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

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

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

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

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

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

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

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

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

ECE PhD Proposal Review: Hamed Mohebbi Kalkhoran
2022-01-20 15:30 - 16:30
PhD Proposal Review: Machine Learning Approaches for Classification of Myriad Underwater Acoustic Events Over Continental-shelf Scale Regions with Passive Ocean Acoustic Waveguide Remote Sensing
Hamed Mohebbi Kalkhoran
Location: Zoom Link
Abstract: Underwater acoustic data contain a myriad of sound sources that include bioacoustics related to marine life such as marine mammals and fishes; man-made such as ships, sonar, and airguns; as well as natural geophysical processes such as earthquake, hurricane, and volcanic eruption. Among underwater acoustic events, marine mammal vocalization classification is one of the most challenging problems due to their transient broadband calls, high variation in the calls of a specie (intra-class variation), and high similarity between the calls of some species. In this thesis, we investigate machine learning approaches for classifying marine mammal vocalizations for real-time applications. We utilize acoustic data from a 160-element coherent hydrophone array and employ the passive ocean acoustic waveguide remote sensing technique to enable sensing and detections over instantaneous wide areas more than 100 km in diameter from the array. A variety of computational accelerating approaches, combining hardware and software, that make the methods desirable for real-time applications are also developed.
Humpback whale behavior, population distribution and structure can be inferred from long term underwater passive acoustic monitoring of their vocalizations. Here we employ machine learning approaches to classify humpback whale vocalizations into song and non-song calls. We use wavelet signal denoising and coherent array processing to enhance the signal-to-noise ratio. To build features vector for every time sequence of the beamformed signals, we employ Bag of Words approach to time-frequency features. Finally, we apply Support Vector Machine (SVM), Neural Networks, and Naive Bayes to classify the acoustic data and compare their performances. Best results are obtained using Mel Frequency Cepstrum Coefficient (MFCC) features and SVM which leads to 94% accuracy and 72.73% F1-score for humpback whale song versus non-song vocalization classification.
To classify a large variety of whale species by their calls, we extracted time-frequency features from Power Spectrogram Density (PSD) of the beamformed signals. Then we used these features to train three classifiers, which are SVM, Neural Networks, and Random forest to classify six whale species: Fin, Sei, Blue, Minke, Humpback, and general Odontocetes. Best results were obtained with Random forest classifier, which achieved 95% accuracy, and 85% F1 score. To detect transient sound sources, first we applied Per-Channel Energy Normalization (PCEN) on the PSD of the beamformed signals. We applied thresholding on the PCEN data followed by morphological image opening to find potential sound sources and reduce noisy detections. Then we applied connected component analysis to obtain the final detected sounds for each bearing. To estimate the Direction of Arrival (DoA) of detected sounds, we applied non-maximum suppression (NMS), which is widely used in object detection applications in computer vision, on the detected sounds. We used mean power of each detected sound as the scores for NMS. To speed up the data processing, we investigated a variety of accelerating approaches, such as analyzing the effect of floating point precision, applying parallel processing, and implementing fast algorithms to run on GPU.

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

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

COE Global Co-op Info Session
2022-01-25 12:00 - 13:00, 2022-02-03 12:00 - 13:00 | Curry Student Center, 360 Huntington Ave., Boston, MA, 02115, United States
Join the College of Engineering Global Co-op team in learning about global co-op opportunities for Summer II/Fall 2022.
Topics discussed will include:

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

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

Materials Exhibiting Biomimetic Carbon Fixation: Kinetic Analysis, Mechanistic Insights, and Material Design
2022-01-26 12:00 - 13:00 | 024 East Village, 360 Huntington Ave, Boston, MA, 02115, United States
ChE Seminar Series Presents:
Dorsa Parviz, Ph.D.
Department of Chemical Engineering, Massachusetts Institute of Technology
Abstract:
Population growth and climate change necessitate a paradigm shift from current chemical and materials production methods to more sustainable approaches with a negative carbon footprint. In view of this, I will introduce carbon fixing materials (CFM) as a new synthetic platform that, like plants, utilize sunlight to photocatalytically reduce ambient CO2 and add to an ever-extending carbon backbone. First, I will describe a mathematical framework enveloping the main functions of carbon fixing materials to answer basic questions about the kinetics regimes of operation, photocatalytic requirements, and limits of functional materials in CFMs. I will also present mechanistic insights on the photocatalytic reduction of CO2 to C1 intermediates as desired intermediates for producing value-added products from CO2. In the second part of my talk, I will focus on state-of-the-art 2D nanomaterials and strategies for surface engineering these materials in the colloidal state, addressing challenges in their characterization for applications in photocatalysis.
Bio:
Dorsa Parviz is a postdoctoral researcher at the Massachusetts Institute of Technology, working with Prof. Michael Strano in the Department of Chemical Engineering. She earned her Ph.D. in 2016 from Texas A&M University under the guidance of Prof. Micah Green, where she pioneered techniques for high-yield production of 2D nanomaterials, investigated their colloidal interactions and assembly, and designed tailored nanosheet-based polymer composites and 3D networks for structural and electrode applications. During her postdoc, she developed carbon fixing materials at MIT, establishing a high-throughput photocatalytic reaction screening system to accomplish this vision. In addition, she has led the research on the preparation and characterization of biocompatible engineered 2D nanomaterials with tailored structure and properties for nanotoxicity studies at NIEHS Nanosafety Center.
If unable to attend in person, please contact a.ramsey@northeastern.edu for the seminar link.

ECE PhD Proposal Review: Meruyert Assylbekova
2022-01-27 11:00 - 12:00
PhD Proposal Review: Aluminum Nitride and Scandium-doped Aluminum Nitride Materials and Devices for Beyond 6 GHz Communication
Meruyert Assylbekova
Location: Zoom Link
Abstract: With almost all of the sub-6 GHz spectrum now being allocated, current bandwidth shortage has motivated the exploration of untapped frequencies beyond 6 GHz for future broadband wireless communication. Shift to higher frequency spectra is expected to deliver a significant performance improvement in network capacity, data rates, latency, and coverage. These refinements will enable the development of new lifechanging technologies such as Vehicle to Everything (V2V to V2X), ubiquitous Internet of Things (IoT), and Augmented and Virtual reality (AR and VR). Among a variety of novel 5G applications, the implementation of 5G mobile broadband imposes especially demanding specifications on Radio Frequency FrontEnd (RFFE) architectures. 5G smartphones are expected to carry over the legacy sub-6 GHz bands, which translates into an increased number of filters.
In this context, the first part of this work will introduce lithographically defined Aluminum Nitride (AlN) piezoelectric microacoustic resonators as a promising solution for the implementation of future minituarized adaptive RFFEs.
While AlN has been a material of choice for acoustic filters for over two decades, future technologies are calling for a material with superior piezoelectric strength. It has been shown that the piezoelectric activity of AlN can be enhanced by partially substituting Al with Sc to form AlScN. Thus, the second part of this work will explore material properties of AlScN along with the challenges that need to be addressed to take full advantage of its piezoelectric and ferroelectric strength. Last, AlScN resonators and filters will be demonstrated as promising candidates for the future beyond 6GHz technologies.

COE Global Co-op Info Session
2022-01-27 17:00 - 18:00 | Curry Student Center, 360 Huntington Ave., Boston, MA, 02115, United States
Join the College of Engineering Global Co-op team in learning about global co-op opportunities for Summer II/Fall 2022.
Topics discussed will include:

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

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

How to Make Compelling Figures- a Data Visualization Workshop
2022-01-27 17:00 - 18:00
In this interactive virtual workshop, we’ll walk you through the steps of creating and revising compelling data visualizations and graphics.
To get the most out of the workshop, please bring a visualization that you would like to improve and use in your own work. The visualization can be anything from a table of numbers, to a graph, to an illustration or diagram. The visualization does not have to be polished and can even be an informal sketch of a visual you would like to make in the future.
Click HERE to Register via Zoom
This workshop is sponsored by the CommLab and presented by Kate Kryder, the Data Analysis and Visualization Specialist at Northeastern University Library.

Galante Engineering Business Program Info Session
2022-01-27 17:30 - 19:00
Northeastern University’s Galante Engineering Business Program offers a progressive opportunity for engineering students to complement their technical engineering education with business skills by earning a graduate certificate in engineering business. Galante is founded on the values of student engagement and leadership to strengthen interpersonal and professional skills. Programmatic elements are offered to students such as workshops, speaker series, site visits, seminars, and other related personal and professional development activities as a connected cohort.
The Info Session Event is an opportunity for CoE students to learn about the Galante Engineering Business Program, the opportunities it provides, the benefits offered, the application process, and more. This event will be hosted on Thursday, January 27th (01/27/22) from 5:30-7:00pm in Egan 440. Attire is business casual. Please be sure to RSVP, and please be sure to reach out to Program Assistant Bradley Miller (b.miller@northeastern.edu) for questions and additional information, or visit our website.

Graduate Student of Color Collective 6th Annual Multicultural Mixer
2022-01-28 18:00 - 20:00
Mark your calendars for a cultural mix and mingle! GSCC will be hosting its 6th Annual Multicultural Mixer on Friday, January 28, 2022.

ECE Seminar: Michael Everett
2022-01-31 13:00 - 14:00 | 442 Dana, 360 Huntington Ave, 442 DA, Boston, MA, 02115, United States
ECE Seminar: Deployable Learning Machines: From cost-to-go estimation to certification
Michael Everett
Location: 442 Dana and Zoom Link
Abstract: Autonomous robots have the potential to transform our everyday lives, yet most of these systems struggle outside of the lab or carefully designed warehouses. This talk will first describe our work toward a new generation of robots that learn to handle the highly dynamic and uncertain nature of human environments. In particular, I will highlight the importance of obtaining accurate cost-to-go models, which we show can be learned from self-play or aerial imagery for a variety of applications, from navigation among pedestrians to last-mile delivery. The talk will then dive into the challenges of certifying the safety and robustness properties of machines that learn. I will describe our work that uses convex relaxations and set partitioning to simplify the analysis of highly nonlinear neural networks used across AI. These analysis tools led to the first framework for deep reinforcement learning that is certifiably robust to adversarial attacks and noisy sensor data. The tools also enable reachability analysis — the calculation of all states that a system could reach in the future — for systems that employ neural networks in the feedback loop, which provides another notion of safety for learning machines that interact with uncertain environments. Finally, I will discuss my long-term vision that aims to spark a new era of learning machines that can be deployed in any environment without human supervision.
Bio: Michael Everett is currently a Research Scientist in the Department of Aeronautics and Astronautics at the Massachusetts Institute of Technology (MIT). He received the S.B., S.M., and Ph.D. degrees in mechanical engineering in 2015, 2017, and 2020, respectively, at MIT. His research lies at the intersection of machine learning, robotics, and control theory. His papers have been recognized as one of the Editors’ Top 5 Articles of 2021 in IEEE Access, Best Paper Award on Cognitive Robotics at IROS 2019, Best Student Paper Award and Finalist for Best Paper Award on Cognitive Robotics at IROS 2017, and Finalist for Best Multi-Robot Systems Paper Award at ICRA 2017. He has been interviewed live on the air by BBC Radio and his team’s robots were featured by Today Show and the Boston Globe.

Platinum: Not as Noble as We Thought
2022-02-02 12:00 - 13:00 | 024 East Village, 360 Huntington Ave, Boston, MA, 02115, United States
ChE Seminar Series Presents:
Arthur Shih, Ph.D.
Leiden Institute of Chemistry, Leiden University, The Netherlands
Abstract
Understanding of catalysis at a fundamental level has historically lagged behind its commercial counterpart with the Haber-Bosch ammonia synthesis process and catalytic converters as pertinent examples [1]. This historical paradigm, however, is shifting with the advancement of computing prowess and collaboration. We will discuss how experiments and density functional theory (DFT) computations led us to discover that platinum, a noble metal that is frequently utilized as a catalyst in the cathode of fuel cells, restructures when the voltage is held constant between fuel-cell relevant voltages of 0.6 and 1.0 V on a reversible hydrogen electrode scale (VRHE) [2].
An anomalous reduction feature at ~0.53 VRHE was observed on a Pt(111) single crystal in Ar-saturated HClO4 after holding at the fuel-cell relevant voltage of 0.8 VRHE (Figure 1). Decades of research has established that Pt(111) in HClO4 oxidizes H2O to adsorbed *OH between 0.6 and 1.0 VRHE [3-5] and this current model is unable to explain the anomalous feature. Using a combination of computational, electrochemical, spectroscopic, and imaging probes, we find that holding the voltage between 0.6 and 1.0 VRHE results in a mildly-roughened Pt(111) surface [6], presumably due to an *OH-induced release of surface stress. The catalytic performance of this mildly roughened Pt(111) was tested for the oxygen reduction reaction (ORR) and carbon monoxide oxidation (CO Oxidation) where it was found that the ORR rate is seemingly structure insensitive and CO Oxidation rate is surprisingly structure sensitive [7]. Overall, this discovery demonstrates the importance of understanding how dynamic and steady operating conditions influence the electrode-electrolyte interface – critical for predicting, designing, and improving current commercial technologies and opening doors for the development of future technologies.
Bio
Arthur Shih’s research interests are in catalysis for the sustainable production of chemicals and energy, with emphasis on utilizing reaction kinetics and spectroscopy to understand catalytic mechanisms. He obtained his bachelor’s in Chemical Engineering from the University of Michigan during which he developed computer-based resources with H. Scott Fogler for his textbook “Elements of Chemical Reaction Engineering” and explored several research areas ranging from cancer detection to polymers to CO2 capture. He then earned his Ph.D., also in Chemical Engineering, from Purdue University with Fabio H. Ribeiro where he investigated the thermal-catalytic reduction of toxic nitrogen oxides in catalytic converters. Inspired by the growth and prowess of computational chemistry coupled with a desire to capitalize on cheap renewable electricity for the environment, he then moved to Leiden University and completed a postdoc in Chemistry with Marc Koper on the electrocatalysis of water splitting to H2 and O2 over well-defined single crystal electrodes. During that time he collaborated with several computational chemists around the world. He is currently a postdoctoral scholar in Materials Science and Engineering at Northwestern University with Sossina Haile working on nitride catalysts for high temperature electrochemical ammonia synthesis.
If unable to attend in person, please contact a.ramsey@northeastern.edu for the link.

COE Global Co-op Info Session
2022-02-02 17:30 - 18:30
Join the College of Engineering Global Co-op team in learning about global co-op opportunities for Summer II/Fall 2022.
Topics discussed will include:

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

Attendance to one of these sessions is required if you plan to do a global co-op in Summer II/Fall 2022.
RSVP on the NUworks Events Calendar. This session will be virtual.
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.

What’s Next? Career Conversations with NUCEAO
2022-02-03 12:00 - 13:00
Do you have questions about life after graduation? Northeastern Civil Engineering Alumni Organization (NUCEAO) is offering a virtual lunchtime “office hours” series for students and young alumni to chat and network with professionals who are actively working in the Civil Engineering and Construction fields.
Each session we will be focusing on a different topic, but feel free to bring specific questions for our panelists! You can sign up for as many hours as you like.
Switching Concentrations After Graduation
February 3, 2022 – 12:00 PM-1:00 PM ET
Hear from NUCEAO board members on how they switched concentrations within civil engineering:
Dan Hack, E’16, Turner Construction Company
Kayla Sousa, E’15, PE, Howard Stein Hudson
Ezgi Talarico, E’14, MBA’21, Gilbane Building Company
Relocating for Work
February 17, 2022 – 12:00 PM-1:00 PM ET
Hear from NUCEAO board members on how they relocated for work:
Sam Kinnaly, E’20, MS’20, Suffolk Construction
Ryan St. Martin, E’08, JB Pacific
Ezgi Talarico, E’14, MBA’21, Gilbane Building Company
Acquiring Professional Licensure
March 3, 2022 – 12:00 PM-1:00 PM ET
Questions about obtaining professional licenses and exams like the PE and LEED? Ask NUCEAO!
Anna Beheshti, E’15, PE, Arup
Teja Pulla, MS’18, LEED AP BDC, CCT, CMIT, Drone Pilot, AECOM
Mike Tecci, E’03, MS’08, PE/SE LEED GA, Simpson Gumpertz & Heger Inc.
Graduate School
March 10, 2022 – 12:00 PM-1:00 PM ET
Hear from NUCEAO board members about their experiences applying to and completing graduate school:
Allie Goldberg, E’15, Weston & Sampson
Ashley Kocsis, MS’15, PE, Greenman-Pedersen, Inc.
Ezgi Talarico, E’14, MBA’21, Gilbane Building Company
Closed Captioning. Live closed captioning services for this event can be made available upon advanced request (2 weeks).
These events are complimentary but registration is required.
All registrants will receive an email with information on accessing this virtual event.
Register

Creating a Poster-A CommLab Virtual Workshop
2022-02-03 17:00 - 18:00
Join the CommLab for an interactive workshop on creating a poster. We will help you craft your story, effectively plan for your audience and give you tips for mindful poster design. Bring a poster at any stage of progress to get feedback from the CommLab Fellows or bring your ideas to get help on sketching out your design. This is a great opportunity to get ready for your next conference or the COE PhD Expo on March 3.
Register for Zoom link.

Invisible disabilities: Mental health & navigating higher education
2022-02-04 15:00 - 16:00
Join Professors Christopher Parsons & Melissa Ferrick to learn how to navigate a career in higher education with invisible disabilities such as mental health on February 4th, 3 p.m. to 4 p.m. at ISEC Room 655, 6th floor or on Zoom at https://northeastern.zoom.us/j/2099280142

Engineering at NERF - First Year Engineering Speaker Series
2022-02-04 17:00 - 18:00 | 103 Churchill, 103 Churchill Hall, 360 Huntington Ave, Boston, MA, 02115, United States
When: Friday February 4th 5pm-6pm
Where: Churchill 103
Who: Justin Saccone, Sr. Engineer @ Nerf (under Hasbro)

Library Webinar: Introduction to Citation Managers
2022-02-07 12:00 - 12:45, 2022-02-09 12:00 - 12:45
Start your spring 2022 research off on the right foot with Northeastern University Library’s series of online workshops and webinars!
In this webinar, learn how to manage your references, organize your research and quickly create citations and bibliographies using a citation management program.
The session will cover: an introduction to EndNote, Refworks, Mendeley and Zotero; similarities and differences between different citation management programs; how to choose the right citation manager for you; and how to install and use a citation manager.
Note: it is recommended that you choose one citation manager to use, as they are not meant to be used together.
Register here: bit.ly/citationmgmtworkshops
All times are in Eastern Time.

ECE Seminar: Sunwoo Lee
2022-02-08 11:00 - 12:20 | 442 Dana, 360 Huntington Ave, 442 DA, Boston, MA, 02115, United States
ECE Seminar: Autonomous Microsystems Based on Heterogeneously Integrated CMOS for Biological Big Data
Sunwoo Lee
Location: 442 Dana or Zoom Link
Abstract: Minimally invasive and chronic physiological monitoring can provide an effective means of disease prevention and early detection while the cumulative big data can unveil hidden patterns in our physiology. Yet, current physiological monitoring tools are often bulky, invasive, and expensive, limiting their sensitivity and applicability. In this talk, I will discuss autonomous microsystems based on heterogeneously integrated CMOS, a platform on which ideal physiological sensors and actuators can be built.
A micro-scale optoelectronically transduced electrode (MOTE), an exemplary microsystem I have designed and built for tetherless neural recording, is powered and communicates optically through a vertically integrated AlGaAs micro-scale light emitting diode (µLED), eliminating the needs for a battery or a RF coil; the MOTE is smaller than a human hair (~60 µm × 30 µm × 330 µm) and weighs about one 1 µg (cf. a grain of sand is about 670 µg). I will review the unique challenges and considerations in developing such heterogeneous systems in terms of device fabrication, circuit design, integration, and handling/manipulation.
While the MOTE is designed for neural recording, its design methodologies can also be used to monitor other physiological parameters such as temperature, pH, glucose-level, etc. I will introduce future autonomous microsystems with expanded modalities and how to interface them with existing wearables. As such microsystems become more accessible, the resulting biological big data will help enable personalized healthcare and produce a physiological ‘digital twin’ (like the architectural digital twins of select cities) that can add a new dimension to epidemiological and aging studies.
Bio: Sunwoo Lee (Member, IEEE) received the B.S. degree in Electrical and Computer Engineering from Cornell University, Ithaca, NY in 2010, and the M.S. and Ph.D. degrees in Electrical Engineering from Columbia University, New York, NY in 2012 and 2016, respectively, working on graphene synthesis and graphene-based nano-electro-mechanical systems for signal processing and sensing applications. In 2016, he joined the Molnar Group in the School of Electrical and Computer Engineering at Cornell University as a post-doctoral researcher and has been working on heterogeneously integrated CMOS for physiological monitoring. Sunwoo was a recipient of Qualcomm Innovation Fellowship (QInF) 2012 as well as QInF 2013, and a recipient of Pi-Star Award for Young Researcher Presentation at CARBONHAGEN 2015.

ECE Seminar: Qiushi Guo
2022-02-09 10:30 - 12:00 | 442 Dana, 360 Huntington Ave, 442 DA, Boston, MA, 02115, United States
ECE Seminar: Emergent Active Photonic Platforms for Next-generation Mid-infrared and Ultrafast Photonics
Qiushi Guo
Location: 442 Dana or Zoom Link
Abstract: As two basic properties of light, wavelength and timescale are central to numerous photonic applications. Compared to visible and near-infrared, the longer wavelength mid-infrared spectral regime contains unique thermal visual information and chemical fingerprints of the environment. On a different front, femtosecond light sources and systems can enable ultrafast information processing, sensing, and computing. Yet, current chip-scale photonic devices and systems are facing tremendous challenges in detecting, generating, and processing light of long wavelength and ultrashort timescale. Overcoming these challenges requires new materials and clever device architectures, and these technologies stand poised to revolutionize fields such as biomedical sensing, free-space communication, and photonic computing in both classical and quantum domains.
In this talk, I will show that by engineering the carrier and nonlinear dynamics in emergent active photonic materials, we can detect photons beyond the regimes accessible to conventional laser sources and detectors, and process information in an ultrafast manner. In the first half of my talk, I will first briefly introduce the discovery of black phosphorus (BP) mid-infrared photonics, highlighting the world’s first BP mid-infrared detectors with high internal gain, as well as BP’s electrically tunable spectral response due to its unique bandgap tunability. Then, I will discuss a new strategy for detecting longer wavelength mid-infrared radiations at 12 µm. This is achieved by harnessing the intrinsic mid-infrared plasmons in large-scale graphene.
The second half of my talk will cover my recent work on integrated lithium niobate (LN) ultrafast photonics in both classical and quantum domains. I will discuss the realization of ultra-strong nonlinear optical interactions and dynamics in dispersion-engineered and quasi-phase-matched integrated LN devices, which have enabled 100 dB/cm optical parametric amplification, ultra-wide bandwidth quantum squeezing, as well as femtosecond and femtojoule all-optical switching. Finally, I will outline promising pathways toward realizing chip-scale ultrafast light sources and microsystems for on-chip spectroscopic sensing, mid-infrared free-space communication, coherent all-optical computing, and next-generation thermal vision technologies.
Bio: Dr. Qiushi Guo is currently a postdoctoral scholar at the California Institute of Technology with Prof. Alireza Marandi. He received his Ph.D. in Electrical Engineering from Yale University in Dec. 2019, advised by Prof. Fengnian Xia. He received his M.S. degree in Electrical Engineering from the University of Pennsylvania in 2014, and his B.S. degree in Electrical Engineering from Xi’an Jiaotong University in 2012. Qiushi is the winner of the 2021 Henry Prentiss Becton Graduate Prize for his exceptional research achievements at Yale University. His research interests include integrated nonlinear and quantum photonics, mid-infrared photonics, and 2-D materials optoelectronics. He has published 36 peer-reviewed research papers in leading scientific journals with citations more than 2700 times. He is serving on the editorial board of the journal Micromachines.

Spring 2022 Study Abroad Info Session
2022-02-09 12:00 - 13:00
This Study Abroad Info Session is designed to introduce you to the wonders of studying abroad. Listen to students talk of past experiences. Representatives from both COE Undergraduate Academic Advising and Global Experience Office will be on hand to provide the details you need for this exciting opportunity. This event will be on Wednesday, Feb. 9th 2022 from 12:00-1:00pm in 458 RI.
