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X-WR-CALDESC:Events for Northeastern University College of Engineering
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211201T080000
DTEND;TZID=America/New_York:20211201T090000
DTSTAMP:20260512T083100
CREATED:20211118T161129Z
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SUMMARY:Learn about the Co-op Program (Disciplinary) Webinar
DESCRIPTION:Please join our Assistant Dean of Co-op at a webinar discussing the Co-op experiential learning opportunities available for graduate students in the departments of Bioengineering\, Chemical Engineering\, Civil & Environmental Engineering\, Electrical & Computer Engineering\, and Mechanical & Industrial Engineering. \nRegister
URL:https://coe.northeastern.edu/event/learn-about-the-co-op-program-disciplinary-webinar/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211201T090000
DTEND;TZID=America/New_York:20211201T100000
DTSTAMP:20260512T083100
CREATED:20211124T175155Z
LAST-MODIFIED:20211129T150240Z
UID:29534-1638349200-1638352800@coe.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Kai Li
DESCRIPTION:PhD Dissertation Defense: Reconfigurable and Intelligent Wireless Charging Surfaces \nKai Li \nLocation: 232 ISEC \nAbstract: Reconfigurable intelligent surfaces (RISs) have received significant attention for theirpotential to transform the environments by intelligently reconfiguring the surfaces\, infrastructures\,and engineering the electrical and magnetic fields. On the other hand\, while wireless power transfer has advanced\, there has been limited progress on increasing the charging coverage\, such as charging over large surfaces\, multi-device charging\, and automation. This dissertation aims to address these challenges and design and develop first-of-its-kindtheory and practice to transform ordinary surfaces into contactless\, intelligent\, and multi-devicewireless chargers. First\, the combination between magnetic resonance and the so-called concept of‘energy hopping’ across wireless inter-connected coils turns a large surface into a programmablewireless charging surface. The magnetic fields are carefully shaped on the fly over the surface\,enabling them to distribute energy efficiently at multiple locations on demand and charge differenttypes of devices. Two frameworks are developed: SoftCharge can deliver 23 W up-to 20cm over a larger surface\, and iSurface enables the creation of arbitrary and configurable power spots and power flow paths over 2D and 3D resonator surfaces. Inspired by the strong coupled magnetic resonance wireless power transfer\, two intelligentsurface sensing frameworks\, SoftSense\, and iSense\, are introduced that create collaborative surface-based object sensing and tracking using networked coils. SoftSense allows detection of the type of object and where it is placed on a large surface. iSense enables robot tracking over large surfaces.We validate our design on real sensing prototypes\, and experimental results show that each sensing coil only consumes few milliwatts and has 90% accuracy for velocity estimation.Combined with meta-surface\, we extended the intelligent charging surfaces to enhances safety\, end-to-end power transfer efficiency\, and customized power pattern over the surface.Toward this\, we design and develop a new system call meta-resonance wireless power transfer system that consists of power distribution layer and meta-resonance layer\, along with a new theory and prototype for fine-tuned and controllable power amplifying\, power blocking and normal power passing over the surface. We aim to create customized pattern and different application from portable devices(phone\, tablet) to medical devices\, and industrial devices with high safety and high power transfer efficiency.
URL:https://coe.northeastern.edu/event/ece-phd-dissertation-defense-kai-li-2/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211201T120000
DTEND;TZID=America/New_York:20211201T130000
DTSTAMP:20260512T083100
CREATED:20211123T213858Z
LAST-MODIFIED:20211123T213858Z
UID:29516-1638360000-1638363600@coe.northeastern.edu
SUMMARY:ChE Seminar Series: Orchestrating Cellular Regeneration at Organ Scale
DESCRIPTION:ChE Seminar Series Presents: \nYvon Woappi\, Ph.D. \nK99/R00 MOSAIC Fellow at Harvard Medical School\, Brigham and Women’s Hospital \nAbstract \nLarge scale tissue damage\, such as organ failure and burn injury\, is a leading cause of morbidity and death. However\, the mechanisms underlying full regeneration of organs remain poorly understood. As the largest organ system in the body\, the integumentary system is a composite tissue evolutionarily adapted for healing. Consequently\, its complex physiology requires multifaceted cooperation between several distinct cell populations and cell lineages of embryologically distinct origins. Equally integrated within this dynamic process is local immune response that produces mitogenic and inhibitory signals throughout the restoration procedure. There remains a significant gap in understanding how these processes are orchestrated\, and how various skin cell populations from distinct developmental lineages functionally cooperate to regenerate tissue at organ scale. My research seeks to characterize the molecular language of tissue healing and to harness this malleable dialect for the regeneration of mammalian tissues. Through the development of organoid models of wound regeneration\, and the coupling of these systems with novel gene-editing approaches\, my work is enabling the functional understanding of the multifaceted cellular events executed throughout restorative healing. This seminar will describe these high throughput technologies and will illustrate their utility in identifying novel regulators of tissue healing. \nBio \nDr. Yvon Woappi’s passion for life sciences ignited during his childhood in Douala\, Cameroon and was magnified after his family immigrated to Hanover\, Pennsylvania during his middle school years. He went on to receive his B.S in Biology at the University of Pittsburgh\, and his Ph.D. in Biomedical Sciences as a Grace Jordan McFadden Fellow under Lucia Pirisi at the University of South Carolina. There\, he developed a 3D skin organoid system to study the relationship between epithelial regeneration and virus-induced neoplasia. He subsequently completed postdoctoral training in the Harvard Dermatology Research Training Program at Brigham and Women’s Hospital where he established novel in vivo gene editing systems to understand the contribution of distinct cell lineages in tissue regeneration and cancer. He was recipient of the 2019 Engineering the Genome Award\, and was later selected as a Rising Star in biomedical sciences and engineering by MIT\, Cornell\, BU and Columbia University. Most recently\, Dr. Woappi was awarded the NIH K99/R00 MOSAIC award to launch his independent research career. Away from the bench\, he is an ardent proponent of inclusive excellence and currently sits on the advisory committee for the NIH Continued Umbrella Research Experiences Program at Harvard Medical School.
URL:https://coe.northeastern.edu/event/che-seminar-series-orchestrating-cellular-regeneration-at-organ-scale/
LOCATION:108 SN
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DTSTART;TZID=America/New_York:20211201T120000
DTEND;TZID=America/New_York:20211201T130000
DTSTAMP:20260512T083100
CREATED:20211124T175107Z
LAST-MODIFIED:20211124T175107Z
UID:29527-1638360000-1638363600@coe.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Flavius Pop
DESCRIPTION:PhD Dissertation Defense: Intrabody Communication for Real-Time Monitoring of Implanted Medical Devices based on Piezoelectric Micromachined Ultrasonic Transducers \nFlavius Pop \nLocation: Zoom Link \nAbstract: Nowadays when we think about medical devices and patient monitoring\, we can easily imagine ourselves laying down in a hospital bad\, wires coming out of everywhere\, and being looked after by nurses and physicians. Scary and not that comfortable! For this reason\, medical wearable devices are becoming more popular for at-home monitoring and transmitting data back to the hospital. Sometimes wearables are not enough\, this is why Implanted Medical Devices (IMDs) are still required to monitor many vital signs (blood flow\, insulin level\, neurons reading etc.) and act upon these readings (nerve stimulation\, heart defibrillation\, insulin pumping etc.). In order to be minimally invasive\, reduce the risk of infection and rejection from the body\, and last a long time (avoiding any further surgery) the IMDs require robust wireless communication technology to communicate with the external world. In this presentation I am going to show how we can implement an ultrasonic wireless communication link based on Piezoelectric Micromachined Ultrasonic Transducers (pMUTs) arrays. PMUT arrays can be integrated with existing IMDs\, used for wireless power charging\, and can enable communication links for receiving and transmitting data. During the first part of the presentation I will show the modeling and design of the pMUT arrays\, followed by the fabrication process and the device’s characterization for system level validation. At this point\, the communication link is implemented with arrays implanted in a tissue phantom and the channel is characterized at several distances. During the second part of the presentation I will show novel techniques to improve the ultrasonic communication link such as duplexing matching networks for bandwidth definition and direct modulation for implantation depth increase and direct bitstream feeding. In the future I envision that the number of IMDs are going to increase\, and therefore I developed a scanning protocol that will allow medical doctors to find all implanted devices. This is the equivalent of an “ultrasonic stethoscope”. Given the small form-factor of the IMDs these will have little to no space for a battery\, limiting the operation lifetime. For this reason\, I developed an Ultrasonic Wakeup Receiver (UWuRx) based and on the direct modulation system and on a Micromachined Electro-Mechanical System (MEMS) switch which allows for near zero-power consumption in the idle state. This UWuRx enabled on-demand device usability and limited the idle power consumption\, which leads to battery life extension.
URL:https://coe.northeastern.edu/event/ece-phd-dissertation-defense-flavius-pop/
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DTSTART;TZID=America/New_York:20211201T153000
DTEND;TZID=America/New_York:20211201T163000
DTSTAMP:20260512T083100
CREATED:20211122T201210Z
LAST-MODIFIED:20211122T201210Z
UID:29483-1638372600-1638376200@coe.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Zulqarnain Qayyum Khan
DESCRIPTION:PhD Proposal Review: Interpretable Machine Learning for Affective Neuroscience and Psychophysiology \nZulqarnain Qayyum Khan \nLocation: Zoom Link \nAbstract: In this thesis\, we leverage Machine Learning to investigate questions of interest in affective psychophysiology and neuroscience . We argue for and apply appropriate existing methods where possible and analyze the results they provide. Where existing methods fail to provide an answer we propose and build new models. We demonstrate the use of Hierarchical Clustering to investigate autonomic nervous system reactivity during an active coping stressor task\, revealing physiological indices of challenge and threat. Similarly\, we leverage Dirichlet Process Gaussian Mixture Modelling (DP-GMM) to reveal the variation in affective experience during a context-aware experience sampling study and to investigate the relationship between emotional granularity and cardiorespiratory physiological activity using resting state data for participants in the same study. We propose and develop Neural Topographic Factor Analysis (NTFA)\, a novel factor analysis model for fMRI data with a deep generative prior that teases apart participant and stimulus driven variation and commonalities and learns a latent space that can shed light on important neuroscientific phenomenon such as individual variation and degeneracy.\nBased on the work we have already done\, we propose three further lines of research that we intend to include in this thesis. First\, NTFA can essentially be viewed as a family of models\, where appropriate modifications can be made depending on what questions are needed to be answered. Leveraging this\, we propose explicitly adapting NTFA to tackle the question of degeneracy in neural responses. This involves introducing another latent space which can be used to capture and visualize the interaction of each participant with each stimulus in a given fMRI study. The arrangement of inferred embeddings in this latent space can then suggest presence or absence of different types of degeneracy in neural responses among participants in response to the presented stimuli. Second\, during the course of this interdisciplinary research we realized that there is a need for a comprehensive work that sheds light on the assumptions and limitations of some of the most popular machine learning methods used commonly in the sciences (specially psychology)\, and provide recommendations on how researchers can be more mindful of the underlying assumptions machine learning methods make. This can then equip users of ML methods to draw more appropriate conclusions from the results they get. We intend to include this in our thesis. Third\, continuing along the same lines\, there is also a need for better explanation models for the increasingly complicated ML models in use today. This is especially true in health sciences where the knowledge of why an ML model made a particular decision is almost as important as that decision being accurate. To this end we propose a theoretical work that ties the reliability of explanation models to the robustness of the models they are trying to explain.
URL:https://coe.northeastern.edu/event/ece-phd-proposal-review-zulqarnain-qayyum-khan/
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