3 relevant engineering projects (including the Panama Canal) in different locations in Panama. Two courses are offered under this program:

ME 3480 – International Applications of Fluid Mechanics (4SH; equivalent to ME 3475, ME degree core course requirement)
Studies fundamental principles in fluid mechanics in an international setting. Students have an opportunity to travel to a foreign locale to develop theoretical understanding while experiencing the issues that affect applications of fluids engineering in a culture and environment different from their own. Topics include hydrostatics (pressure distribution, forces on submerged surfaces, and buoyancy); Newton’s law of viscosity; dimensional analysis; integral forms of basic laws (conservation of mass, momentum, and energy); pipe flow analysis; differential formulation of basic laws including Navier-Stokes equations; and the concept of boundary layer and drag coefficient.

ME 4699 – Special Topics in Mechanical Engineering: Fluid Mechanics Engineering Analysis within the Socio-Cultural, Political and Economic History of Panama (4SH)
This course is designed for college undergraduate students who are interested in addressing and analyzing fluid mechanics related engineering problems and solutions in the context of the traditions, cultures, and socioeconomic and political history of Panama, seeking to obtain a solid grasp on the historical developments of the country and their effects on contemporary fluid mechanics engineering projects and issues.

The courses and program will be taught and run by Prof. Carlos Hidrovo Chavez.
Please visit the program website for more information.

Engineering Elastin-like Peptides to Control Solid Surface Properties: A Biomaterials Research Platform and Education Tool
2022-11-30 12:00 - 13:00 | 236 Richards, 360 Huntington Ave, Boston, MA, 02115, United States
ChE Seminar Series Presents: Dr. Julie N. Renner
Associate Professor, Department of Chemical Engineering, Case Western Reserve University
Abstract: Key biomaterials applications (e.g., bioelectronics, drug delivery, and tissue engineering) rely on control of solid surface properties for success. Surface-bound peptide monolayers are a promising way to control surface properties because peptides are biocompatible, easily tunable, can be stimuli-responsive, and possess specific secondary structures and binding capabilities. Our work focuses on enabling surface-bound peptide monolayers as a means of precisely engineering surfaces for biomaterials applications by understanding their assembly and sequence-driven properties. Specifically, we are establishing new engineering models to control and understand the behavior of surface-bound elastin-like peptides. We use a quartz crystal microbalance with dissipation to provide detailed information about the binding behavior of the peptides under various conditions, including in an electric field. We also use techniques such as Fourier-transform infrared spectroscopy, atomic force microscopy, and cyclic voltammetry to further probe our materials. These techniques combined with traditional peptide analysis tools show that 1) the coverage of surface-bound elastin-like peptides can be predicted with a simple linear model based on mass loading and hydrophobicity and 2) control of peptide orientation can be achieved using a combination of electric field and peptide chemistry which results in the ability to dictate surface morphology, loading and properties. In addition, we demonstrate that biomolecular engineering is an excellent platform for service learning which engages East Cleveland high school students, as well as CWRU undergraduate and graduate students in way that significantly increases their self-efficacy in science and engineering. Generally, our results demonstrate that engineered surface-bound peptides are promising tools for biomaterials design and excellent education tools for helping to achieve a more diverse STEM workforce.
Bio: Dr. Julie N. Renner is a Climo Associate Professor at Case Western Reserve University. Her group has multiple projects developing biomolecular platforms to control solid-liquid interfaces including projects in nutrient recycling technology, resource recovery, antifouling, and biomaterials. Her work has been recognized by the National Science Foundation (NSF) CAREER award, an Electrochemical Society Toyota Young Investigator Fellowship, and the Case School of Engineering Research Award. In addition, her efforts in the classroom have received the Case School of Engineering Undergraduate and Graduate Teaching Awards. Prior to becoming a professor, Dr. Renner worked in a broad range of research areas. She spent four years conducting industrial research at Proton OnSite (now Nel Hydrogen), a world-leader in hydrogen generation via proton exchange membrane electrolysis. She completed her thesis as an NSF Graduate Research Fellow at the Purdue School of Chemical Engineering, where she specialized in designing, creating, and characterizing novel polypeptide materials for tissue engineering applications. She earned her bachelor’s degree in chemical engineering from the University of North Dakota where she worked on environmental remediation projects.

DOC info session: Sustainable Waste Management in Cagliari, ITALY
2022-11-30 17:00 - 18:00
Learn about the Summer 1 Dialogue of Civilizations on Sustainable Waste Management in Cagliari, ITALY
Join Zoom Meeting
Meeting ID: 960 2359 0759
One tap mobile
+19292056099,,96023590759# US (New York)
+13017158592,,96023590759# US (Washington DC)
OR
Join by Skype for Business

Postgrad Virtual Fair - Africa & Middle East
2022-12-01 10:00 - 14:00
Join the Graduate Admissions team at The Student World’s Virtual Postgrad Fair! Talk with an admissions representative and learn more about our graduate engineering programs.
Registration and event information may be found at the website below. We look forward to seeing you there!

Public Policy for Healthcare Cyber Security: Fireside Chat with Senator Mark Warner
2022-12-01 15:00 - 16:00
Zoom Registration Required: https://tinyurl.com/archimedes-warner
From hospital floors to medical device manufacturing factory floors, health care cybersecurity faces daunting challenges including unpatchable legacy medical devices, ransomware disrupting patient care, privacy breaches of patient records, cybersecurity workforce development, and basic cyber hygiene. U.S. Senator Mark Warner recently published a paper, “Cybersecurity is patient safety: policy options in the health care sector.” Join a conversation with Senator Mark Warner and moderator Prof. Kevin Fu, PhD, of the Archimedes Center for Health Care and Medical Device Security presently at the University of Michigan.
Senator Mark Warner was elected to the U.S. Senate in November 2008 and reelected to a third term in November 2020. He serves on the Senate Finance, Banking, Budget, and Rules Committees as well as the Select Committee on Intelligence, where he is the Chairman. He has a history of crafting legislation that addresses the cybersecurity challenges facing our nation. From 2002 to 2006, he served as Governor of Virginia. The first in his family to graduate from college, Mark Warner spent 20 years as a successful technology and business leader in Virginia before entering public office. An early investor in the cellular telephone business, he co-founded the company that became Nextel and invested in hundreds of start-up technology companies that created tens of thousands of jobs. Senator Warner and his wife Lisa Collis live in Alexandria, Virginia. They have three daughters.
Prof. Kevin Fu, PhD, founded the field of medical device security, served as the nation’s first Acting Director of Medical Device Cybersecurity at the U.S. Food and Drug Administration, and directs the Archimedes Center for Health Care and Medical Device Security at Michigan. Archimedes carries out higher education and academic research to protect Operational Technology (OT) cybersecurity of medical devices, health care delivery, and pharmaceutical factory floors. In 2023, Prof. Kevin Fu and Archimedes join Northeastern University in Electrical & Computer Engineering in the College of Engineering and the Khoury College of Computer Sciences.

First Year Engineering Expo
2022-12-02 08:00 - 17:00 | Curry Student Center, 360 Huntington Ave., Boston, MA, 02115, United States
Please come to the Curry Student Center indoor quad and pit on Friday, December 2nd to see Northeastern’s First-Year Engineering Students’ inventive projects, games, and exhibits.
Students will showcase original board games, interactive projects geared to teach children sustainability concepts, and prolific prototypes to help solve a wide range of problems.
Each project applies the engineering concepts introduced this past semester, which includes the Engineering Design Process, Solidworks, AutoCAD, Programming with C++ and Matlab, and controlling microelectronics with Arduino.

Ramtin Khalili's PhD Dissertation Defense
2022-12-05 10:00 - 11:00
Abstract:
State estimation is a critical application in energy management systems. Due to the increased penetration of inverter-based resources, installed advanced infrastructure at all voltage levels, and unconventional loads like electric vehicle charging stations, a three-phase state estimator formulation is essential. The first issue is the convoluted formulation and modeling techniques that are required in three-phase systems studies. Moreover, the size of network matrices expanded, which makes the analysis computationally costly. This dissertation addresses this by proposing a new decoupled state estimation method. The idea is to exploit the linearity of measurement equations, decompose the three-phase coupled equations into three independent modal measurement equations, perform the state estimation independently for each mode, and finally reconstruct the three-phase quantities. This method is applicable to both radial and meshed three-phase networks. Furthermore, multi-phase structures can be handled by the new estimator, which makes the approach practical when monitoring mixed-phase feeder sections is of interest.
While utilities are investing in expanding the grid and installing more PMUs, there might not be enough PMUs to make the network observable in all networks, especially at lower voltage levels. So, PMU-based linear state estimators are not always feasible. On the other hand, SCADA measurements are available with adequate redundancy in most networks. However, SCADA-based state estimation is nonlinear, which brings various problems like divergence issues and significant CPU times. The computational complexity will be even worse if the three-phase state estimation is formulated based on SCADA measurements due to their nonlinear nature, which makes modal decoupling impossible. So, a new linear formulation has been proposed for both the positive-sequence and three-phase networks based on conventional measurements. This approach converts the nonlinear recursive problem into an iterative linear state estimation problem.
The inherent assumption in most of the state estimators is a perfect network model. However, network parameter errors are susceptible to errors that can bias the state estimation solution. This can deceive the existing bad data tools as parameter errors appear as if multiple interacting measurement errors occur locally. So, a two-stage method is proposed for parameter error identification and correction for large three-phase networks. A systematic PMU placement strategy is also proposed to ensure the detectability of parameter errors. The benefits of multi-area state estimation are demonstrated for the deregulated power grids for monitoring the local and boundary areas. It has also shown promising results in increasing the efficiency of state estimation using a distributed framework. Parameter and measurement errors can remain undetected as a result of weakened measurement redundancy on the boundaries. However, boundary errors in the area boundaries will be detected due to measurement consolidation at the coordination level.
Committee:
Prof. Ali Abur (Advisor)
Prof. Bahram Shafai
Prof. Mahshid Amirabadi

Dialogues of Civilization: Biomedical Imaging in Chile
2022-12-05 18:00 - 19:00
If you are interested in a Summer 1 Dialogue on Biomedical imaging and Chilean Culture in Santiago, there will be an info session on Monday 5 December, at 6pm in 302 Stearns, or on Zoom at
https://northeastern.zoom.us/j/99185015932
Meeting ID: 991 8501 5932

Enabling Engineering Fall Showcase
2022-12-06 13:30 - 15:30 | 002 Ell Hall, 360 Huntington Ave, Boston, MA, 02115, United States
Please come to the Enabling Engineering Fall Showcase on Tuesday, December 6th, 1:30 -3:30pm ET in 002 Ell Hall where students will present their design projects.
Enabling Engineering is a Northeastern University student group that designs and builds devices to empower individuals with physical and cognitive disabilities. Our students collaborate with clients on projects that provide greater independence, reduce medical burdens, and increase social connectedness. We help family members, clinicians, and teachers care for people with disabilities. By giving students the opportunity to participate in Enabling Engineering projects, we are training the next generation of engineers to be knowledgeable about, and aware of, the needs of individuals with disabilities.

Md Navid Akbar's PhD Dissertation Defense
2022-12-06 16:00 - 17:30
“Inference from Brain Imaging: Incorporating Domain Knowledge and Latent Space Modeling”
Abstract:

Brain imaging can probe the anatomy (structural) of our brain, or its function (functional). A particular imaging modality (unimodal) generally provides only a particular insight into human health. Transcranial magnetic stimulation (TMS), though still in its infancy as a brain imaging modality, is such a functional, unimodal technique. TMS helps model human motor-cortical mapping, using corresponding muscle activity captured by surface electromyography (EMG), but it necessitates a reliable data-driven model. Earlier works have modeled the causal direction only (from cortical representation to muscles), or the inverse direction (from muscles to cortical representation), with simple statistical regression. We modeled this motor-cortical mapping bi-directionally in this dissertation, using deep learning. We first modeled TMS-induced 3D electric field (E-field) in a brain to causal multi-muscle activation picked up by EMG, in a regression task using a convolutional neural network (CNN) autoencoder. By fusing neuroscience domain knowledge (e.g., an empirical neural response profile), we reduced 14% squared error, compared to the baseline model that did not contain this. We then designed our novel inverse imaging CNN model, to reconstruct physiologically meaningful E-field distributions (in the image domain) from a given set of muscle activations (in the sensor domain). By adopting variational inference in the CNN model, to learn the underlying latent space better, we were able to reduce 13% in squared error over our purely CNN baseline.
Diagnosis with brain imaging is often incomplete with a unimodal technique, and having multiple sources (multimodal) may be advantageous. Successful multimodal fusion can provide more holistic information, compared to its constituents. One relevant example is the classification of late post-traumatic seizure (LPTS). Previous works in this space have tackled LPTS classification with either unimodal functional imaging, or non-machine learning (ML) structural modeling. In this dissertation, we first undertook the ML classification of binary LPTS: with unimodal, structural brain imaging, namely diffusion magnetic resonance imaging (dMRI). By incorporating interpretable domain knowledge (post-traumatic lesion volume compensation), we improved 7% in the mean area under the curve (AUC) over the standard technique in literature. Finally, we classified LPTS for a larger sample of subjects, utilizing multimodal imaging, including functional MRI (fMRI) and electroencephalography (EEG). Following unsupervised imputation for any missing modality within the subjects, we introduced our novel multimodal fusion algorithm, which attempts to leverage the underlying structure of the multivariate information. We found that our proposed algorithm improved by 7% in AUC performance, over a naive Bayesian estimator that can handle missing data intrinsically.
Collectively, the work presented here demonstrated that incorporating domain knowledge in the modeling pipeline successfully improved inference. Similar improvements were also observed by learning and leveraging the possible underlying latent structure of the given information, and adapting the models accordingly.

Committee:

Prof. Deniz Erdogmus (Advisor)
Prof. Mathew Yarossi (Co-advisor)
Prof. Dominique Duncan
Prof. Sarah Ostadabbas

Offshore Wind Tech Week Network Reception
2022-12-06 17:00 - 19:00 | Alumni Center, 716 Columbus Ave, 6th Floor, Boston, MA, 02120, United States
This event will celebrate #OffshoreWindTechWeek – comprised of the National Offshore Wind R&D Symposium December 5 & 6 and the International Offshore Wind Technical Conference (IOWTC) December 7 & 8.
This networking reception will close out the 2022 Symposium and introduce the IOWTC, happening the following days on December 7 & 8 at Northeastern University. All offshore wind industry professionals are welcome to attend, regardless of whether they are attending Symposium or IOWTC.
*Note – All in-person NOWRDC Symposium registrants are automatically registered for this networking reception.
Location: Alumni Center at Northeastern University, 716 Columbus Ave, Boston, MA 02120
Register

The "Finals Cookie" with Dean Abowd
2022-12-06 20:30 - 22:00
Please join us for a relaxing evening before finals begin. We’ll provide hot chocolate and LOTS of cookies.
Hosted by the College of Engineering
Tuesday December 6th from 8:30 to 10:00 pm
The Tents at Robinson Quad

How green hydrogen is made
2022-12-07 12:00 - 13:00 | 236 Richards, 360 Huntington Ave, Boston, MA, 02115, United States
ChE Seminar Series Presents:
Marc T.M. Koper
Leiden Institute of Chemistry
Leiden University, Leiden, The Netherlands
Abstract:
The electrocatalytic production of hydrogen through water splitting is a necessary approach for storing (excess) renewable electricity as chemical energy in fuels, and for making green hydrogen as a building block for the chemical industry. Here, I will discuss recent advances and challenges in the mechanistic understanding of electrochemical H2 formation. Specifically, I will show that H2O activation is influenced by an intricate interplay between surface structure (both on the nano- and on the mesoscale), electrolyte effects (pH, ion effects) and mass transport conditions. This complex interplay is currently still far from being completely understood.
Bio:
Marc Koper is Professor of Surface Chemistry and Catalysis at Leiden University, The Netherlands. He received his PhD degree (1994) from Utrecht University (The Netherlands) with a thesis on nonlinear dynamics and oscillations in electrochemistry. He was an EU Marie Curie postdoctoral fellow at the University of Ulm (Germany) and a Fellow of Royal Netherlands Academy of Arts and Sciences (KNAW) at Eindhoven University of Technology, before moving to Leiden University in 2005. His research in Leiden focuses on fundamental aspects of electrocatalysis, theoretical and computational electrochemistry, and electrochemical surface science, in relation to renewable energy and chemistry. He has received various national and international awards, among which the Spinoza Prize of the Netherlands Organization for Scientific Research (2021), Allen J. Bard Award for Electrochemical Science of The Electrochemical Society (2020), the Netherlands Catalysis and Chemistry Award (2019), and the Faraday Medal (2017) from the Royal Society of Chemistry. He is currently President of the International Society of Electrochemistry.

12/7 IER Seminar Series: Steve Dorton - "Trust Dynamics with AI in High-Consequence Work Systems"
2022-12-07 13:00 - 14:00 | 532 ISEC, 360 Huntington Ave, Boston, MA, 02115, United States
Trust Dynamics with AI in High-Consequence Work Systems
Wednesday, 12/7/2022 from 1 – 2 pm
ISEC 532 & Zoom
Zoom: https://northeastern.zoom.us/j/96750528112?pwd=[REDACTED]
Steve Dorton
Principal Scientist for Sensemaking, Decision Making, and AI
The MITRE Corporation
Abstract:
Artificial Intelligence (AI) is often viewed as the means by which intelligence analysts will cope with the ever-increasing deluge of data from various sources. The best AI is moot, however, if analysts cannot trust the outputs of the AI to inform high-consequence decision making. A naturalistic study was performed to understand how intelligence professionals gain and lose trust in AI “in the wild.” The study assessed various trust factors proposed in the literature and identified various themes from interviews with intelligence professionals. We will discuss how to apply these findings to engineer more trustworthy AI for high-consequence decision applications.
Bio:
Steve Dorton is a Principal Scientist for Sensemaking, Decision Making, and AI at the MITRE Corporation. His research generally falls at the intersection of the social and computational sciences, focusing on how intelligent systems can help and harm human cognition in national security contexts. He also holds an adjunct lecturer appointment in the University of Maryland School of Public Policy, where he teaches social, ethical, and policy considerations for AI and big data.

Forge Fall Showcase
2022-12-07 18:30 - 20:30 | 010 Hayden Hall, 010 Hayden Hall, 360 Huntington Ave, Boston, MA, 02115, United States
Come to Forge’s Fall Showcase to learn all about the amazing work that our Product Lab teams have completed this semester.
Forge a student-led initiative of the Sherman Center to help students learn about product development and entrepreneurship through hands-on project experience and tailored workshops.
As a part of Forge, students solve problems in our community by developing solutions that make a lasting impact and develop skills through an engaging workshop and speaker series.
This semester, our Product Lab teams have completed amazing projects all centered around the theme of musical exploration, while learning transferrable skills within the field of entrepreneurial engineering.
We look forward to seeing everyone there!

Danlin Jia's PhD Dissertation Defense
2022-12-08 11:00 - 12:00
“Towards Performance and Cost-efficiency for Data-intensive Applications in Distributed Data Processing Systems”
Abstract:
Data-intensive science (DIS) has experienced a significant boom in the past decade. The emerging technologies of data-intensive services and infrastructures contribute to DIS’s development and raise challenges. An ecosystem has been constructed considering performance, scalability, sustainability, and reliability to provide a high-quality service to DIS applications. The ecosystem consists of services exposed to users for application deployment and infrastructures to support data storage, transfer, and management from the system’s perspective. DIS applications share typical features, such as memory and I/O intensity. Thus, addressing the bottlenecks triggered by memory-intensive or I/O-intensive workloads in services and infrastructures is essential to improve the performance and cost-efficiency of the whole ecosystem. In this dissertation, we investigate the characteristics of various DIS applications and design new resource allocation and scheduling schemes for the services and infrastructures in the DIS ecosystem.
We first investigate memory optimization in DIS ecosystems. In-memory data analytic frameworks are proposed to cache critical intermediate data in memory instead of in storage drives. Apache Spark is a commonly adopted in-memory data analytic framework with two memory managers, Static and Unified. However, the static memory manager lacks flexibility. In contrast, the unified memory manager puts heavy pressure on the garbage collection of the Java Virtual Machine on which Spark resides. To address these issues, we propose a new learning-based bidirectional usage-bounded memory allocation scheme to support dynamic memory allocation considering both memory demands and latency introduced by garbage collection. Distributed data-processing workloads in container-based virtualization take advantage of resource sharing, fast delivery, and excellent portability of containerization but also suffer from resource competition and performance interference. This inevitably induces performance degradation and significantly long latency, even worse when over-provisioning. Motivated by this problem, we design an efficient memory allocation scheme (RITA) for containerized parallel systems to improve data processing latency. RITA monitors applications’ memory usage and cache characteristics and dynamically re-allocates memory resources.
We also propose I/O optimizations for DIS applications and infrastructures. Distributed Deep Learning (DDL) accelerates DNN training by distributing training workloads across multiple computation accelerators, e.g., GPUs. Although a surge of research has been devoted to optimizing DDL training, the impact of data loading on GPU usage and training performance has been relatively under-explored. When multiple DDL applications are deployed, the lack of a practical and efficient technique for data-loader allocation incurs GPU idleness and degrades the training throughput. In this dissertation, we thus investigate the impact of data-loading on the global training throughput and design a resource allocator that uses the data-loading rate as a knob to reduce the GPU idleness. Finally, designs and optimizations on disaggregated storage systems supported by cutting-edge storage and network techniques emerge dramatically. Disaggregated storage systems can scale resources independently and provide high-quality services for hyper-scale architectures. The traditional congestion control mechanism relieves congestion by limiting the data-sending rate of senders. However, such a design scarifies the storage drive’s performance as data are generated but stalled on storage host nodes if network congestion happens. To solve this issue, we design a storage-side rate control mechanism to mitigate network congestion while avoiding sacrificing I/O performance.
Committee:
Prof. Ningfang Mi (Advisor)
Prof. Xue Lin
Prof. David Kaeli

Chuangtang Wang's PhD Proposal Review
2022-12-08 14:00 - 16:00 | 138 ISEC, 360 Huntington Ave, 138 ISEC, Boston, MA, 02115, United States
“All-optical Control of Magnetization in Nanostructures”
Committee:
Prof. Yongmin Liu (Advisor)
Prof. Don Heiman
Prof. Nian X. Sun
Abstract:
The switching of magnetization by a femtosecond laser within several picoseconds has recently gained substantial attention, because it promises next-generation, energy-efficient, and high-rate data storage technology. One of the most intriguing demonstrations is the helicity-dependent switching (HD-AOS) of a ferromagnet, in which the magnetization states can be deterministically written and erased using left- and right-circularly polarized light. However, the challenge is to realize a single-pulse HD-AOS. Controlling the spin angular momentum transfer from light to magnetic materials in nanostructures is the key to advance this field.
In my thesis research work, I will study the all-optical control of magnetization in different nanostructures, aiming to better understand the underlying mechanisms of HD-AOD and accelerate the technology development. Firstly, helicity-driven magnetization dynamics in heavy metal/ferromagnet Au(Pt)/Co bilayer by the optical spin transfer torque (OSTT) is experimentally explored. The wavelength-dependent measurement of OSTT reveals that the quantum efficiency of OSTT strongly depends on the interface electronic structure and pump energy. The Inverse Faraday effect (IFE), which is believed to be the driving mechanism of HD-AOS, is subsequently investigated in an Au thin film. The dependence of IFE on photon energy implies that the orbital angular momentum contribution to IFE is dominated by the excitation of laser pulses. To the best of our knowledge, it is the first demonstration of this phenomenon. Lastly, I will discuss our recent results on plasmonics-enhanced all-optical control of magnetization. Light can be tightly confined in plasmonic structures, which can potentially enable low-energy and high-density magnetic data storage.

The Quantum Age: From Bell Pairs to Quantum Computers
2022-12-08 16:00 - 17:00 | 168 SN, 360 Huntington Ave, Boston, MA, 02115, United States
Nobel Physics Colloquium 12.8 @4pm
Every year, the Physics Department celebrates the Nobel Prize in Physics by inviting a renowned expert in the field of the awardees to introduce the ideas and advances that lead to their nomination. This year, we are fortunate to host Prof. Vladan Vuletić, from MIT, and expert in quantum optics and emergent quantum technologies such as quantum computers.
Speaker: Prof. Vladan Vuletić, MIT
Title: The Quantum Age: From Bell Pairs to Quantum Computers
Abstract: Quantum mechanics has not one but two mysteries: the double-slit experiment and quantum correlations (entanglement) between two or more particles. Criticized by Einstein as “spooky action at a distance”, entanglement is now seen as an essential part of the physical world, in part thanks to the recipients of the 2022 Nobel Prize. The Bell inequalities, introduced in 1964 to experimentally distinguish local hidden variable theories from quantum physics, have been confirmed to agree with quantum mechanics in the Nobel-Prize winning and many other experiments.
Building on entangled Bell pairs, the last few years have seen a remarkable development in our ability to control many neutral atoms individually, and induce controlled interactions between them on demand. This progress ushers in a new era where one can create highly entangled states of many particles, break certain limits for quantum sensors, or study quantum phase transitions. I will present results on quantum sensing enhanced by entanglement, and on quantum simulation with atomic arrays containing more than 250 atoms. Finally, I will discuss prospects for near- and medium-term neutral-atom quantum computers with full quantum error correction.
Bio: Professor V. Vuletić earned the Physics Diploma with highest honors from the Ludwig-Maximilians-Universität München, and in 1997, a Ph.D. in Physics (summa cum laude) from the same institution. While a postdoctoral researcher with the Max-Planck Institute for Quantum Optics in Garching, Germany, Professor Vuletić accepted a Lynen Fellowship at Stanford University in 1997. In 2000, he was appointed an Assistant Professor in the Department of Physics at Stanford and in June 2003 accepted an Assistant Professorship in Physics at MIT. He was promoted to Associate Professor in July 2004. He was promoted to Full Professor in July 2011.
Research Interests include laser cooling and trapping, quantum physics, quantum entanglement, quantum optics, quantum information processing. The idea of the research of the Vuletić group is to develop new methods to manipulate many-body states in a regime where the quantum mechanical aspects dominate their behavior and their properties. On the one hand, this should lead to new tools that allow one to probe physical laws and to measure fundamental constants with increasing precision. On the other hand, the progress of experimental methods also drives the advances in our understanding of the ever mysterious, beautiful, accurate, yet deeply dissatisfying structure of quantum mechanics. This interplay between theoretical concepts and experimental realizations promises to be very fertile in fields such as quantum control, quantum feedback and its limits, many-particle quantum systems, and many-particle entanglement (quantum computing). We use various methods, but most include laser-cooled atoms (to be able to keep atoms localized, and attain long coherence time) and laser-light interaction to manipulate the atoms, the photons, or both, at the quantum level. Using internal states of atoms in combination with laser light, which has essentially zero entropy, allows us to reduce thermal noise without having to cool the atoms to very low (sub-microkelvin) temperatures.
Prof. Vuletić was awarded by Lester Wolfe Career Development Chair in 2003, Alfred P. Sloan Research Fellowship in 2003-2004, and APS Fellowship “for pioneering advances across AMO physics, including quantum information and precision measurement with atomic ensembles, cavity QED, atomic collisions and Casimir forces for atom condensates near surfaces” in 2012. He is one of the founders of QuEra Computing, a Boston-based company developing quantum computers based on neutral Rydberg atoms.
168 Snell Engineering Center or Zoom
