Northeastern University College of Engineering

Virtual Heads Up Lunchtime Funtime
2020-10-01 12:00 - 13:00 (America/New_York)
Graduate Student Services is hosting a Heads Up Virtual “lunchtime funtime” event for Master’s students to get to know each other and play an exciting competition against each other on October 1st, 12-1pm EST! Heads Up is a game where one student in a group will have to guess 10 words within a category with the help of their group members. You will need to register via the Zoom link we will send out shortly to students to attend the event.

Bioengineering Works in Progress Student Seminar Series
2020-10-02 12:00 - 13:00 (America/New_York)
This virtual seminar series is an opportunity for Bioengineering graduate students to present their research. The first presenter is Shira Tsour from the Slavov lab. Title: “Quantifying rates of mistranslation”. The second presenter is Richard Huffman from the Slavov lab. Title: “Increased Sensitivity, Reliability, and Consistency of Single-Cell Proteomics Measurements via Targeted LC-MS/MS Methods”. Please email Danielle at d.freshnock@northeastern.edu for the link to the seminar.

Rose-Hulman Institute of Technology Virtual Fall Career Fair
2020-10-05 10:00 - 15:00 (America/New_York)
Join the Graduate Admissions team at the Rose Hulman Institute of Technology Virtual Fall Career Fair. They will be available to answer your admissions and program questions. This event will take place on October 5 from 10:00-3:00pm virtually.

Big Ten + Graduate School Expo
2020-10-05 10:00 - 15:00 (America/New_York)
Join the Graduate Admissions team at the Big Ten + Graduate School Expo hosted by Purdue University. They will be available to answer your admissions and program questions. This event will take place on October 5th virtually.

Getting Started with EndNote for Windows
2020-10-05 12:00 - 12:45 (America/New_York)
Start your fall 2020 research off on the right foot with Snell Library’s series of online workshops about citation management! In this session, learn how to use EndNote for Windows to help you manage citations for yourself or your research group.
Register here: bit.ly/citationmgmtworkshops

ECE PhD Dissertation Defense: Mahsa Bayati
2020-10-06 12:00 (America/New_York)
PhD Defense: Efficient Data Access with Heterogeneous Computing using GPUs and Direct Non-volatile Storage.
Mahsa Bayati
Location: Zoom Link
Abstract: The amount of data being collected that requires analysis is growing at an exponential rate. Along with this growth comes an increasing need for storage and computation. Researchers address these needs by (I) deploying distributed bigdata platforms equipped with cutting-edge storage devices, and (II) building heterogeneous clusters with Central Processing Units (CPUs) and computational accelerators such as Graphics Processing Units (GPUs). The high performance of these mainstream systems is achieved by efficiently accessing data and computation resources and scheduling parallel and distributed tasks.
The performance of each job depends on the characteristics of both the application and the underlying storage and computational environments. However, it is not a trivia to maintain efficiency and provide high performance in these mainstream systems. First, in bigdata platforms like Spark and Hadoop, full utilization of Solid State Devices (SSDs), i.e., Non-Volatile Memory Express (NVMe) and Key-Value (KV) SSDs is challenging. Data communication between Spark tasks, levels of parallelism, and resource co-location significantly affects achieving high I/O throughput. Second, in heterogeneous systems, one of the main bottlenecks of GPU computation is the data transfer bandwidth to GPUs in I/O intensive applications. The traditional GPU approach gets data from host memory, which can limit data throughput and processing and thus degrade end-to-end performance. In this work, we initially explore different attributes to exploit the full benefits of various SSDs in bigdata platforms. We then focus on mitigating the data transfer bottleneck in a heterogeneous bigdata framework.
We build a heterogeneous framework that facilitates GPU direct access to storage. Our framework aims to minimize the data transfer delay, thus enhancing the performance of distributed and parallel tasks to obtain the full benefits of compute and storage resources. Our heterogeneous cluster is supplied with CPUs and GPUs as computing resources and non-volatile flash-based drives as storage resources. We also deploy the Spark bigdata platform to execute large workloads over our cluster. We then adopt a novel technique (e.g., Peer-to-Peer Direct Memory Access) to connect GPUs to non-volatile storage directly. Experimental results reveal that our heterogeneous Spark platform successfully bypasses the host memory and enables GPUs to communicate directly to the NVMe drive, thus achieving higher data transfer throughput. The contributions of the dissertation are: (I) Realizing that bigdata processing applications need to consider framework features and application characteristics to fully utilize the high bandwidth of modern SSDs, where compute and storage locality is essential to optimize the cost and performance. (II) Deploying our novel heterogeneous framework supporting GPU direct storage access improves data communication time around 35%- 50% and end-to-end performance by 30%.

Galante Info Session
2020-10-06 18:00 - 19:00 (America/New_York)
Join the Galante Engineering Business Program for the first virtual info session of the semester on Tuesday, October 6th from 6:00pm – 7:00pm via Zoom. During this meeting, you will listen to a short presentation and also hear from a team of panelists that is made up of current students in the program. This is a great opportunity to ask questions and learn more about the various benefits of the program. Please RSVP by clicking this Link.

Olin College Fall Career Fair
2020-10-07
Join the Graduate Admissions team at the Olin College Fall Career Fair. They will be available to answer your admissions and program questions. This event will take place on October 7, 2020, virtually.

ChE Seminar Series Presents: “Engineering the intestinal and retinal microenvironments”
2020-10-07 12:00 - 12:30 (America/New_York)
ChE Seminar Series Presents:

Rebecca Carrier, Ph.D.
Professor & Associate Chair of Research, Department of Chemical Engineering
Affiliated Faculty, Bioengineering, Northeastern University, Boston, MA
“Engineering the intestinal and retinal microenvironments”
Abstract: The Advanced Drug Delivery Lab conducts research in regenerative medicine, with a focus on intestinal and retinal tissue engineering, and in oral drug delivery, with a focus on enabling effective oral delivery of therapeutics. In the area of enabling effective oral drug delivery, we have developed an experimental and theoretical framework to predict the impact of ingested lipids, in food or drug delivery systems, on oral drug absorption. This work could have a significant impact on societal health by providing practical, relevant guidance for the oral dosing of drugs and nutritive supplements. As part of our efforts in enabling effective oral treatments, we have explored the barrier properties of the gastrointestinal mucus barrier, and revealed changes in the mucus barrier certain disease states. The lab is now exploring how to alter mucus barrier properties to potentially prevent certain diseases, including necrotizing enterocolitis. In the area of intestinal tissue engineering, we are developing human in vitro models of the microbiome-gut-immune axis for understanding the impact of what we ingest and the microbiome on human health. We are also developing biomaterial cell carriers for cell-therapy based retinal regeneration strategies, with a focus on engineering cues into biomaterial carriers that will promote the survival and integration of implanted cells.
Biosketch: Rebecca Carrier is a Professor in the Department of Chemical Engineering at Northeastern. She earned a BS in Chemical Engineering from Rensselaer Polytechnic Institute in 1995, and a Ph.D. in Chemical Engineering from the Massachusetts Institute of Technology in 2000, where she worked as a pioneer in cardiac muscle tissue engineering. After completing her graduate studies, Rebecca worked at Pfizer, Inc., as a Senior Research Scientist in oral controlled release drug delivery. She joined Northeastern in 2003, and the overall theme of her research interests is the interaction between biological systems and materials, with specific applications in drug delivery and regenerative medicine. She has worked with multiple industrial partners including Pfizer, Merck, and Boehringer Ingelheim, and has received honors including the NSF CAREER award, NU “Outstanding Teacher (2011),” “Faculty Fellow (2014),” and Soren Buus Outstanding Research (2017) Awards for excellence in teaching and research leadership. She was also invited to participate in the National Academy of Engineering Frontiers of Engineering (2016) and Frontiers of Engineering Education (2013) Symposia, served as the Member-At-Large for the Society for Biomaterials from 2018-2019, and was inducted into the American Institute for Medical and Biological Engineering in 2019.
Please email Alyssa Ramsey at a.ramsey@northeastern.edu for the link to the seminar.

BioE Seminar Series Presents: Wayne Mitzner
2020-10-07 12:00 - 13:00 (America/New_York)
Wayne Mitzner, Ph.D.
Professor and Associate Chair for Research in the Department of Environmental Health & Engineering, Johns Hopkins University Bloomberg School of Public Health, Baltimore Maryland
“New Insights into Lung Anatomy”
ABSTRACT:
We have utilized an optical clearing method to allow visualization of a heretofore undescribed subpleural acinar structural organization in the mammalian lung. The clearing method enables visualization of the lung structure 40-50 alveoli deep below the visceral pleura in intact inflated lungs. In addition to confirming previous observations that the immediate subpleural alveoli are uniform in appearance, we document for the first time that the subpleural lung parenchyma is much more uniformly organized than the internal parenchyma. Specifically, below the surface layer of alveoli, there is a striking parallel arrangement of alveolar ducts that all run perpendicular to the visceral pleural surface. A three dimensional visualization of alveolar ducts allowed for a calculation of the average inner to outer duct diameter ratio of 0.53 in these subpleural ducts. This unique, self-organizing parallel duct structure likely impacts both elastic recoil and the transmission of tethering forces in healthy and diseased lungs.
BIOGRAPHY:
Wayne Mitzner is Professor and Associate Chair for Research in the Department of Environmental Health & Engineering at the Johns Hopkins University Bloomberg School of Public Health. Dr. Mitzner’s research interests are focused on the structural basis of physiologic and pathologic phenotypes in the lung. He also has a keen interest in developing methods to assess and quantify pulmonary function in preclinical models, and has chaired several minisymposia and postgraduate courses in this area. These functional studies are directed toward investigating the best ways to evaluate pathologic processes after acute and long-term exogenous insults to the lung. His work has had continuous funding from the NIH for the past 30 years and is supported by over 300 research publications.
Please email Elizabeth Chesley at e.chesley@northeastern.edu for the seminar link.

ChE Seminar Series Presents: “Engineering Bacteria to Solve Problems in Renewable Chemical Production and Human Health”
2020-10-07 12:30 - 13:00 (America/New_York)
ChE Seminar Series Presents:

Benjamin M. Woolston, PhD
Assistant Professor, Department of Chemical Engineering, Northeastern University, Boston , MA
“Engineering Bacteria to Solve Problems in Renewable Chemical Production and Human Health”
Abstract: The synthetic biology revolution has given us the ability to genetically reprogram microbes to serve a wide variety of purposes – from miniscule chemical factories that orchestrate exquisitely selective enzymatic pathways to produce fuels, pharmaceuticals and polymers from simple raw materials – to biological computers that can sense their chemical environment and implement complex decision-making algorithms. The overall goal of the Woolston lab is to harness this potential for applications in renewable energy production and the human gut microbiota. In this talk, I will present an overview of the two major current thrusts of the lab: In the first, we are engineering anaerobic bacteria for the conversion of renewable single-carbon feedstocks to biofuels, taking advantage of a number of economic and ethical benefits of using these substrates compared to 1st and 2nd generation biofuel efforts. In the second, we are developing engineered microbes that can sense and correct an overabundance of the microbially derived metabolite hydrogen sulfide in the human gut; a toxic, volatile molecule implicated in the onset of IBD and colorectal cancer. As well as the exciting applied potential of the resultant technology, these efforts will also provide us with model systems with which to answer broader fundamental questions about microbial metabolism.
Biography: Dr. Woolston joined the NEU Chemical Engineering department as an Assistant Professor in January 2020. As an NSF Graduate Research Fellow, Dr. Woolston received his PhD in Chemical Engineering in 2017 from MIT under the guidance of Prof. Greg Stephanopoulos, where his research focused on the development of genetic tools to enable metabolic engineering in anaerobic CO2-fixing microbes, and the establishment of a methanol utilization pathway in the model organism Escherichia coli. While at MIT, he was an inaugural Fellow of the Chemical Engineering Communication Lab, where he provided peer tutoring and department-wide workshops to assist students and post-docs with aspects of scientific communication. His Post-doctoral work was conducted in the laboratory of Prof. Emily Balskus in the Chemistry & Chemical Biology department at Harvard University, where he studied microbial metabolic pathways and enzymes that contribute to the stability of health-associated Lactobacilli in the human vaginal microbiota. At Northeastern, his research program combines approaches from his previous research training in metabolic engineering, synthetic biology, biochemistry and microbiology to engineer microbes for biofuel & biochemical production, and as diagnostics and therapeutics in the Human gut microbiota. His lab team currently consists of three graduate students and two undergraduates. Since joining NEU, Dr. Woolston has taught the Biochemical Engineering senior elective (CHME 5630) and the graduate course in Kinetics & Reactor Design (CHME 7340).
Please email Alyssa Ramsey at a.ramsey@northeastern.edu for the link to the seminar.

Getting Started with RefWorks
2020-10-08 12:00 - 12:45 (America/New_York)
Start your fall 2020 research off on the right foot with Snell Library’s series of online workshops about citation management! In this session, learn how to use RefWorks to help you manage citations for yourself or your research group.
Register here: bit.ly/citationmgmtworkshops

BioE Seminar Series Presents: Aileen Huang-Saad
2020-10-08 12:00 - 13:00 (America/New_York)
Aileen Huang-Saad, Ph.D., MBA
Associate Professor, Biomedical Engineering, Entrepreneurship, and Engineering Education, University of Michigan, Ann Arbor MI
“Closing the Gap Between Theory and Practice: Rethinking Engineering Education in Real-Time”
ABSTRACT:
Despite 20 years of focus on organizational change and effective educational best practices in STEM higher education, significant research findings have had minimal influences on practice. In 2016, the University of Michigan BME Department sought to disrupt engineering education. Informed by organizational change theory, we asked: 1) Is there potential for change; 2) what strategies facilitate change, and 3) how can these strategies be implemented? As a result, we developed an Instructional Design Sequence, a new approach to instruction in which students, post-docs, and faculty create short Modules that use evidence-based teaching practices to expose BME students to BME professional practice. This talk describes how the Sequence was conceived of and demonstrates how organizational theory, entrepreneurship, and design can be used to inform education practice. The resultant Sequence has the potential to be a transferrable model for transforming engineering education, offering a new mechanism for integrating career-relevant curriculum into undergraduate curriculum in real-time, while training future educators in instructional evidence-based practices.
BIOGRAPHY:
Dr. Huang-Saad is faculty in the Department of Biomedical Engineering and Engineering Education at the University of Michigan. Dr. Huang-Saad has a thirteen-year history of bringing about organizational change in higher education, leveraging evidence-based practices. She created the U-M BME graduate design program, co-founded the U-M College of Engineering Center for Entrepreneurship, launched the U-M National Science Foundation (NSF) I-Corps Node, and developed the U-M BME Instructional Incubator. She is a canonical instructor for both the NSF and National Institute of Health (NIH) I-Corps Programs. Dr. Huang-Saad has received numerous awards for her teaching and student advising, including the 1938E College of Engineering Award, the Thomas M. Sawyer, Jr. Teaching Award, the U-M ASEE Outstanding Professor Award, the International Teaching with Sakai Innovation Award, and the College of Engineering Outstanding Student Advisor Award.
Dr. Huang-Saad’s current research areas are entrepreneurship, innovation, and transforming higher education. She is funded by the NSF to explore the influence of the microenvironment of entrepreneurship education on minoritized populations, entrepreneurial ecosystems, and fostering graduate student professional development.
Prior to joining the University of Michigan faculty, Dr. Huang-Saad worked in industry gaining experience in new venture biotech, the defense industry, and medical device testing. She has a Bachelor of Science in Engineering from the University of Pennsylvania, a Doctor of Philosophy from The Johns Hopkins University School of Medicine, and a Master of Business Administration from the University of Michigan Ross School of Business.
If interested in attending, please email Elizabeth Chesley at e.chesley@northeastern.edu

Getting Started with EndNote for Mac
2020-10-09 12:00 - 12:45 (America/New_York)
Start your fall 2020 research off on the right foot with Snell Library’s series of online workshops about citation management! In this session, learn how to use EndNote for Mac to help you manage citations for yourself or your research group.
Register here: bit.ly/citationmgmtworkshops

Bioengineering Works in Progress Student Seminar Series
2020-10-09 12:00 - 13:00 (America/New_York)
This virtual seminar series is an opportunity for Bioengineering graduate students to present their research. The first presenter is Yifang Liu from the Rouhanifard lab. Title: “Defining cell states based on stochastic transcription”. The second presenter is Max Winkelman from the Dai lab. Title: “Bioengineered Neurovascular Niche Supports Neural Stem Cell Proliferation and Neurogenesis”. Please email Danielle at d.freshnock@northeastern.edu for the link to the seminar.

ECE PhD Proposal Review: Muhamed Yildiz
2020-10-12 13:00 - 14:00 (America/New_York)
PhD Proposal Review: Interpretable Machine Learning for Retinopathy of Prematurity
Muhamed Yildiz
Location: Zoom Link
Abstract: Retinopathy of Prematurity (ROP), a leading cause of childhood blindness, is diagnosed by clinical ophthalmoscopic examinations or reading retinal images. Plus disease, defined as abnormal tortuosity and dilation of the posterior retinal blood vessels, is the most important feature to determine treatment-requiring ROP. State-of-the-art ROP detection systems employ convolutional neural networks (CNNs) and achieve up to $0.947$ and $0.982$ area under the ROC curve (AUC) in the discrimination of and levels of ROP. However, due to their black-box nature, clinicians are reluctant to trust diagnostic predictions of CNNs.
First, we aim to create an interpretable, feature extraction-based pipeline, namely, I-ROP ASSIST, that achieves CNN like performance when diagnosing plus disease from retinal images. Our method segments retinal vessels, detects the vessel centerlines. Then, our method extracts features relevant to ROP, including tortuosity and dilation measures, and uses these features for classification via logistic regression, support vector machines and neural networks to assess a severity score for the input. For predicting and levels of ROP on a dataset containing 5512 posterior retinal images, we achieve $0.88$ and $0.94$ AUC, respectively. Our system combining automatic retinal vessel segmentation, tracing, feature extraction and classification is able to diagnose plus disease in ROP with CNN like performance.
Furthermore, we aim to address the interpretability problem of CNN-based ROP detection system. Incorporating visual attention capabilities in CNNs enhances interpretability by highlighting regions in the images that CNNs utilize for prediction. Generic visual attention methods do not leverage structural domain information such as tortuosity and dilation of retinal blood vessels in ROP diagnosis. We propose the Structural Visual Guidance Attention Networks (SVGA-Net) method, that leverages structural domain information to guide visual attention in CNNs. SVGA-Net achieves $0.979$ and $0.987$ AUC to predict and levels of ROP. Moreover, SVGA-Net consistently results in higher AUC compared to visual attention CNNs without guidance, baseline CNNs, and CNNs with structured masks.

ECE PhD Proposal Review: Sara Banian
2020-10-12 14:00 - 15:00 (America/New_York)
PhD Proposal Review: Content-Aware Design Assistance Frameworks for Graphic Design Layouts
Sara Banian
Location: Zoom Link
Abstract: Layout is an important visual communication factor in graphic design that encompasses a page’s overall composition. During the different design stages, designers express their requirements through images describing the interface’s visual layout, hierarchical structure, and content. They create wireframe layouts to meet user requirements and find relative design examples to gain inspiration and explore design alternatives. This is not only an iterative process, but also a time-consuming one.
In this proposal, we aim to design and evaluate design assistance methodologies to augment the process of layout design with a particular focus on visual search and wireframe creation in the context of mobile User Interface (UI) deign. For visual search, we investigate how to find design examples that are relative to the design requirements of a UI layout. Layout retrieval is different from pixel-level image retrieval, as it requires processing both the spatial layout and the content of the data to retrieve similar images. To achieve this, I explore the problem of user interface image retrieval from both the data and the model side, by collecting a more highly annotated, well-suited dataset and proposing an object-detection based image retrieval model. The model takes as input a user interface image and retrieves the visually similar design examples. It uses object detection to identify the user interface components, performs semantic segmentation to produce a hierarchical structure, and trains an attention-aware multi-modal embedding network that leans the structure and content of the given layout design for relevant image retrieval. Results show that the system is capable of retrieving relative design examples through content analysis. Next, I propose a generative framework to investigate how to generate layout wireframes according to user specifications and following common design practices and conventions. The generative framework aims at modeling the content of the UI layouts taking into account different layout variations and design features.

ChE Seminar Series Presents: Joshua Gallaway
2020-10-13 08:00 - 17:00 (America/New_York)
Joshua Gallaway, Ph.D.
DiPietro Assistant Professor
Department of Chemical Engineering, Northeastern University, Boston, MA
“Next Generation for the Grid”
Abstract: In our lab, we study mechanisms within complex electrochemical systems. Practical electrochemical systems such as batteries, sensors, and fuel cells are often complex, involving engineered high surface area electrodes, starved electrolyte conditions, phase transformations, and interplay of the two electrodes. For these reasons, there are significant deviations from ideal behavior as well as emergent or unexpected phenomena. We seek to understand these to engineer better devices for broad societal benefit, such as the worldwide adoption of sustainable energy. We have developed strategies for coupling electrochemical methods with materials synthesis, theory, and operando analysis techniques, often based on high energy X-rays. This allows distributed and localized phenomena to be observed within operating electrochemical cells, and fundamental mechanistic information to be extracted from within the complex system. Thus we have a laboratory equipped for modern electroanalytical study and also make use of specialized X-ray sources maintained by the US Department of Energy. Recent research has focused on low-cost and safe battery materials for electrical storage at the scale of the power grid. Intermittent renewables like solar and wind power will succeed if excess electricity can be stored safely without adding significantly to the cost.
Biography: Joshua Gallaway has been an Assistant Professor at Northeastern University since 2017, where he has founded the Analysis of Complex Electrochemical Systems Laboratory (ACES). He received his PhD in chemical engineering from Columbia University in 2007. Working with his advisor Prof. Scott Calabrese Barton, he characterized the electron transfer rates of enzymes embedded in oxygen-reducing hydrogels. After his PhD work he completed a postdoctoral appointment with Prof. Alan West, also at Columbia, studying non-uniform current distributions in sub-micron interconnect features for the semiconductor industry. He then joined the newly-formed CUNY Energy Institute in a research position funded by the Wallis Foundation. There he worked on an ARPA-E funded project headed by Distinguished Professor Sanjoy Banerjee, which resulted in the spin out company Urban Electric Power. His recent research has focused on using high energy synchrotron techniques to visualize non-uniform reactions within battery electrodes.
Please email Alyssa Ramsey at a.ramsey@northeastern.edu for the link to the seminar.

Getting Started with Mendeley
2020-10-13 12:00 - 12:45 (America/New_York)
Start your fall 2020 research off on the right foot with Snell Library’s series of online workshops about citation management! In this session, learn how to use Mendeley to help you manage citations for yourself or your research group.
Register here: bit.ly/citationmgmtworkshops

ChE Seminar Series Presents: Richard West
2020-10-14 12:00 - 12:30 (America/New_York)
Richard West, Ph.D.
Associate Professor
Department of Chemical Engineering, Northeastern University, Boston, MA
“Detailed Kinetic Modeling: using computers to predict complex systems of chemical reactions”
Abstract: The primary focus of our research is the development of detailed microkinetic models for complex reacting systems, like combustion, heterogeneous catalysis, and bio-fuel processing. Our approach is to automate the discovery of reaction pathways, and the calculation of key parameters using ab initio quantum chemistry calculations, and novel machine learning techniques. These kinetic models will link to multi-scale models of the reactor systems so that the overall process can be understood and optimized as a whole. This approach towards microkinetic model development will then contribute to the optimization and understanding of catalytic processes, leading to catalyst design and discovery, as well as combustion processes, leading to safer and greener fire suppressants, refrigerants, and fuels. Our approach to computational chemistry involves both learning chemistry from the computers, and teaching chemistry to the computers. Learn more at neu.edu/comocheng.
Biography: Richard read Chemical Engineering at the University of Cambridge, completing his BA and MEng degrees in 2004. He was in the first cohort to spend a year at MIT on the undergraduate exchange program of the Cambridge-MIT Institute. He returned to MIT for a semester of graduate classes at the start of his PhD, also in Chemical Engineering at the University of Cambridge. His thesis work, in the Computational Modelling Group of Prof. Markus Kraft, was on modeling the chloride process for the gas-phase synthesis of titanium dioxide nano particles. He finished in 2008 and returned to MIT once more, this time as a postdoctoral research associate working with Prof. William Green to develop detailed kinetic models and the tools used to create them. In 2011 he moved across the river to Boston, starting the Computational Modeling in Chemical Engineering group at Northeastern University. In 2018 he became an Associate Professor (with tenure) and the Associate Chair for Graduate Studies in the Department of Chemical Engineering, a position he held until his sabbatical began in 2020. He lives with his wife (also NU faculty) and 5-year-old daughter in Brookline, MA. You can follow his weird toast diet on Instagram (@richardhwest).
Please email Alyssa Ramsey at a.ramsey@northeastern.edu for the link to the seminar.

Getting Started with Zotero
2020-10-14 12:00 - 12:45 (America/New_York)
Start your fall 2020 research off on the right foot with Snell Library’s series of online workshops about citation management! In this session, learn how to use Zotero to help you manage citations for yourself or your research group.
Register here: bit.ly/citationmgmtworkshops

BioE Seminar Series Presents: Roger Kamm
2020-10-14 12:00 - 13:00 (America/New_York)
Roger Kamm, Ph.D.
Cecil and Ida Green Distinguished Professor of Biological and Mechanical Engineering, Massachusetts Institute of Technology, Cambridge MA
“Metastasis on a chip: Effects of intravascular and transendothelial flow”
ABSTRACT:
Many CTCs fail to survive the voyage from the primary tumor to the metastatic site, and die before they can initiate a new tumor. Others not only survive, but enter into the tissue where some fraction proliferate and spread. We studied the effects of vascular flows, both intravascular (IVF) and transendothelial (TEF) on tumor cell adhesion, migration and transendothelial migration with the goal of better understanding how flow either promotes or impedes metastasis. Our results show that CTCs migrate or are dragged in the direction of flow prior to trans-endothelial migration. We also observe enhanced potential for TEM due to IVF. TEF, on the other hand, has little effect on either migration of CTCs along the luminal surface of the endothelium or their propensity to undergo trans-endothelial migration, but hastens the process once initiated. TEF increased the migration speed of tumor cells post-extravasation, and caused the cells to remain close to the outer endothelial surface. In summary, both types of flow tend to promote a pro-metastatic phenotype.
BIOGRAPHY:
Roger Kamm is the Cecil and Ida Green Distinguished Professor of Biological and Mechanical Engineering at MIT, where he has served on the faculty since 1978. Kamm has long been instrumental in developing research activities at the interface of biology and mechanics, formerly in cell and molecular mechanics, and now in engineered living systems. Current interests are in developing models of healthy and diseased organ function using microfluidic technologies, with a focus on vascularization.
Please email Elizabeth Chesley at e.chesley@northeastern.edu for the seminar link.

Meet Your Graduate Student Ambassadors!
2020-10-15 - 2020-12-30
Meet your Student Ambassadors! Prospective and Admitted Graduate Students are invited to meet their Student Ambassador via Unibuddy.

Engineering Graduate School Virtual Fair
2020-10-15 11:00 - 15:00 (America/New_York)
Join the Graduate School of Engineering Admissions team at Career Echo’s Virtual Graduate School Fair for Engineering programs on October 15th from 11:00 am -3:00pm. Our admissions team will be available to speak about graduate MS, PhD, and Certificate Programs.
Register today!

ECE PhD Proposal Review: Maher Kachmar
2020-10-15 16:00 - 17:00 (America/New_York)
PhD Proposal Review: Active Resource Partitioning and Planning for Storage Systems using Time Series Forecasting and Machine Learning Techniques
Maher Kachmar
Location: Zoom Link
Abstract: In today’s enterprise storage systems, supported data services such as snapshot delete or drive rebuild can result in tremendous performance overhead if executed inline along with heavy foreground IO, often leading to missing Service Level Objectives (SLOs). Moreover, static partitioning of storage systems resources such as CPU cores or memory caches may lead to missing Service Level Agreements (SLAs) such as data reduction rate (DRR). However, typical storage system applications such as Virtual Desktop Infrastructure (VDI) or web services follow a repetitive workload pattern that can be learned and/or forecasted. Learning these workload pattern allows us to address several storage system resource partitioning and planning challenges that may not be overcome with traditional manual tuning and primitive feedback mechanism.
We propose a priority-based background scheduler that learns storage system workload repetitive pattern and allows storage systems to maintain peak performance and meet service level objectives (SLOs) while supporting a number of data services. When foreground IO demand intensifies, system resources are dedicated to service foreground IO requests and any background processing that can be deferred are recorded to be processed in future idle cycles as long as our forecaster predicts that the storage pool has remaining capacity. The smart background scheduler adopts a resource partitioning model that allows both foreground and background IO to execute together as long as foreground IOs are not impacted, harnessing any free cycles to clear background debt. Using traces from VDI and web services applications, we show how our technique can out performance a static method that sets fixed limits on the deferred background debt and reduces SLO violations from 54.6% (when using a fixed background debt watermark), to only 6.2% when dynamically adjusted by our smart background scheduler.
This thesis also proposes a smart capacity planning and recommendation tool that ensures the right number of drives are available in the storage pool in order to meet both capacity and performance constrains without over-provisioning storage. Aided by forecasting models that characterizes workload pattern, we can predict future storage pool utilization and drive over-wearing. Similarly, to meet SLOs, the tool recommends expanding pool space in order to defer more background work through larger debt bins. We also propose a content-aware learning cache (CALC) that uses machine learning techniques to actively partition the storage system cache between a deduplication data digest cache, content cache, and address based data cache to improve cache hit performance while maximizing data reduction rate (DRR).

COE Dialogue of Civilizations Information Session
2020-10-16 12:00 (America/New_York)
Hear from COE students who have done a dialogue before:

Alex Renaud
Environmental Engineering and Landscape Architecture
Sustainable Engineering in the 21st Century – Brazil
Julia Ubertini
Civil Engineering
Global Climate Change, Science, and Policy – India

Location: DOC Information Session Link
Meeting ID: 969 3847 5666
Learn about in-person and virtual dialogues this summer. For more information please contact Joy Erb: j.erb@northeastern.edu

Bioengineering Works in Progress Student Seminar Series
2020-10-16 12:00 - 13:00 (America/New_York)
This virtual seminar series is an opportunity for Bioengineering graduate students to present their research. The first presenter is Sepideh Tavakoli from the Rouhanifard lab. Title: “Single-molecule detection of modified mRNAs in human cells”. The second presenter is Aleksandra Petelski from the Slavov lab. Title: “Exploring Ribosomal Protein Stoichiometry in Lung Adenocarcinoma Patients”. Please email Danielle at d.freshnock@northeastern.edu for the link to the seminar.

Graduate Studies Virtual Open House
2020-10-17 08:00 - 14:00 (America/New_York)
Northeastern University will be hosting a Graduate Studies Virtual Open House for students interested in pursuing their graduate studies at Northeastern. Join Graduate School of Engineering faculty, staff, and current students on October 17th from 8-2pm to learn more about your program of interest.
More information including a schedule of events and a registration link will be added to the Graduate Studies website when available.

West Coast Masters Alumni Panel on Diversity, Equity, and Inclusion
2020-10-20 18:30 - 21:00 (America/Los_Angeles)
On October 20th, from 6:30-9pm PST, Graduate Student Services is hosting a virtual Alumni Panel on Diversity, Equity, and Inclusion in the Engineering Field for West Coast Master’s students. Panelists are all graduates of COE and have a diverse range of experiences in Engineering. From 6:30-7:30pm, the panelists will discuss with one another their experiences regarding these topics, and from 7:30-8pm, students can ask the panelists any questions they have. Finally, from 8-9pm, an optional networking event with the panelists is available for students. The link to register/RSVP will be sent out shortly.

ECE PhD Proposal Review: Wenqian Liu
2020-10-21 09:00 - 10:00 (America/New_York)
PhD Proposal Review: Explainable Efficient Models for Computer Vision Applications
Wenqian Liu
Location: Zoom Link
Abstract: State of the art deep learning based models, such as Convolutional Neural Networks (CNNs) and generative models, achieve impressive results, but with their great performance comes great complexity and opacity, huge parametric spaces and little explainability. The criticality of model explainability and output interpretability, manifests clearly in real-time critical decision making processes and human-centred applications, such as in healthcare, security and insurance.
Explainability and interpretability are tackled in this thesis, as intrinsic qualities in the model architecture as well as post-hoc improvement on existing models.
In the area of frame prediction in video sequences, we introduce DYAN, a novel network with very few parameters, that is easy to train and produces accurate high quality frame predictions and more compact than previous approaches. Another key aspect of DYAN is interpretability, as its encoder-decoder architecture is designed following concepts from systems identification theory and exploits the dynamics-based invariants of the data. We also introduce KW-DYAN, an extension of DYAN that tackles the issue of time lagging in video predictions, by implementing a novel way of quantifying prediction timeliness and proposing a new recurrent network for adaptive temporal sequence prediction that employs a warping module to reduce dynamic changes and a Kalman filtering module to detect dynamic changes in video frames. The experimental results show the reduced lagging across the tested Caltech dataset and the UCF dataset, while also performing well in other commonly used metrics.
In the area of image classification, categorization and scene understanding, we observe that techniques such as gradient-based visual attentions have driven much recent efforts in using visual attention maps as a mean for visual explanations of Convolution Neural Networks (CNNs), with impressive results but fail to extend to explaining generative models, e.g Variational Autoencoders (VAEs) as efficiently. In this thesis we bridge this crucial gap, and propose the first technique to visually explain VAEs by means of gradient-based attentions, with methods to generate visual attentions from the learned latent space, and also demonstrate such attention explanations serve more than just explaining VAEs. We show how these attention maps can be used to localize anomalies in images, conducting state-of-the-art performance on the MVTec-AD dataset. We also show how they can be infused into model training, helping bootstrap the VAEs into learning disentangled latent space, as proved on the Dsprites dataset.
