Northeastern University College of Engineering

PhD Proposal Review: Rashida Nayeem
2021-06-11 10:30 - 11:30 (America/New_York)
PhD Proposal Review: Human Strategies in the Control of Complex Objects: A Task-Dynamic Approach with Clinical Applications
Rashida Nayeem
Location: Zoom Link
Passcode: [REDACTED]
Abstract: Functional interaction with objects – tool use – is essential in daily living and is regarded as the foundation of our evolutionary advantage. Humans effortlessly interact with a variety of objects, including those with complex internal dynamics. Even the simple action of picking up a cup of coffee to drink is a mechanically intricate process: the hand applies a force to the cup, and indirectly to the liquid, which exerts forces back on the hand. Reacting to and mitigating these dynamics in real time is difficult due to long sensorimotor delays and ubiquitous noise in the human sensorimotor system. Hence, prediction is necessary to preempt undesired ‘sloshing’. But prediction of this nonlinear and potentially chaotic object is extremely difficult. Hence, this research tests the hypothesis that humans learn to control the object to make dynamics simpler––or more predictable. Inspired by the task of transporting a ‘cup of coffee,’ a series of experiments use a virtual and a real testbed that model the task as a cup-and-ball system. In all experiments, human subjects move the cup with a rolling ball inside. Aim-1 investigates the effect of linearization on human control strategies in a 2D virtual task. Aim-2 examines if humans exploit initial conditions to facilitate predictable dynamics in the 2D virtual task. This is tested in subjects that are provided with either full sensory information, or when deprived of visual or haptic information. Aim-3 examines how subjects explore and prepare the cup-and-ball in the same 2D task and a novel 3D virtual task introducing planar cup movements. The question is how subjects explore and transport objects that have different dynamic properties, either unknown or indicated by the visual cues. The analysis adopts a task-dynamic approach that affords principled hypothesis-testing by parsing the complex dynamics into execution and result variables, with minimal assumptions about the human controller. Aim-4 takes the insights from this basic research to a clinical context, testing patients with stroke in this functional task. A real version of the cup-and-ball task was created to quantitatively assess severity and recovery of motor impairment in patients after stroke. Using the same analysis methods, the objective is to sensitively assess impairment in the context of a functional skill.

CommLab Research Dissemination Series: Literature Review Workshop
2021-06-10 16:30 - 17:30
Are you joining a new research area that you are not familiar with and don’t know how to get started? Or perhaps you are writing an introduction section to your dissertation or fellowship proposal and need help?
Join the Northeastern COE CommLab for the next workshop in the Research Dissemination Series. We will discuss the importance of writing a literature review and different tools you can use to communicate broad ideas effectively.
Register for this Zoom virtual workshop.

PhD Proposal Review: Bernard Herrera
2021-06-10 15:30 - 16:30
PhD Proposal Review: Ferroelectric Micromachined Ultrasonic Transducers for Intra-body and In-memory Sensing Applications
Bernard Herrera
Location: Teams
Abstract: Piezoelectric Micromachined Ultrasonic Transducers (PMUTs) are Micro Electro-Mechanical Systems (MEMS) devices that have become an established technology in applications such as range-finding, fingerprint sensing and imaging due to their capability of ultrasonic transduction in a miniaturized footprint, easily amenable to create large arrays. However, their application space still remains quite open. PMUTs are well fitted to applications in liquid media, such as implantable and underwater devices, due to their inherent acoustic matching and wide bandwidth. Thus, in the first part of this proposal, we explore novel applications such as PMUT-based intra-body networking, power transfer, source localization, wide-band matching and duplexing.
Aluminum Nitride (AlN) has been the material of choice for our PMUTs due to its biocompatibility and possibility of single-chip integration with supporting CMOS circuitry. Scandium doping of AlN thin films has recently been demonstrated to increase piezoelectric coupling coefficients while introducing ferroelectric properties in the material. However, a simultaneous use of both capabilities has not been demonstrated in the state-of-the-art. The ability of having distinct ferroelectric states, that alter the mechanical performance of the devices, allows for in-memory sensing and actuation features and provides the building blocks for neuromorphic signal processing capabilities. The second part of the proposal explores the AlScN material integration into novel Ferroelectric Micromachined Ultrasonic Transducers (FMUTs) and their emerging application space.

ECE PhD Proposal Review: Andac Demir
2021-06-10 13:00 - 14:00
PhD Proposal Review: Automated Bayesian Network Exploration for Nuisance-Robust Inference
Andac Demir
Location: Zoom Link
Abstract: A fundamental challenge in the analysis of physiological signals is learning useful features that are robust to nuisance factors e.g., inter-subject & inter-session variability, and achieve the highest nuisance-invariant classification performance. Towards resolving this problem, we introduce 2 frameworks: AutoBayes, which is an AutoML approach to conduct neural architecture search for research prototyping, and a GNN based framework: EEG-GNN.
The ultimate goal of the AutoBayes framework is to identify the conditional relationship between a physiological dataset, associated task labels, nuisance variations and potential latent variables in order to robustly infer the task labels invariant of nuisance factors. Nuisance factors in the case of physiological datasets could be variations in subjects or sessions, but we only focus on subject variations in the experiments. AutoBayes enumerates all plausible Bayesian networks between data, labels, nuisance variations and potential latent variables, detects and prunes the unnecessary edges according to Bayes-Ball Algorithm, and then trains the resulting DNN architectures for different hyperparameter configurations in an adversarial/non-adversarial or a variational/non-variational setting to achieve the highest validation performance. Instead of hyperparameter tuning for model optimization, AutoBayes concentrates on the architecture search of plausible Bayesian networks, and achieves state-of-the-art performance across several physiological datasets. Furthermore, we ensemble several Bayesian networks by stacking their posterior probability vectors in a higher level learning space, train a shallow MLP as a meta learner, and measure the task and nuisance classification performance on a hold-out dataset. We observe that exploration of different inference Bayesian networks has a significant benefit in improving the robustness of the machine learning pipeline, and the parallel activity of vast assemblies of different Bayesian network models significantly reduces variation across subjects in the cross-validation setting.
In the second part of the proposal, we benchmark the performance of EEG-GNN against the AutoBayes framework. CNN’s have been frequently used to extract subject-invariant features from EEG for classification tasks, but this approach holds the underlying assumption that electrodes are equidistant analogous to pixels of an image and hence fails to explore/exploit the complex functional neural connectivity between different electrode sites. We overcome this limitation by tailoring the concepts of convolution and pooling applied to 2D grid-like inputs for the functional network of electrode sites. Furthermore, we develop various GNN models that project electrodes onto the nodes of a graph, where the node features are represented as EEG channel samples collected over a trial, and nodes can be connected by weighted/unweighted edges according to a flexible policy formulated by a neuroscientist. The empirical evaluations show that our proposed GNN-based framework outperforms standard CNN classifiers across ErrP and RSVP datasets, as well as allowing neuroscientific interpretability and explainability to deep learning methods tailored to EEG related classification problems. Another practical advantage of our GNN-based framework is that it can also be used in EEG channel selection, which is critical for reducing computational cost, and designing portable EEG headsets.

CILS June Seminar: Bruker BipSpec 3T MRI
2021-06-09 12:00 - 13:30
Join the Institute for Chemical Imaging of Living Systems for an informational seminar on the Bruker BioSpec 3T MRI machine.
The seminar will cover

how preclinical MR offers longitudinal observation/measurement of disease processes
an overview of components and process for preclinical MR
review of methods and data obtained in preclinical MR

Following the talk, Kristine Ma, PhD candidate from the Clark Lab, will present on DNA-based pH-responsive MR contrast agents.
Register: https://forms.gle/PTHE2yNtftbkRBGV9

ECE PhD Dissertation Defense: Lorenzo Bertizzolo
2021-06-08 10:00 - 11:00
PhD Dissertation Defense: Software-Defined Wireless Networking for 5G and Beyond: From Indoor Cells to Non-Terrestrial UAV Networks
Lorenzo Bertizzolo
Location: Microsoft Teams
Abstract: While Software-defined Networking (SDN) is a consolidated and widely adopted concept in fixed infrastructure, its adoption to the wireless domain has been limited by some fundamental challenges. Unlike traditional fixed infrastructure, which relies entirely on fiber communications, wireless networks suffer from unpredictable access and backhaul operations. Therefore, they ask for new architectural solutions to implement SDN. A prerequisite to implementing SDN for wireless is to adopt programmable radio hardware and software-based implementation of the wireless protocol stack. Then, it is necessary to adopt control APIs that expose the wireless protocols’ internal operations to external control. Finally, to bridge the gap between SDN theory and implementation, this thesis proposes a series of architectural solutions that provide the communication infrastructure and the architectural control innovations necessary to implement SDN control and optimization on real wireless systems. Specifically, this thesis proposes two different architectural solutions. For fixed Radio Access Networks (RANs) that benefit from a low-latency and reliable backhaul infrastructure, we propose to implement a centralized control approach similar to SDN for fixed infrastructure. Here a central controller collects network state information of a distributed RAN, solves a network control problem, and distributes the solutions to the individual wireless nodes. On the other hand, for infrastrcuture-less RANs, which implement both the access and the backhaul in wireless, we propose a new architectural solution that moves the control logic to the edge of the networks, to the very wireless nodes.

ECE PhD Dissertation Defense: Yumin Liu
2021-06-03 15:00 - 16:00
PhD Dissertation Defense: Learning from Spatio-Temporal Data with Applications in Climate Science
Yumin Liu
Location: Zoom Link
Abstract: Climate change is one of the major challenges to human beings and many other species in our time. In the recent decade, the number of disasters related to climate change such as wildfires, storms, floods and droughts are increasing, and the casualty and economic losses caused by them are larger compared to those of decades ago. This calls for better and efficient ways to predict climate change in order to better prepare and reduce losses. Predicting climate change involves using historical observational data and model simulated data, both of which usually involve multiple locations and timestamps and are spatio-temporal. With the rapid development and progress of machine learning, these methods have achieved several impactful contributions in many domains; we would like to translate its impact to climate science.
In this thesis we address several problems in climate science. This challenging complex domain enable us to develop, innovate, adapt, and advance machine learning in the following ways. 1) We develop a multi-task learning method to estimate relationships between tasks and learn the basis tasks in different locations especially for nearby locations which may share similar climate patterns. This method assumes that the weights of an observed task is a linear combination of several latent basis tasks and that the task relationships can be learnt by imposing a regularized precision matrix. 2) We propose a nonparameteric mixture of sparse linear regression models to cluster and identify important climate models for prediction. This model incorporates Dirichlet Process (DP) to automatically determine the number of clusters, imposes Markov Random Field (MRF) constraints to guarantee spatio-temporal smoothness, and selects a subset of global climate models (GCMs) that are useful for prediction within each spatio-temporal cluster with a spike-and-slab prior. We derive an effective Gibbs sampling method for this model. 3) We adapt image super resolution methods to climate downscaling — increasing spatial resolution for climate variables for local impact analysis. The proposed method is called YNet which is a novel deep convolutional neural network (CNN) with skip connections and fusion capabilities to perform downscaling for climate variables on multiple GCMs directly rather than on reanalysis data. 4) We use saliency map method to discover dependencies among climate variables. We propose the concept of cyclical saliency map (Cyclic-SM) which are meaningful in climate context and more robust to noise as compared to ordinary saliency maps. We show that Cyclic-SMs can reveal relevant spatial regions for prediction. We demonstrate the effectiveness of this method in climate downscaling, ENSO index prediction and river flow prediction.

ECE PhD Proposal Review: Mehdi Nasrollahpourmotlaghzanjani
2021-06-03 11:00 - 12:00
PhD Proposal Review: RFICs for Biomedical Magnetic and Magnetoelectric Microsystems
Mehdi Nasrollahpourmotlaghzanjani
Location: Zoom Links
Abstract: Design and analysis of the advanced biomedical circuit and systems in wide variety of applications has emerged a significant interest. Not only in different engineering disciplines, but also in a variety of applications such as neuroscience, COVID-19, etc. In this study, we are proposing an implantable device, handheld device for detecting different diseases and the RFIC design for the ME antenna and sensor evaluations.
First, we show and miniaturized implantable device for deep brain implantation that provides wireless power transfer efficiency (PTE) of 1 to 2 orders of magnitude higher than the reported micro-coils for brain stimulation. The proposed device will simultaneously measure the as magnetic field activity when neurons are firing. In the second part we will go over the RFIC design for the bio-implant devices, evaluation of the ME antennas for communication purposes and the circuit interface to measure the ME and GMI sensors. For final part, we will discuss the handheld device design for early diagnosis of different diseases such as, lung cancer, Alzheimer, Covid-19, etc through exhaled breath on the molecularly imprinted polymer (MIP) gas sensors.

Plant Shift Initiative | Food Tech
2021-06-01 12:00 - 13:00
To have 100% plant based diets is the most ecological and sustainable way to combat climate change. The main issue we have—making it tasty and accessible. This panel will dive into the food revolution taking form.
Panelists
Sebastiano Cossia Castiglioni, PNT’23 – Moderator
Founder & Chairman, Vegan Capital
Chris Kerr – Speaker
Co-founder & Chair, Gathered Foods Corp (Good Catch)
Founding Partner & Chief Investment Officer, Unovis Asset Management
Christie Lagally – Speaker
Founder & Chief Executive Officer, Rebellyous Foods
Julie Farkas – Speaker
Co-founder & Social Impact Director, PLNT Burger
REGISTER

NanoSI 2021 Annual Workshop on Nano Systems Innovation
2021-05-27 09:00 - 18:00
NanoSI 2021 Annual Workshop on Nano Systems Innovation
Thursday, May 27th, 2021
Registration: Zoom Link
Location: Once registered, a Zoom Link will be available.
Description: The goal of the Northeastern SMART Annual Workshop on Nano Systems Innovation (NanoSI 2021) is to bring together researchers, government and industry to discuss new strategies to address the growing demand of sensing, communication and artificial intelligence at the chip-scale while reducing the time for innovation and transition of the new foundational nano-system technologies that are going to be the at root of our nation’s economic strength, national security and technological standing in the years to come.
Preliminary Agenda:
8:50 am – 9:00 am: Check-in
9:00 am – 10:00 am: Opening remarks from University and SMART Center Leadership, DARPA PMs
10:00 am – 10:30 am: Plenary Talk – David Horsley
10:30 am – 11:00 am: Intros from Industrial Partners
11:00 am – 12:00 pm: Center Projects Presentations
12:00 pm – 1:00 pm: Lunch Break
1:00 pm – 3:00 pm: Center Projects Presentations
3:00 pm – 4:30 pm: Panel Discussion with DARPA, Industry, and Academia: Benjamin Griffin, Ronald Polcawich, Amit Lal, Troy Olsson, David Horsley
4:30 pm – 4:45 pm: Closing Remarks
4:50 pm – 5:50 pm: IAB Meeting (Members Only)
6:00 pm: Meeting adjourn

PhD Dissertation Defense: Kunpeng Li
2021-05-26 13:00 - 14:00
PhD Dissertation Defense: Visual Learning with Limited Supervision
Kunpeng Li
Location: Zoom Link
Abstract: Deep learning models have achieved remarkable success in many computer vision tasks. However, they typically rely on large amounts of carefully labeled training data whose annotating process is usually expensive, time-consuming and even infeasible when considering the task complexity and scarcity of expert knowledge.
In this dissertation talk, I will discuss several explorations along the direction of visual learning with limited supervision. They are mainly about learning from data with weak forms of annotations and learning from multi-modal data pairs. Specifically, I will first present a guided attention learning framework to conduct semantic segmentation by mainly using image-level labels, as such weak form of annotation can be collected much more efficiently than pixel-level labels. Under mild assumptions, our framework can also be used as a plug-in to existing convolutional neural networks to improve their generalization performance. This is achieved by guiding the network to focus on correct things when learning concepts from a limited set of training samples. Besides, I will also introduce models that can effectively learn from multi-modal data pairs without relying on dense annotations of visual semantic concepts. Our models incorporate relational reasoning ability into the visual representation learning process so that it can be better aligned with the supervision from corresponding text descriptions.

ECE PhD Dissertation Defense: Mohammad Hossein Hajkazemi
2021-05-25 10:00 - 11:00
PhD Dissertation Defense: High-performance Translation Layers for Cloud Immutable Storage
Mohammad Hossein Hajkazemi
Location: Zoom Link
Abstract: Most storage interfaces support in-place updates: blocks can be rewritten, files can be modified at byte granularity, fields may be updated in database table rows. Yet internally these layers often rely on out-of-place (immutable) writes. In some cases, this may be necessary to use media, such as flash, SMR (shingled magnetic recording) and IMR (interlaced magnetic recording) disk, which do not allow overwrites. In others, it is used to simplify the implementation of transactions and/or crash consistency, in the form of journaling, write-ahead logging, shadow paging, etc.
In a storage system, translation layers perform out-of-place writes, and they are implemented in different layers of storage stack from the file system to the storage device firmware depending on the application. In this dissertation I focus on translation layers for cloud immutable storage technologies to improve the cloud I/O performance. As a part of my thesis, I focus on translation layers for state-of-the-art immutable storage media such as SMR and IMR used in cloud environments, proposing several novel algorithms to improve their efficiency. I also introduce FSTL, a framework to design and implement SMR translation layer. Finally, I describe Collage, a virtual disk I developed over S3 (could be implemented over a similar object storage) using a translation layer which performs large, sequential, out-of-place writes for high performance. It optionally uses fast local storage for write logging and as a write-back cache, guaranteeing prefix consistency under all failure conditions and recovery of all acknowledged writes if the local cache is not lost. Collage supports snapshots and cloned volumes, performs well over erasure-coded storage, and allows consistent asynchronous volume replication over geographic distances. I show that Collage can achieve massive performance improvements (e.g., over 100x for microbenchmarks and 10x for macro-benchmarks) over CEPH RBD, a popular open-source scale-out virtual disk implementation.

ECE MS Thesis Defense: Daniel Uvaydov
2021-05-21 11:00 - 12:00
MS Thesis Defense titled DeepSense: Fast Wideband Spectrum Sensing Through Real-Time In-the-Loop Deep Learning
Daniel Uvaydov
Location: Microsoft Teams
Abstract: Spectrum sharing will be a key technology to tackle spectrum scarcity in the sub-6 GHz bands. To fairly access the shared bandwidth, wireless users will necessarily need to quickly sense large portions of spectrum and opportunistically access unutilized bands. The key unaddressed challenges of spectrum sensing are that (i) it has to be performed with extremely low latency over large bandwidths to detect tiny spectrum holes and to guarantee strict real-time digital signal processing (DSP) constraints; (ii) its underlying algorithms need to be extremely accurate, and flexible enough to work with different wireless bands and protocols to find application in real-world settings. To the best of our knowledge, the literature lacks spectrum sensing techniques able to accomplish both requirements. In this paper, we propose DeepSense, a software/hardware framework for real-time wideband spectrum sensing that relies on real-time deep learning tightly integrated into the transceiver’s baseband processing logic to detect and exploit unutilized spectrum bands. DeepSense uses a convolutional neural network (CNN) implemented in the wireless platform’s hardware fabric to analyze a small portion of the unprocessed baseband waveform to automatically extract the maximum amount of information with the least amount of I/Q samples. We extensively validate the accuracy, latency and generality performance of DeepSense with (i) a 400 GB dataset containing hundreds of thousands of WiFi transmissions collected “in the wild” with different Signal-to-Noise-Ratio (SNR) conditions and over different days; (ii) a dataset of transmissions collected using our own software-defined radio testbed; and (iii) a synthetic dataset of LTE transmissions under controlled SNR conditions. We also measure the real-time latency of the CNNs trained on the three datasets with an FPGA implementation, and compare our approach with a fixed energy threshold mechanism. Results show that our learning-based approach can deliver a precision and recall of 98% and 97% respectively and a latency as low as 0.61ms.

ECE PhD Proposal Review: Siyue Wang
2021-05-13 14:00 - 15:00
PhD Proposal Review: Towards Robust and Secure Deep Learning Models and Beyond
Siyue Wang
Location: Zoom Link
Abstract: Modern science and technology witness the breakthroughs made by deep learning during the past decades. Fueled by rapid improvements of computational resources, learning algorithms, and massive amount of data, deep neural networks (DNNs) have played a dominant role in more and more real-world applications. Nonetheless, there is a spring of bitterness mingling with this remarkable success – recent studies reveals that there are two main security threats of DNNs which limit its widespread usage: 1) the robustness of DNN models under adversarial attacks, and 2) the protection and verification of intellectual properties of well-trained DNN models.
In this dissertation, we fist focus on the security problems of how to build robust DNNs under adversarial attacks, where deliberately crafted small perturbations added to the clean input can lead to wrong prediction results with high confidence. We approach the solution by incorporating stochasticity into DNN models. We propose multiple schemes to harden the DNN models when facing adversarial threats, including Defensive Dropout (DD), Hierarchical Random Switching (HRS), and Adversarially Trained Model Switching (AdvMS).
The second part of this dissertation focuses on how to effectively protect the intellectual property for DNNs and reliably identify their ownership. We propose Characteristic Examples (C-examples) for effectively fingerprinting DNN models, featuring high-robustness to the well-trained DNN and its derived versions (e.g. pruned models) as well as low-transferability to unassociated models. The generation process of our fingerprints does not intervene with the training phase and no additional data are required from the training/testing set.

AMGEN Lecture Series: Biotechnology Edge
2021-05-13 - 2021-05-14
This two-day lecture series is presented by AMGEN scientists, in partnership with the School of Pharmacy and Bouvé College of Health Sciences Dean’s office. It will provide participants with an overview of the drug development process, as well as the biotechnology industry and potential career paths. Lectures will be presented live online. Recording is not allowed. See the full program below.
The lectures will be in real time on WebEx. A link will be sent to everyone who has registered. Attendees of the two-day lecture series will be eligible to earn a Badge.
RSVP

Thursday May 13th 2021, 9.00 am – 4.30 pm

9.00
Introduction

9.10
Life of a Drug, Roger Hart, PhD

10.00
Drug Discovery at Amgen: A Multi‐Modality Approach, Roger Hart, PhD

10.50
Break and Informational Session

11.10
Process Development from Clinic to Approval, Jennifer Litowski,Process Development Principal Scientist

12.00
Lunch and Learn

1.00
Target Identification & Validation, John Ferbas, PhD,

1.50
Introduction to Pharmaceutical Solid-State Chemistry and MaterialsScience, Hyunsoo Park, Process Development Principal Scientist

2.40
Break

2.50
From Bench to Bedside: Discovery of the First FDA‐ ApprovedAntibody Therapeutic for Migraine, Cen Xu, PhD

3.40
The Role of Continuous Manufacturing to Advance Amgen’s SyntheticPortfolio, Matt Beaver, Principal Scientist

Friday, May 14th 2021, 9.00 am – 4.30 pm

9.00
Introduction

9.10
Drug Safety: An Industry Perspective Oluwadamilola Ogunyankin,MD,MPH

10.00
Modeling of Processes, Products and Devices for Drug Development &Manufacturing, Pablo Rolandi, Director Data Sciences

10.50
Break and Informational Session

11.10
Raw Material Selection and Control for ManufacturingPharmaceuticals, Susan Burke, PhD

12.00
Lunch and Learn

1.00
Oncology, Kristin Tarbell, Principal Scientist

1.50
Innovations in Device Technologies for Delivering Biologics to Patients,Shirish Ingawale, PhD

2.40
Break

2.50
Digital Transformation in Biopharmaceutical Operations, Myra Coufal,PhD

3.40
Careers in Biotech, Jessica Smith, Process Development AssociateScientist

Huskies Wellness Week
2021-05-10 - 2021-05-14
Welcome to Huskies Wellness Week, a personal retreat that will leave you feeling refreshed and empowered from the comfort of your own home. Build some ‘you time’ into your days through a lineup of exclusive programming, hosted by the Northeastern Boston Community. Share your progress with our Instagram gameboard—designed for us to stay on track together—for the chance to win a prize.
How do I play?
On Sunday, May 9 we’re posting a gameboard on our Instagram account for the chance to win a $100 gift card to the bookstore. Complete 7 squares for one entry into our challenge, and 10 squares for two entries. Send us a screenshot of the gameboard with your completed tiles checked off or with pictures of your activity overlayed on top by the following Sunday, May 16 at 12pm EST to be entered into the raffle.
The winner will be selected on Monday, May 17 by 12pm EST and contacted via email by Ilana Gensler, MA’19, Assistant Director, Affinity and Domestic Engagement.
Your completed board can be sent directly to @northeastern_alumni through direct message on Instagram. Be sure to share your progress throughout the week on Instagram by tagging @northeastern_alumni.
What if I have a private account?
Upon registering you will be asked to provide your Instagram handle. You will receive a follow-request ahead of Huskies Wellness Week.
Sessions
Viewing in Eastern Time

Activate your Full Potential
5/10/21
8:30 AM-9:30 AM ET
Leena Prabhoo, MEd’90
Managing Partner
Path to Prajñā
Solutions LLP
Enhance your personal and professional wellness by learning how to activate your full potential. This session will explore what it means to be living your full potential and give you the tools to act so you can make it a reality. Learn about some of the benefits you can gain from this process and develop your personal game-plan for moving forward on this journey.

Meditation to Cultivate Peace of Mind
5/12/21
3:00 PM-4:00 PM ET
Stacy Hernandez, AS’98, MS’01
Owner/College
Counselor
The Best U
Settle in for a 40-minute meditation session to cultivate peace of mind in service of your mental health. When you dedicate time exploring within, you learn to listen to your inner voice rather than the influences outside of you. This internal reconnection can help you activate the power you have inside of yourself to stay grounded and bring enhanced mindfulness to every element of your life as you move through each day.

MOVE by The Handle Bar
5/14/21
8:00 AM-8:45 AM ETAnthony Charter
Indoor Cycling Instructor
The Handle Bar Indoor Cycling Studio
MOVE is a 45-minute, total-body workout that combines high-intensity plyometric movement with slow-burning kettlebell strength work. It fuses The Handle Bar’s passion for music-driven exercise with thoughtful programming that complements and enhances the studio’s work on the bike. Class will be accessible for 48 hours after it goes Live at the time of the event.

Vinyasa Flow Yoga
5/14/21
12:00 PM-12:30 PM ET
H Alex Harrison, JD’11
Yoga Instructor
Beacon Hill Yoga
Make space both physically and mentally as we explore and connect with our bodies through the synthesis of yoga, movement, breath, and mindfulness exercises. Expect to flow through traditional yoga postures as we explore the intricacies of skeletal alignment and the muscular engagement required to link pose to pose, and end with a restful savasana.

Register Now

Urban-scale Measurements and Modeling Fate & Transport of PFAS Across Media
2021-05-07 12:00 - 13:00
Join the Department of Civil and Environmental Engineering and the PROTECT Center for a seminar with the EPA’s Dr. Kiran Alapaty, who will deliver a talk titled “Urban-scale Measurements and Modeling Fate & Transport of PFAS Across Media.”
Seminar Abstract:
Per- and Polyfluoroalkyl Substances (PFAS) have gained attention due to their adverse health effects as well as unknown exposures to legacy and novel compounds. As many of these compounds are stable and persistent, many PFAS compounds have been detected worldwide across different media in the total environment. Thus, comprehensive multi-media PFAS chemical concentration data are needed to study PFAS human exposure and health impacts. While some PFAS measurements and exposure studies are available, no comprehensive PFAS measurement data exist at a continental scale. Also, it is not clear to the local and federal government agencies as to how to account for the spatiotemporal distributions of PFAS contamination and associated long-term health impacts. Such issues are acute at local to urban scales. Thus, the in-depth understanding of fate and transport of PFAS across media is much needed and may provide critical information for stakeholders.
The Washington Works plant in Parkersburg, West Virginia has emitted long-chain perfluorooctanoic acid (PFOA) into the environment for decades and at present, it continues to emit hexafluoropropylene oxide dimer acid [(HFPO-DA), GenX]. A database for PFOA at Parkersburg was developed and these PFOA measurements in air, water, and soil provide a good opportunity to validate the multi-media modeling system.
We are tailoring a robust and efficient suite of modeling tools to simulate PFAS fate and transport in air, water, and soil at urban scales. For air, a state-of-the-art dispersion model (QUIC) is being tested for PFAS air modeling. For other media, we are testing two state-of-the-art USGS models (MODFLOW and MT3D) for groundwater, the BreZo model for surface water, and EPA’s model PRZM-5 for vadose zone. These modeling tools can be used at seasonal to decadal timescales, and their PFOA estimations can be provided as input data to a high throughput physiologically based pharmacokinetic (PBPK) model to estimate human exposure to PFAS. The combination of multi-media modeling system and PBPK model bridges the gaps between PFAS emissions and human exposure estimates and thus can provide the basis for epidemiological studies. This research opens doors to study the association between human exposure to PFAS and specific human diseases.
Speaker Bio:
Kiran Alapaty is the Senior Science Advisor in the Atmospheric & Environmental Systems Modeling Division in the ORD of US EPA. His research interests are in air quality modeling and model development, PFAS life cycle modeling, integrated assessment modeling, convective cloud parameterization development, boundary layer modeling, climate change and exposure science, and socio-economics. In the past, he was the Chief of the Climate Branch of the AMA Division with research interests in improving regional climate data for use with exposure science research.
Before joining EPA in 2011, for several years Kiran was at DOE HQ as a Program Director for the DOE’s national climate program managing DOE’s National Labs and research grants. Prior to that, he was also a Program Director at the National Science Foundation managing climate research at NCAR and academia.
Kiran holds an MS in Aerospace Engineering from the Indian Institute of Sciences and a PhD in Atmospheric Science from North Carolina State University.

About the Lunch & Learn Seminar Series:
A new Bimonthly seminar series from the Department of Civil and Environmental Engineering (CEE), focusing on convergent research, bringing together Northeastern colleagues and collaborators to think big/bold, explore ideas that build cooperation, and foster transformative innovation within CEE and across disciplines beyond CEE.
Due to COVID restrictions, this event remains virtual-only. Please bring your own lunch and join us online.

ECE Faculty Seminar: Sumientra Rampersad
2021-05-07 11:00 - 12:00
Faculty Seminar: Is temporal interference the key to noninvasive deep brain stimulation? Answers from simulation studies in mice and humans.
Sumientra Rampersad
Location: Zoom Link
Abstract: Transcranial current stimulation (tCS) has been used for two decades to noninvasively investigate and influence brain function in both healthy volunteers and clinical populations. While many positive effects have been found, the goals of high focality, accurate targeting and deep stimulation are yet to be achieved. Transcranial temporal interference stimulation (tTIS) is a new form of tCS that might improve the method on all three fronts. tTIS uses two alternating currents to create an amplitude-modulated electric field that can peak deep in the brain. A recent murine study showed promising effects of tTIS and concluded that the technique may be used as a noninvasive form of deep brain stimulation in humans, but results from human experiments have not yet been published. In this talk I will present results of finite element simulations with realistic head models to investigate the electric fields induced by tTIS in the brain, comparing results in murine and human head models for tTIS and conventional tCS. Due to the nonlinear nature of tTIS, conventional methods to optimize tCS fields for a specific brain target cannot be used. I will present two nonconvex optimization methods for tTIS and compare their efficiency and results. Finally, I will discuss the implications of the results of these simulation and optimization studies for potential applications of tTIS in humans.
Bio: Sumientra Rampersad is an Assistant Research Professor in the Department of Electrical and Computer Engineering at Northeastern University in Boston, where she leads the Brain Stimulation & Simulation Lab. Dr. Rampersad’s research aims to improve understanding of the working mechanisms behind neuromodulation and improve its application using computational methods and experiments with human subjects. She investigates invasive (ECoG, sEEG) and noninvasive (tCS, TMS) brain stimulation, as well as peripheral stimulation, and is especially interested in bridging the gap between modeling and experiments through model-based experimentation. Her research in collaboration with various academic and clinical partners has been awarded funding by NIA, NINDS and NIMH. Dr. Rampersad was previously a research scientist in Northeastern’s Cognitive Systems Lab and obtained her PhD at the Radboud University Donders Institute in Nijmegen, the Netherlands.

CEE Seminar: Cycling for Sustainable Cities
2021-05-04 18:30 - 19:30
Please consider joining the Department of Civil and Environmental Engineering for this seminar on sustainable urban travel.
ABSTRACT: Cycling is the most sustainable means of urban travel, practical for most short- and medium-distance trips—commuting to and from work and school, shopping, visiting friends—as well as for recreation and exercise. Cycling promotes physical, social, and mental health, helps reduce car use, enhances mobility and independence, and is economical for both public and personal budgets.
This presentation explores how to make city cycling—the most sustainable means of travel—safe, practical, and convenient for all. Buehler and Pucher discuss the latest cycling trends and policies around the world and consider specific aspects of cycling. Taken together, the presentation demonstrates that successful promotion of cycling depends on a coordinated package of mutually supportive infrastructure, programs, and policies. Cycling should be made feasible for everyone and not limited to especially fit, daring, well-trained cyclists riding expensive bicycles.
Bios:
Ralph Buehler: Ph.D. is Professor and Chair of Urban Affairs and Planning in the School of Public and International Affairs at Virginia Tech’s Research Center in Arlington, VA. Most of his research has an international comparative perspective, contrasting transport and land-use policies, transport systems, and travel behavior in Western Europe and North America. Between 2012 and 2018, he served as chair of the Committee for Bicycle Transportation of the Transportation Research Board (TRB). His research interests include: (1) the influence of transport policy, land use, and socio-demographics on travel behavior; (2) active travel and public health; and (3) public transport demand, supply, regional coordination, and financial efficiency.
John Pucher: Ph.D. is professor emeritus at the Bloustein School of Planning and Public Policy at Rutgers University, in New Jersey. He was a professor at Rutgers University from 1978 to 2014, conducting research on urban transportation in the United States, Canada, Australia, and Europe. Over the past 25 years, John’s research has focused on walking and bicycling, and how to improve their safety and convenience for all age groups, for women as well as men, and for all levels of physical ability. John has published four books and over 200 articles in academic and professional journals. His most recent book, “Cycling for Sustainable Cities,” was published by MIT Press in February 2021.

CommLab Data Visualization Workshop
2021-04-29 17:00 - 18:00
The ability to visually display your data is an integral part of your scientific communication toolbox. In our second Research Dissemination Series workshop, the COE Communication Lab will discuss best practices for clear design and inspire new ideas for designing figures. Data visualization resources and tools will also be shared to effectively communicate the meaning of your data. Register for this event

Distinguished Speaker Series in Robotics
2021-04-29 12:00 - 13:30
We cordially invite you to join the
DISTINGUISHED SPEAKER SERIES IN ROBOTICS
Thursday, April 29, 12:00 – 1:30pm

Virtual Meeting – Zoom Link | Meeting ID: 928 6786 9946 | Passcode: [REDACTED]
https://northeastern.zoom.us/j/92867869946?pwd=[REDACTED]

Manual Skills and Dexterity in Robots and Humans
Aude Billard
Professor of Robotics, Swiss Federal Institute of Technology (EPFL), Switzerland

Part 1: Robots have moved from imitating humans to exceeding humans’ capabilities – sometimes: The design of robots’ manipulation capabilities is driven by our admiration for humans’ exquisite dexterity and motor agility. Yet, robots are far from reproducing the complexity of human cognition, for some skills robots do better than humans. Thanks to their powerful motors and the speed of computation of their computer-driven circuits, robots can beat humans in precision and reactivity. This talk will give an overview of several approaches developed at LASA to endow robots with the ability to react extremely rapidly in the face of unexpected changes in the environment, combining control with dynamical systems and machine learning. We use human demonstrations to guide the design of the controller’s parameters to modulate the compliance and to determine the range of feasible paths. A review of these algorithms will be accompanied with illustrations of their implementation for controlling uni-manual and bi-manual manipulation. I will conclude by showing some examples of super-human capabilities for catching objects with a dexterity that exceeds that of human beings.
Part 2: Understanding bimanual skill – a case study in watchmaking: Human dexterity still eludes largely robotics. In an effort to better understand and model this dexterity, we took on an adventure and decided to follow a cohort of apprentices at watchmaking, a craft unique in its requirement for precise control of finger movements. Precise control of force is also of essence to prevent breakage of the tiny, and often highly valuable, pieces. In a two-year long training, apprentice acquire the ability to go beyond their natural perception of touch, so as to sense when the piece clicks and the screw in. Most impressive is the ability with which they acquire unusual but efficient hand postures. Our study unveils how the two hands work in coordination to distribute control variables and achieve better precision than when using a single hand.
Bio: Aude Billard is professor in robotics at the School of Engineering at the Swiss Federal Institute of Technology in Lausanne (EPFL). Trained in physics and robotics, she has been a pioneer in the application of machine learning for robotic control and human-robot interactions. Billard’s research focuses on manual control and dexterity, inspired by human skill. Her work on robotics and human-robot interactions has been recognized numerous times by the Institute of Electrical and Electronics Engineers (IEEE) and she currently holds a leadership position on the executive committee of the IEEE Robotics and Automation Society (RAS) as the vice president of publication activities.

Presented by the Institute for Experiential Robotics and Action Club

Culturally Responsive & Anti-Racist Teaching: Strategies for Inclusive Excellence
2021-04-29 10:00 - 11:30
Join Dr. Ivonne M. García, Chief DEI Officer at the College of Wooster, for a seminar & workshop that will cover culturally responsive teaching strategies with respect to multilingual learners, and then delve into antiracism as defined by Ibram X. Kendi and the practices that we as educators can incorporate into our classes and our departments. Intensive breakout sessions will provide ample opportunity for participants to think deeply about incorporating antiracist strategies into courses and provide a platform for brainstorming how to engage our students in these conversations.
Although this workshop was designed with faculty in mind, staff are very welcomed to attend – this is going to be a highly interactive event with plenty of takeaways – and even some homework!

For more information and to register for the event please click on the link below:

https://www.eventbrite.com/e/multilingual-learners-strategies-in-teaching-for-inclusive-excellence-tickets-145930499051

Women In Engineering Webinar
2021-04-29 09:00 - 10:00
Join the Graduate School of Engineering for a Women in Engineering Webinar that will take place on April 29th at 9:00AM EST. Please find the registration link below. A recording will be available to those who are unable to attend.
Join link: https://us02web.zoom.us/webinar/register/WN_4ij5_t78QSyEJxC9VPiK6Q

NE GWISE Presents Dr. Adriana Bankston, Science Policy Workshop
2021-04-27 18:00 - 19:30
NE GWiSE is excited to announce the sixth seminar in our “Building Inclusive Communities” 2020-21 virtual series, which will take place on Tuesday, April 27th from 6:00-7:30pm.
This month’s speaker, Dr. Adriana Bankston, is a Principal Legislative Analyst in the University of California (UC) Office of Federal Governmental Relations, where she serves as an advocate for UC with Congress, the Administration, and federal agencies. She’s also the new CEO and Managing Publisher of the Journal of Science Policy & Governance.
During this interactive workshop, Dr. Bankston will cover effective strategies for graduate students to communicate their research to policymakers, both orally and in writing. She will discuss different types of policy writing and how to tailor your message to a specific audience within the current political context. The session will also provide opportunities to practice speaking and writing on a policy topic of your choice.
The event is free to attend and open to all genders. It will be held on Tuesday, April 27th from 6:00-7:30PM EST. Learn more about the event and RSVP!

ECE PhD Dissertation Defense: Yue Zheng
2021-04-27 15:00 - 16:00
PhD Dissertation Defense: Modular Plug-and-Play Photovoltaic Subpanel System
Yue Zheng
Location: Zoom Link
Abstract: This thesis designs, builds and tests plug-and-play photovoltaic (PV) panels. A prototype modular PV system is built consisting of a dozen small PV units that can slide in and out of a mechanical frame without impacting other units. Each unit contains one PV subpanel and a DC-DC converter with a distributed maximum power point tracking (dMPPT) control board. Each PV unit works at its maximum power, while every output of the converter is connected in parallel to a DC bus. A new combined control strategy is proposed in which the decision to use centralized or distributed control depends on the system efficiency at the varying load operating points. A disadvantage of this dMPPT structure is that in each PV unit, the DC-DC converter must convert the entire power from its PV subpanel. Therefore, this research also explores the use of Differential Power Processing (DPP) system, which harvests maximum power while only processing a small amount of power due to the mismatches between PV panels. Thus, DPP structure reduces power loss compared to traditional dMPPT structure. Since it processes only a small amount of power, differential power processing structure has the potential to further be integrated on a chip and become installed in the junction box during the assembling process. Finally, the research proposes to implement the plug-and-play features of the solar PV system using wireless power transfer (WPT) instead of hard wire connectors. A series-to-series topology of WPT system (L-R-C series circuit) for one PV unit is proposed. In this system, the DC-DC converter on the PV side is used to perform MPPT, while the DC-AC inverter simultaneously perturbs its switching frequency to match possible variations in resonance frequencies. Wireless communication is used between transmitter and receiver. Thus, the maximum efficiency point on the constant output voltage trajectory can be tracked dynamically under wide and varying operating conditions.

Rare Earth Element-Based Magnets: Science, Supply and Sustainability in 2021 and Beyond
2021-04-27 11:00 - 12:00
University Distinguished Professor Vincent Harris is presenting “Rare Earth Element-Based Magnets: Science, Supply and Sustainability in 2021 and Beyond” as part of the Jefferson Science Fellowship Program of the National Academies of Sciences and Engineering.
Registration is required in advance of the lecture: Register here
Rare earth elements (REEs) and their supply chain have become topics of great interest to the U.S. diplomatic and national security communities. Presently, China dominates REE markets in all facets of processing from earth extraction to metals as well as value and commercialization verticals. Beijing has shown no hesitancy in using its position of market dominance to advance its broader political goals and agendas.
In this presentation, we focus on REE-based magnets and associated challenges faced in 2021. We explore REE science and applications, supply and policy, and sustainability and environmental impact. We examine what the future holds in terms of alternative sources, recycling, and the practice of designing components around the need to employ REEs. Finally, we report on steps taken by the global community to offset China’s monopoly on rare earths.

ECE MS Thesis Defense: Anran Wei
2021-04-26 10:00 - 11:00
MS Thesis Defense: A soft-switching non-inverting buck-boost converter
Anran Wei
Location: Zoom Link
Abstract: There are numerous applications in which DC-DC converters with wide range of voltage gain are required. Non-inverting buck-boost converter is a classical topology that can provide wide range of voltage conversion and bidirectional power transfer; thus, it is frequently used in industrial applications. However, the conventional hard-switching configuration, which transfers power through a link inductor, can only reach a high voltage conversion ratio at the expense of low efficiency due to switching loss. This thesis proposes a soft switching non-inverting buck-boost converter. This converter uses a small film capacitor in parallel with the link inductor to provide zero voltage switching (ZVS) by allowing the link capacitor and link inductor resonate between power transfer states. Principles of the operation of this converter are presented in this thesis and its performance is evaluated through simulations and experiments.

ECE PhD Dissertation Defense: Lichen Wang
2021-04-23 13:00 - 14:00
PhD Dissertation Defense: Correlation Discovery for Multi-view and Multi-label Learning
Lichen Wang
Location: Zoom Link
Abstract: Correlation indicates the interactions or connections across different instances. It exists in a wide range of real-world applications such as social network, scene understanding, and time-series data analysis. Correlation provides the unique and informative knowledge to reveal the connections across instances, and it plays an essential and important role in machine learning field. However, recovering and utilizing correlation is challenging. First, it is hard to explicitly define and understand the correlations. Second, there are not sufficient datasets which contain the well-labeled task-specific correlations. Third, how to efficiently utilize the learned correlations for other down-stream tasks have not been well-explored.
In this dissertation research, we investigate the techniques to effectively discover various kinds of correlations in machine learning tasks including multi-view learning, multi-label learning, image/scene understanding, time-series data analysis, human action recognition, and graph representation learning. Specifically, we propose algorithms from the following perspectives: (1) designing an advanced correlation discovery network to automatically explore the label correlations in multi-label scenarios, (2) proposing a multi-view fusion strategy which effectively dig the latent correlations across different views, (3) exploring the correlations and structural knowledge from graph structured objects in an inductive and unsupervised scenario. To demonstrate the effectiveness of the proposed algorithms, various experiments on commonly used datasets have been implemented and the results shows the superiority of our algorithms over the other state-of-the-art methods.

Plant Shift Initiative | Speaker Series: Engineering & Climate Correction
2021-04-22 15:00 - 16:30
There is no debate that in order to combat climate change we need to think of creative solutions. Join us on Earth Day, April 22nd, to hear more about the Plant Shift Initiative which is dedicated to spark new “plant-based” ideas in all forms of productions and activities.
For decades, entrepreneurs and leaders have invented new systems to minimize the carbon footprint in every part of our lives. To kick off this series, we will hear from disrupters Sebastiano Cossia Castiglioni, PNT’23, Co-Founder and Director of Natural Order Acquisition Corp., Dale Vince, Founder of Ecotricity, and Paul Watson, Founder and Chief Executive Officer of Sea Shepherd. They will share with you how they engineered new designs and systems within their industries to be more plant-based.
Register

Order of the Engineer Ring Ceremony
2021-04-22 14:00 - 15:00
This year the College of Engineering is pleased to invite the Class of 2020 to join the Class of 2021 in a combined virtual Order of the Engineer Ring Ceremony. Eligible students will be inducted into the Order, receive a ring, and a certificate.
The Order of the Engineer is an organization composed of engineers within the United States who have publicly accepted the obligation of an Engineer, a formal statement of an engineer’s responsibilities to both the public and the profession. As part of this ceremony, you will receive a stainless-steel ring, which symbolizes and reminds engineers of their obligation to serve the public and demonstrates the common bond engineers share.
Zoom link: https://northeastern.zoom.us/j/94311514917
