Chemical Engineering Graduate Program Webinar
2021-03-22 10:00 - 11:00 (America/New_York)
Please join faculty, staff, and current students to learn more about graduate programs in the Chemical Engineering Department on March 22 at 10:00 AM EST.
Registration may be found at: https://us02web.zoom.us/webinar/register/WN_dhul9DvaSamlRIyBYkJtoQ
A recording will be available for those who are unable to attend.

Mechanical and Industrial Engineering Graduate Program Webinar
2021-03-23 08:30 - 21:30
Please join faculty, staff, and current students to learn more about graduate programs in the Mechanical and Industrial Department on March 23 at 8:30 EST.
Registration may be found at: https://us02web.zoom.us/webinar/register/WN_S6PynL9hS7mXCoey0EVVlg
A recording will be available for those who are unable to attend.

Civil and Environmental Engineering Graduate Program Webinar
2021-03-23 10:00 - 11:00
Please join faculty, staff, and current students to learn more about graduate programs in the Civil and Environmental Engineering Department on March 23 at 10:00 EST.
Registration may be found at: https://us02web.zoom.us/webinar/register/WN_KiuKTsiLRn2n-vmRlz2m6A
A recording will be available for those who are unable to attend.

Communication Series: RISE Poster Workshop
2021-03-23 12:00 - 13:00
Join the CommLab for a virtual interactive poster workshop to get ready for Northeastern’s Research, Innovation, and Scholarship Expo (RISE). We will help you craft your story, effectively plan for your audience, and give you tips for mindful poster design. Learn more and RSVP here. This workshop is organized and produced by the Engineering CommLab.

Crafting an Effective Elevator Pitch Workshop with the COE CommLab
2021-03-24 11:00 - 12:00
Useful during any stage of your research career, the elevator pitch is an integral part of your research dissemination toolbox. The CommLab Fellows will discuss the essential components of the elevator pitch, build the content of your pitch, and practice your pitch for a variety of situations. Five lucky participants will be eligible for a signed book written by a CommLab fellow! RSVP for this workshop through Zoom.

Women Supporting Women Panel
2021-03-24 16:00 - 17:00
Join GWiSE on Wednesday, March 24th at 4 pm for a virtual panel of women faculty sharing their experiences mentoring, and being mentored by, women. We’ll ask them about the role this support plays in shaping STEM careers.
Our panelists:

Dr. Debra Auguste, Professor, Chemical Engineering
Dr. Alessandra Di Credico, Associate Teaching Professor, Physics
Dr. Michelle Laboy, Assistant Professor of Architecture
Dr. Carla Mattos, Professor, Chemistry and Chemical Engineering

We will select 3 participants to win a $25 GrubHub gift card! (Must be a grad student to win.)
Register for the Zoom event here: https://bit.ly/wmn4wmn
Submit questions to ask the panelists here: https://bit.ly/MarchPanelQs

Information Systems, Software Engineering Design, Data Architecture + Management Graduate Programs Webinar
2021-03-26 08:30 - 09:30
Please join faculty, staff, and current students to learn more about graduate programs in Information Systems, Software Engineering Design, Data Architecture + Management on March 26 at 8:30 EST.
Registration may be found at: https://us02web.zoom.us/webinar/register/WN_5ulL1KHbRpyLZIUVys6Tzw
A recording will be available for those who are unable to attend.

ECE PhD Dissertation Defense: Mo Han
2021-03-26 12:00 - 13:00
PhD Dissertation Defense: Human Grasp Intent Inference and Multimodal Control in Prosthetic Hands
Mo Han
Location: Zoom Link
Abstract: Upper limb and hand functionality is critical to many activities of daily living and the amputation of one can lead to significant functionality loss for individuals. From this perspective, advanced prosthetic hands of the future are anticipated to benefit from improved shared control between a robotic hand and its human user, but more importantly from the improved capability to infer human intent from multimodal sensor data to provide the robotic hand perception abilities regarding the operational context. Such multimodal data may be collected from various environment sensors such as camera providing visual information, as well as easily-accessed human physiologic sensors including electromyographic (EMG) sensors. A fusion methodology for environmental state and human intent estimation can combine these sources of evidence in order to help prosthetic hand motion planning and control.
As part of a multi-disciplinary project, i.e. HANDS project, which aims at designing a robotic hand as an upper limb prosthetic device, we developed two independent prosthetic control systems (HANDS V1 and HANDS V2) integrating multimodal sources of EMG and visual evidences into the control loop. Multiple grasps required for activities of daily living can be performed by both robotic systems which were developed in a lighter and cheaper semi-autonomous manner. The HANDS V1 system was first developed to provide an easy and convenient prosthesis with a portable EMG armband and a built-in palm camera, and hereafter the HANDS V2 was constructed as an upgraded solution of HANDS V1 to achieve more difficult tasks with more identified grasp types, more EMG channels and more complicated visual information involved. Both systems depend on multimodal signals from EMG and vision, where the EMG could reflect the physiologic features related to user intents, while the robustness and adaptability to different users could be retained by the visual information relying more on surrounding environments. We collected two datasets for the initialization of each system, and the developments of the EMG-control, visual-control, and joint-control algorithms were conducted for both systems. We exploited efficient computer vision and physiological signal processing methodologies to decrease the system complexity as well as improve the user comfort, in order to provide smarter and cheaper prosthetic hands to the audience. Online experiments were executed and evaluated on both HANDS V1 and HANDS V2 systems, implemented by the Robot Operating System (ROS) system.

ECE Seminar: Sara Dean
2021-03-26 13:00 - 14:00
ECE Seminar: Reliable Machine Learning in Feedback Systems
Sara Dean
Location: Zoom Link
Abstract: Machine learning techniques have been successful for processing complex information, and thus they have the potential to play an important role in data-driven decision-making and control. However, ensuring the reliability of these methods in feedback systems remains a challenge, since classic statistical and algorithmic guarantees do not always hold. In this talk, I will provide rigorous guarantees of safety and discovery in dynamical settings relevant to robotics and recommendation systems. I take a perspective based on reachability, to specify which parts of the state space the system avoids (safety) or can be driven to (discovery). For data-driven control, we show finite-sample performance and safety guarantees which highlight relevant properties of the system to be controlled. For recommendation systems, we introduce a novel metric of discovery and show that it can be efficiently computed. In closing, I discuss how the reachability perspective can be used to design social-digital systems with a variety of important values in mind.
Bio: Sarah is a PhD candidate in the Department of Electrical Engineering and Computer Science at UC Berkeley, advised by Ben Recht. She received her MS in EECS from Berkeley and BSE in Electrical Engineering and Math from the University of Pennsylvania. Sarah is interested in the interplay between optimization, machine learning, and dynamics in real-world systems. Her research focuses on developing principled data-driven methods for control and decision-making, inspired by applications in robotics, recommendation systems, and developmental economics. She is a co-founder of a transdisciplinary student group, Graduates for Engaged and Extended Scholarship in computing and Engineering, and the recipient of a Berkeley Fellowship and a NSF Graduate Research Fellowship.

MS Cyber Physical Systems Graduate Program Webinar
2021-03-30 08:00 - 09:00
Please join faculty, staff, and current students to learn more about graduate programs in the MS in Cyber Physical Systems on March 30 at 8:00 EST.
Registration may be found at: https://us02web.zoom.us/webinar/register/WN_KiuKTsiLRn2n-vmRlz2m6A
A recording will be available for those who are unable to attend.

Electrical and Computer Engineering Graduate Programs Webinar
2021-03-30 09:30 - 10:30
Please join faculty, staff, and current students to learn more about graduate programs in the Electrical and Computer Engineering Department on March 30 at 9:30 EST.
Registration may be found at: https://us02web.zoom.us/webinar/register/WN_cKfKDbSOQQu63xcwc9y4WA
A recording will be available for those who are unable to attend.

Notable Women in STEM
2021-03-30 16:00 - 17:00
What makes a scientist notable? What notable women in STEM come to mind? Join us on Zoom Tuesday, March 30 at 4 pm for a discussion of what makes a notable contribution to STEM, how that compares to stated metrics of notability, and as a result, how those criteria may influence our understanding of women’s role in STEM fields, past and present.
To register for the event and submit your ideas of the notable women of Northeastern, please visit https://bit.ly/NotableWomenInSci.
Co-hosted by the College of Science, Graduate Women in Science and Engineering, and Northeastern University Library Digital Scholarship Group

PlusOne Information Session
2021-03-30 19:00 - 20:00
LEARN ABOUT THE PLUSONE ACCELERATED MASTER’S DEGREE PROGRAM
A master’s degree can provide you an additional level of expertise in an area aligned with your career goals. As a currently enrolled Bachelor of Science (BS) student in the College of Engineering at Northeastern, you have the opportunity to earn a Master of Science degree (MS) in an accelerated time period with the PlusOne program. Once accepted into the program in an approved PlusOne pathway, which is a BS and MS PlusOne combination, you can earn an MS degree with, in most cases, just one extra year of study beyond your undergraduate degree program.
In this virtual information session, College of Engineering undergraduate and graduate academic advisors will provide an overview of the PlusOne program to give you the knowledge and next steps to take advantage of the program if you choose.
WHAT YOU WILL LEARN:

What is PlusOne
Benefits of the program
Eligibility
Co-op considerations
Financial considerations
Selecting your pathway
Academic advising resources
Timeline to apply
The application process
Course registration
Transitioning to graduate school

EVENT DETAILS:
Date: Tuesday, March 30, 2021
Time: 7 – 8 p.m. EST
Zoom Link

ChE Seminar Series: Engineering Approaches to Understand Functional Connectivity in Neocortex
2021-03-31 12:00 - 13:00
ChE Seminar Series Presents:
Dr. John A. White, Ph.D
Professor and Chair of Biomedical Engineering, Boston University
Engineering Approaches to Understand Functional Connectivity in Neocortex
Abstract
The mammalian neocortex is a crowning achievement of evolution. It is astronomically complex, with around 100 billion computational elements, each of which is staggeringly intricate by itself, and on the order of 1016 synaptic connections. In this talk, I plan to examine three questions related to the neocortex. First, what are the consequences of component miniaturization for neural computation? Second, how can we model neural computation on such a scale in a way that makes tractable predictions? Third, what does distributed neural computation “look like?” The bulk of the talk will focus on testing strong predictions from the relatively simple stabilized supralinear network (SSN) model of how neocortical networks behave in resting wakefulness, and how that behavior changes when the network is activated by sensory input or intentional movement. Our data are collected from mouse somatosensory cortex, mainly under whole-cell patch clamp, but also using genetically encoded calcium indicators. Our results are mainly compatible with the SSN model.
Biography
John A. White is Professor and Chair of Biomedical Engineering at Boston University. He has joint appointments in the Program in Neuroscience and the Department of Pharmacology and Experimental Therapeutics. He is PI and Program Director for BU BME’s long-standing NIGMS training grant in Quantitative Biology and Physiology. Prof. White received his BS in BME from Louisiana Tech University (1984), and his PhD in BME from Johns Hopkins University (1990).
Professor White’s research group uses engineering and computational approaches to study computation in single neurons and astrocytes, as well as network interactions. He is a co-developer of RTXI, the most widely used programming environment for virtual-reality-inspired experiments in neurophysiology, and is known for describing the biophysical bases of neuronal oscillations and the factors that limit signal-to-noise in neurons and neuronal networks. His group has collaborated to develop new mouse lines, and new scanning approaches, for fluorescence imaging in neurons and astrocytes. He is the author of over 100 peer-reviewed publications, has given over 150 invited lectures, and has raised over $50M in external funding. White is a Fellow of the Biomedical Engineering Society, the American Institute for Medical and Biological Engineering, and the International Academy of Medical and Biological Engineering. In 2019, White was elected President of the Biomedical Engineering Society.
Please email Alyssa Ramsey at a.ramsey@northeastern.edu for the link to the seminar.

Scattergories w/ GWiSE
2021-04-05 18:00 - 19:30
Join GWiSE for our monthly community time on Monday, April 5th at 6 pm EST to play some zoom scattergories and maybe win some prizes! This event will have two winners: $25 for the person with the most points $25 for overall funniest answers (we will vote!). Register on SAIL 🙂

Global Co-op Self-Developing Information Session
2021-04-06 10:00 - 11:00
Join the College of Engineering Global Co-op team in learning about self-developing a global co-op opportunity for Summer II/ Fall 2021. This session will be interactive and the topics discussed will include:

Search techniques and global positions in your field
What to consider when interested in a global co-op
Step by step information for networking and self-developing

RSVP via NUworks Events Calendar for Zoom link.
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: Subhramoy Mohanti
2021-04-06 10:00 - 11:00
PhD Proposal Review: Distributed Data and Energy Beamforming with Unmanned Vehicles for Wireless IoT : A Systems Perspective
Subhramoy Mohanti
Location: Teams Meeting
Abstract: The pervasive deployment of the wireless Internet of Things (IoT) has given rise to heterogeneous sensors and small form-factor computing devices in homes, offices, public spaces, manufacturing floors, among others. Such large number of connected devices require (i) simple ways of charging, so that they remain operationally available, and (ii) effective ways of sharing wireless spectrum, so that they continue to transmit and receive data amidst competing and interfering signals. This thesis focuses on the link and physical layer of the protocol stack to enable distributed beamforming as a key enabler for these two objectives. Specifically, we experimentally demonstrate how beamforming capability can address both wireless power transfer (WPT) needs and resilient communication in interference-challenged environments.
This thesis proposes a method for accessing and sharing the wireless channel for both regular data communication and WPT. This is the first work that accomplishes these dissimilar tasks within the constraints of the standard compliant IEEE 802.11 protocol, resulting in a practical and so called ‘WiFi-friendly Energy Delivery’ (WiFED). First, WiFED exploits the IEEE 802.11 supported protocol features to request energy and for energy transmitters to participate in energy transfer via beamforming. Second, it devises a controller-driven bipartite matching algorithm, assigning appropriate number of energy transmitters to sensors for efficient energy delivery. Thirdly, it detects outlier sensors, which have limited power reception from static energy transmitters and utilizes mobile energy transmitters to satisfy their charging cycles.
From a communication-only perspective that relies on distributed beamforming, this thesis presents AirBeam, a software-based approach that runs on Unmanned Aerial Vehicles (UAVs) to deliver on-demand data to sensors deployed in infrastructure constrained environments. We first show why this problem is difficult given the continuous hovering-related channel fluctuations, synchronizing the distributed transmit streams without a wired clock reference, the need to ensure timely feedback from the ground receiver due to the channel coherence time, and the size, weight, power, and cost (SWaP-C) constraints for UAVs. This work is extended further to consider realistic traffic patterns and packet arrival thresholds, involving dynamic grouping of transmitters to beamform towards target receivers at any given time. Again, we evaluate outcome both experimentally and in a virtual environment in Colosseum, the world’s largest RF emulator.
Since beamforming requires the action of multiple devices not directly connected to each other by wire, we introduce a security framework called AirID, which identifies authorized beamforming UAVs by learning their so called ‘RF fingerprints’. This step requires applying deep learning techniques on their received signals, with the goal of identifying discriminative features introduced by the transmitter due to process variations. Our approach involves intentionally inserting ‘signatures’ in the signals from each known UAV, which are detected through a deep convolutional neural network (CNN) at the physical layer, without affecting the ongoing UAV data communication process.
In the proposed work, we will explore optimized placement of UAVs, while also considering battery limits, to enhance beamforming performance. We will validate these outcomes in a testbed of 4-5 UAVs.

ChE Seminar Series: Engineered Autonomous Control of Metabolic Pathways
2021-04-07 12:00 - 13:00
ChE Seminar Series Presents:
Kristala L. J. Prather, Ph.D.
Arthur D. Little Professor, Department Executive Officer, Department of Chemical Engineering, MIT
Engineered Autonomous Control of Metabolic Pathways
Abstract
Microbial systems offer the opportunity to produce a wide variety of chemical compounds in a sustainable fashion. Economical production, however, requires processes that operate with high titer, productivity, and yield. One challenge towards maximizing yields is the need to use substrate for biomass, resulting in a competing pathway that cannot merely be eliminated. Productivities may also be significantly influenced by the timing of expression of genes in the production pathway. Dynamic metabolic engineering has emerged as a means to address these and other impediments in strain performance. Ideally, the triggers for dynamic control would be autonomous, that is, independent of any external intervention by the operator. We have developed such autonomous devices based on pathway-independent quorum-sensing circuits and have demonstrated their utility across several distinct metabolic pathways and with varying levels of complexity. In this talk, I will describe our approach for development of these Metabolite Valves and results to date from their implementation.
Biography
Kristala L.J. Prather is the Arthur D. Little Professor in and Executive Officer of the Department of Chemical
Engineering at MIT. She received an S.B. degree from MIT in 1994 and Ph.D. from the University of California, Berkeley (1999), and worked 4 years in BioProcess Research and Development at the Merck Research Labs prior to joining MIT. Her research interests are centered on the design and assembly of recombinant microorganisms for the production of small molecules, with additional efforts in novel bioprocess design approaches. A particular focus is the elucidation of design principles for the production of unnatural organic compounds with engineered control of metabolic flux within the framework of the burgeoning field of synthetic biology. Prather is the recipient of an Office of Naval Research Young Investigator Award (2005), a Technology Review “TR35” Young Innovator Award (2007), a National Science Foundation CAREER Award (2010), the Biochemical Engineering Journal Young Investigator Award (2011), and the Charles Thom Award of the Society for Industrial Microbiology and Biotechnology (2017). Additional honors include selection as the Van Ness Lecturer at Rensselaer Polytechnic Institute (2012), as a Fellow of the Radcliffe Institute for Advanced Study (2014-2015), the American Association for the Advancement of Science (AAAS; 2018), and the American Institute for Medical and Biological Engineering (AIMBE; 2020).
Please email Alyssa Ramsey at a.ramsey@northeastern.edu for the link to the seminar.

ECE PhD Dissertation Defense: Vikrant Shah
2021-04-07 14:00 - 15:00
PhD Dissertation Defense: Visual Navigation Applications in Low Contrast Environments: Multi Sensor Iceberg Mapping
Vikrant Shah
Location: Zoom Link
Abstract: Most approaches to visual navigation make multiple assumptions about the scenes being imaged. There are implicit assumptions about the scene being predominantly static and the availability of well illuminated, texture rich, objects in the scene. In some cases these assumptions severely limit or eliminate the full applicability of visual Simultaneous Localization and Mapping (SLAM) and Structure from Motion (SfM) methodologies. This dissertation attempts to address problems where the assumptions of static scenes and texture rich objects are not valid. Motivated by the application of mapping rotating and translating icebergs, we propose a system level solution for addressing the problem of mapping large, low contrast, moving targets with slow but complicated dynamics.
Our approach leverages the complementary nature of multiple sensing modalities and utilizes a rigidly coupled combination of a subsurface multibeam sonar (a line scan sensor) and an optical camera (an area scan sensor). This allows the system to exploit the optical camera information to perform iceberg relative navigation, which can be directly used by the multibeam sonar to map the iceberg underwater. To compensate for the effect of low contrast we conducted an in-depth analysis of features detectors and descriptors on end-to-end SfM algorithms to demonstrate and understand how methodologies such as Contrast Limited Adaptive Histogram Equalization (CLAHE) and Zernike Moment descriptors help improve the overall accuracy in these challenging applications.
We merge these approaches into an algorithmic framework that allows us to compute the scale of the navigation solution and iceberg centric navigation corrections. These corrections can then be used for accurate iceberg reconstructions. This enables a quantitative analysis of our iceberg mapping efforts including volume estimation and change detection.
We successfully demonstrate our approach on real field data from three of the icebergs surveyed multiple times during the 2018 and 2019 campaigns to the Sermilik fjord in Eastern Greenland. Availability of iceberg mounted Global Navigation Satellite System (GNSS) observations during these research expeditions also allowed for a comparison of this approach against ground truth, providing additional confidence in the systems level mapping efforts. The accuracy of the reconstructions is demonstrated by estimating iceberg volumes, calculating their ablation rates, and performing change detection at a granular scale.

ECE Seminar: Mahdi Imani
2021-04-08 11:00 - 12:00
ECE Seminar: Reinforcement Learning Perspective to Data-Driven and Model-Based Experimental Design
Mahdi Imani
Location: Zoom Link
Abstract: Design and decision-making are pervasive in most practical systems including smart grids, transportation, manufacturing, healthcare, and smart homes. Accurate system modeling is difficult in most systems/processes due to the complicated system dynamics, multi-physics and multiple time scales involved in phenomena, hybrid dynamics across cyber and physical layers, and various sources of parametric and environmental uncertainties. Design and decision-making in these systems are fraught with choices, choices that are often expensive, complex, and high-dimensional, with interactions and uncertainties that make them difficult for individuals to reason about. This talk will mainly focus on the speaker’s latest research on providing a new unified reinforcement learning perspective for model-based and data-driven experimental design to enable scalable, efficient, and reliable design and decision-making under various sources of uncertainty.
Bio: Mahdi Imani is an Assistant Professor in the Department of Electrical and Computer Engineering at the George Washington University. He received his Ph.D. degree in Electrical and Computer Engineering from Texas A&M University in 2019, and his M.Sc. degree in Electrical Engineering and his B.Sc. degree in Mechanical Engineering, both from the University of Tehran in 2014 and 2012. His research interests include Machine Learning, Control Theory, and Signal Processing, with a wide range of applications from computational biology to cyber-physical systems. He has been elevated to IEEE Senior Member grade in 2021. He is also the recipient of multiple awards, including NSF SCH Aspiring PI Awardee in 2020 and 2021, IBM Research Almaden Distinguished Speaker in 2019, the Association of Former Students Distinguished Graduate Student Award for Excellence in Research-Doctoral in 2019, the Best Ph.D. Student Award in ECE department and a single finalist nominee of ECE department for the Outstanding Graduate Student Award in the college of engineering at Texas A&M University in 2018, and the best paper finalist award from the 49th Asilomar Conference on Signals, Systems, and Computers, 2015.

ChE Seminar Series: Tools for Analyzing and Repairing Biological Systems
2021-04-09 12:00 - 13:00
ChE Seminar Series Presents:
Dr. Edward S. Boyden, Ph. D.
Y. Eva Tan Professor in Neurotechnology at MIT
Howard Hughes Medical Institute, McGovern Institute
Professor, Departments of Brain and Cognitive Sciences, Media Arts and Sciences, and Biological Engineering, MIT
Tools for Analyzing and Repairing Biological Systems
Abstract
Understanding and repairing complex biological systems, such as the brain, requires technologies for systematically observing and controlling these systems. We are discovering new molecular principles that enable such technologies. For example, we discovered that one can physically magnify biological specimens by synthesizing dense networks of swellable polymer throughout them, and then chemically processing the specimens to isotropically swell them. This method, which we call expansion microscopy, enables ordinary microscopes to do nanoimaging – important for mapping the brain across scales. Expansion of biomolecules away from each other also decrowds them, enabling previously invisible nanostructures to be labeled and seen. As a second example, we discovered that microbial opsins, genetically expressed in neurons, could enable their electrical activities to be precisely controlled in response to light. These molecules, now called optogenetic tools, enable causal assessment of how neurons contribute to behaviors and pathological states, and are yielding insights into new treatment strategies for brain diseases. Finally, we are developing, using new strategies such as robotic directed evolution, fluorescent reporters that enable the precision measurement of signals such as voltage and calcium. By fusing such reporters to self-assembling peptides, they can be stably clustered within cells at random points, distant enough to be resolved by a microscope, but close enough to spatially sample the relevant biology. Such clusters, which we call signaling reporter islands (SiRIs), permit many fluorescent reporters to be used within a single cell, to simultaneously reveal relationships between different signals. We share all these tools freely, and aim to integrate the use of these tools so as to enable comprehensive understandings of neural circuits.
Biography:
Ed Boyden is Y. Eva Tan Professor in Neurotechnology at MIT, an investigator of the Howard Hughes Medical Institute and the MIT McGovern Institute, and professor of Brain and Cognitive Sciences, Media Arts and Sciences, and Biological Engineering at MIT. He leads the Synthetic Neurobiology Group, which develops tools for analyzing and repairing complex biological systems such as the brain, and applies them systematically to reveal ground truth principles of biological function as well as to repair these systems. He co-directs the MIT Center for Neurobiological Engineering, which aims to develop new tools to accelerate neuroscience progress, and is a faculty member of the MIT Center for Environmental Health Sciences, Computational & Systems Biology Initiative, and Koch Institute.
Amongst other recognitions, he has received the Wilhelm Exner Medal (2020), the Croonian Medal (2019), the Lennart Nilsson Award (2019), the Warren Alpert Foundation Prize (2019), the Rumford Prize (2019), the Canada Gairdner International Award (2018), the Breakthrough Prize in Life Sciences (2016), the BBVA Foundation Frontiers of Knowledge Award (2015), the Carnegie Prize in Mind and Brain Sciences (2015), the Jacob Heskel Gabbay Award (2013), the Grete Lundbeck Brain Prize (2013), the NIH Director’s Pioneer Award (2013), the NIH Director’s Transformative Research Award (three times, 2012, 2013, and 2017), and the Perl/UNC Neuroscience Prize (2011). He was also named to the World Economic Forum Young Scientist list (2013) and the Technology Review World’s “Top 35 Innovators under Age 35” list (2006), and is an elected member of the National Academy of Sciences (2019), the American Academy of Arts and Sciences (2017), the National Academy of Inventors (2017), and the American Institute for Medical and Biological Engineering (2018). His group has hosted hundreds of visitors to learn how to use new biotechnologies, and he also regularly teaches at summer courses and workshops in neuroscience, and delivers lectures to the broader public (e.g., TED (2011), TED Summit (2016), World Economic Forum (2012, 2013, 2016)).
Ed received his Ph.D. in neurosciences from Stanford University as a Hertz Fellow, working in the labs of Jennifer Raymond and Richard Tsien, where he discovered that the molecular mechanisms used to store a memory are determined by the content to be learned. In parallel to his PhD, as an independent side project, he co-invented optogenetic control of neurons, which is now used throughout neuroscience. Previously, he studied chemistry at the Texas Academy of Math and Science at the University of North Texas, starting college at age 14, where he worked in Paul Braterman’s group on origins of life chemistry. He went on to earn three degrees in electrical engineering and computer science, and physics, from MIT, graduating at age 19, while working on quantum computing in Neil Gershenfeld’s group. Long-term, he hopes that understanding how the brain generates the mind will help provide a deeper understanding of the human condition, and perhaps help humanity achieve a more enlightened state.
Please email Alyssa Ramsey at a.ramsey@northeastern.edu for the link to the seminar.

Virtual Graduate School Open House
2021-04-10 08:00 - 19:00
Join us at the Virtual Graduate Open House that will take place on April 10, 11 and 12. Learn more about your program of interest from faculty or learn more about services at Northeastern University that will enhance your graduate school experience.

ECE PhD Dissertation Defense: Armin Moharrer
2021-04-12 09:00 - 10:00
PhD Dissertation Defense: Leveraging Structural Properties for Large-Scale Optimization
Armin Moharrer
Location: Zoom Link
Abstract: Large scale optimization problems abound in data mining, machine learning, and system design. We address the challenges posed by such large scale optimization problems by providing efficient optimization algorithms. The scope of studied problems is quite broad; it includes applications such as experimental design, computing graph distances (dissimilarity scores), training auto-encoders, multi-target regression, and the design of cache networks. We leverage the structural properties present in these problems, e.g., sparsity or separability. In particular, we introduce some structural properties under which the Frank-Wolfe algorithm (FW) can be distributed over a cluster of computers. We show that the distributed FW running over 350 workers (CPUs) solves an instance of experimental design problem with 20M variables in 79 minutes, while the serial implementation takes 48 hours. Furthermore, we study a variant of FW for the design of cache networks. The problem is NP-hard, but we achieve a $1-1/e$ approximation ratio, by optimizing a non-convex relaxation via FW. We also propose a distributed Alternating Direction Method of Multipliers (ADMM) algorithm for computing graph distances. We observe speedups of 153 times when running over a cluster with 448 CPUs, in comparison with running over 1 CPU, for graphs with 2.4K nodes. Moreover, we study applications of ADMM in solving robust variants of risk minimization problems; in these variants we replace the typically chosen mean squared error loss with a general lp norm. We combine model based optimization with ADMM to minimize the resulting non-smooth and non-convex objectives. We show that a stochastic variant of ADMM converges with the rate O(log T/T) and is highly efficient for optimizing the corresponding model functions.

The Path to Climate Justice
2021-04-12 12:00 - 13:00
Join leading Northeastern faculty for a discussion on the important links between the climate crisis and social justice. Energy justice and climate researchers and activists Jennie Stephens, Frances Roberts-Gregory, and Brian Helmuth will lead a conversation about climate justice action and how to effectively connect knowledge to action during these disruptive times.
Registration

How To: Gain Creative Confidence
2021-04-12 12:30 - 13:00
Mohamed Kante, E’12 Visionary & Chief Nerd iNERDE Inc.

Have you ever wondered how to generate great ideas on demand? Are you looking to unleash your creative potential? This tutorial will explore some of the myths about creativity and innovation, and guide you toward a higher level of thinking for your customers and users. By the end, you’ll be equipped with tools and techniques for practicing outside-the-box problem-solving in our era of accelerating change.
Hosted by Northeastern Alumni Relations. Whether you identify as a seasoned entrepreneur or an entrepreneur in the making, learn from thought leaders and idea generators on our Instagram page, 30-minutes at a time.

ECE PhD Dissertation Defense: Neville Sun
2021-04-12 15:00 - 16:00
PhD Dissertation Defense: RF Magnetoelectric Devices for Communication, Sensing, and Power Electronics
Neville Sun
Location: Zoom Link
Abstract: A strong magnetoelectric (ME) coupling of layered magnetic/ferroelectric heterostructures can effectively convert energy between electric and magnetic fields. By utilizing strain mediated ME coupling, it is possible to use an electric field to control magnetic film properties, such as magnetization, permeability, and spin wave. Additionally, an applied magnetic field can be used to control electric polarization. In this talk, ME voltage tunable inductors and ME acoustically actuated mechanical antennas/sensors are demonstrated and analyzed with different heterostructure compositions and design considerations for improving device performance.
The first part examines a new class of voltage tunable magnetoelectric inductors with textured multiferroic cores consisting of a Metglas/piezoelectric laminate/Metglas composite for MHz adaptive power systems. These inductors demonstrate a large, instantaneous, and non-discrete tunable range with a wide operational frequency range from DC to 10 MHz. A tunable inductance range of up to 346% was achieved with an applied electric field of 24 kV/cm. However, low voltage tunability is miniscule, typically less than 6% at 30 V applied voltage. By optimizing the anisotropy of magnetoelastic stress, a 50 um thick PMN-PT slab is shown to improve low voltage tuning by 6 times. These ME tunable inductors with low driving voltage provide adaptability for changing circuit conditions and are ideal for compact/lightweight power systems for electronic warfare and communication systems.
The second device of interest is a new MEMS ME antenna/sensor design based on the solidly mounted resonator (SMR) structure. The SMR replaces the freestanding membrane structure of a film-bulk acoustic resonator (FBAR) with a Bragg acoustic reflector for concentrated energy confinement while improving structural integrity and power handling. The antenna radiates using converse ME coupling physics while receiving and sensing EM waves by using direct ME coupling. A unique spin sprayed NiZn ferrite/AlN structure and performance characterization for arrayed resonators are presented. The acoustic resonance in the heterostructure films operates at UHF range for seamless on-chip integration with WiFi, Bluetooth, and GPS devices. The robust features of the sub-mm size SMR ME antenna are demonstrated in a miniature aerial drone communication system and provide a possible alternative for biomedical implantables for neurological studies.

ECE PhD Proposal Review: Murphy Wonsick
2021-04-12 15:00 - 17:00
PhD Proposal Review: Improving Human Robot Interaction through Extended Reality Technologies
Murphy Wonsick
Location: Teams Link
Abstract: Recent advancements in robotics have allowed robots to become capable enough to be used in a wide variety of domains, such as manufacturing, search-and-rescue, and space exploration. However, human-robot interaction with these systems are still primarily achieved using 2D devices, such and laptops, tablets, and/or game controllers despite operating in a 3D world. And although these interfaces can be very capable in operating a robot, they are often complex and require expert operators as well as extensive training. Extended reality technologies provide an opportunity to create more intuitive human-robot interaction by allowing operators to visualize and interact with 3D data in a 3D environment, allowing for a more natural interaction. Usage of extended reality technologies in human-robot interaction though are still very limited. In this proposal, I aim to investigate how to provide better experiences for humans in human-robot interaction using extended reality technologies. Focus will be spent on using virtual reality headset to create supervisory control interfaces for remote robot operation and augmented reality head-mounted displays to help facilitate communication in human-robot shared workspaces. The goal of this work is to move towards more intuitive and easy-to-use interfaces for human-robot interaction.

ChE Seminar Series: Metal electrodes: the future of cost-effective storage of electrical energy
2021-04-14 12:00 - 13:00
ChE Seminar Series Presents: Dr. Lynden A. Archer
Lynden A. Archer, Ph.D
Joseph Silbert Dean of the College of Engineering and the James A Friend Family Distinguished Professor of Chemical and Biomolecular Engineering
Cornell University, Ithaca NY
Metal electrodes: the future of cost-effective storage of electrical energy
Abstract
The levelized cost of electric power generated from renewable wind and solar resources have fallen, continuously over the last decade. This trend is fueling optimism about humanity’s ability to achieve net-zero carbon emissions in the electric power generation and transportation sectors—without the large government subsides predicted as recently as a decade ago. It is known that the intermittency and seasonal variability of the electric power supply from wind and solar sources pose significant barriers to broad-based acceptance of clean electric power. Low-cost options for storing large quantities of renewable electric power would lower/eliminate these barriers and meet an unmet need in both the power generation and transportation sectors. Rechargeable electrochemical cells based on metallic anodes, including lithium, zinc, and aluminum, offer the potential for transformative advances in cost-effective storage of electrical energy. Such cells are under active development worldwide because they provide a path towards battery systems capable of meeting the performance and long-term storage requirements for truly dispatchable electric power generation from renewables. Recharge of any metal anode requires reversible electrodeposition/crystallization of metals; a process that is fundamentally unstable. This talk considers the stability limits for metal electrodeposition processes in liquid and semisolid structured electrolytes and, on that basis, proposes electrode and anode/electrolyte interphase design principles for enabling highly reversible storage solutions. The talk will also explore contemporary efforts to create minimal electrolytes and electrochemical interphases based on these principles and will discuss their effectiveness in enabling cost-effective energy storage systems with high levels of reversibility.
Biography
Lynden Archer is the Joseph Silbert Dean of the College of Engineering and the James A Friend Family Distinguished Professor of Chemical and Biomolecular Engineering. His research focuses on the transport properties of polymers and polymer-nanoparticle hybrid materials, and their applications for electrochemical energy storage. Archer received his Ph.D. in chemical engineering from Stanford University in 1993 and was a Postdoctoral Member of the Technical Staff at AT&T Bell Laboratories in 1994. He is a member of the National Academy of Engineering (NAE) and fellow of the American Physical Society (APS). His research contributions have been recognized with various awards, including the AIChE Nanoscale Science and Engineering Forum award, the National Science Foundation award for Special Creativity, an NSF Distinguished Lectureship in Mathematical & Physical Sciences, the American Institute of Chemical Engineer’s MAC Centeniell Engineer award, and the Thompson-Reuters World’s Most Influential Scientific Minds in Materials Science for 2014 & 2015. At Cornell, he has been recognized with the James & Mary Tien Excellence in Teaching Award and thrice with the Merrill Presidential award as the most influential member of the Cornell faculty selected by a Merrill Presidential Scholar.

Global Co-op Self Developing Info Session
2021-04-14 17:00 - 18:00

Machine Learning with MATLAB Webinar
2021-04-15 18:00 - 19:30
Date: Thursday, April 15th
Time: 6:00pm – 7:30pm, including Q&A
Register Now
Engineers and data scientists work with large amounts of data in various formats such as sensor, image, video, telemetry, databases, and more. They use machine learning to find patterns in data and build models that predict future outcomes based on historical data.
In this session, we explore the fundamentals of machine learning using MATLAB. We introduce machine learning techniques in MATLAB to quickly explore your data, evaluate machine learning algorithms, compare the results and apply the best technique to your problem.
Highlights include:

Training, evaluating, and comparing a range of machine learning models
Using refinement and reduction techniques to create models that best capture the predictive power of your data
Running predictive models in parallel using multiple processors to expedite your results
Deploying your models to production in a variety of formats
