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X-WR-CALDESC:Events for Northeastern University College of Engineering
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DTSTART;VALUE=DATE:20221102
DTEND;VALUE=DATE:20221107
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SUMMARY:SHPE 2022 NATIONAL CONVENTION
DESCRIPTION:Join Northeastern University in Charlotte\, NC for the annual SHPE (Society of Hispanic Professional Engineers) National Convention! \nNortheastern University and the Graduate School of Engineering will be in attendance at the Career Fair & Graduate School Expo from 10am-4pm on Friday and Saturday. We will also be hosting a Diversity and Inclusion Hospitality Suite on Thursday night from 7:30-9:30pm.
URL:https://coe.northeastern.edu/event/shpe-2022-national-convention/
ORGANIZER;CN="Graduate School of Engineering":MAILTO:coe-gradadmissions@northeastern.edu
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221102T120000
DTEND;TZID=America/New_York:20221102T130000
DTSTAMP:20260420T100537
CREATED:20221019T135830Z
LAST-MODIFIED:20221019T135830Z
UID:33652-1667390400-1667394000@coe.northeastern.edu
SUMMARY:Engineered cellular models to explore human disease heterogeneity
DESCRIPTION:ChE Seminar Series Presents:  \nAlison McGuigan\, PhD \nProfessor\, Chemical Engineering & Applied Chemistry\, University of Toronto \nAbstract: \nEx vivo culture models provide powerful tools to interrogate the role of tumour heterogeneity in human cancers. Patient-derived organoids (PDOs) are emerging as powerful models to capture the genetic heterogeneity of human tumors. However\, extrinsic factors present in the tumor microenvironment (TME) of a tumour\, such as the presence of stromal cells and gradients of small molecules such as oxygen\, also affect cancer phenotype and response to therapy. This talk will describe tissue-engineered platforms we have developed 1) to enable controlled assembly and disassembly of organoid structures to study the impact of both genetic and microenvironmental heterogeneity on tumor cell behavior and 2) to explore tumour microenvironment remodelling\, heterogeneity in response to therapy\, and potential to re-grow after therapy. \nBio: \nDr. Alison McGuigan is a Professor in Chemical Engineering and Applied Chemistry and the Institute for Biomedical Engineering at University of Toronto. She obtained her undergraduate degree from University of Oxford\, her PhD from University of Toronto working\, and completed Post Doctoral Fellowships at Harvard University and Stanford School of Medicine. Dr. McGuigan research group is focused on the engineering of tissue models to explore mechanisms of disease and regeneration. Dr. McGuigan has established strategies to generate multi-component tissue systems with specified organization. Furthermore\, she has pioneered the design of tissue platforms for smart data acquisition\, with a focus on stratifying heterogeneous bulk data by cell population\, by spatial location\, or by time. In recognition of Dr. McGuigan’s work she has received numerous awards including the 2013 TERMIS-AM Young Investigator Award\, and the Canadian Society for Chemical Engineering Hatch Innovation Award. In 2018 was elected to the Royal Society of Canada-College of New Scholars\, Artists and Scientists and in 2022 she was elected a Fellow of TERM by the Tissue Engineering and Regenerative Medicine International Society. She serves on the executive leadership team of CFREF Medicine by Design program and on the Centre for Commercialization of Regenerative Medicine (CCRM) incubation and outreach committee.
URL:https://coe.northeastern.edu/event/engineered-cellular-models-to-explore-human-disease-heterogeneity/
LOCATION:236 Richards\, 360 Huntington Ave\, Boston\, MA\, 02115\, United States
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221102T120000
DTEND;TZID=America/New_York:20221102T130000
DTSTAMP:20260420T100537
CREATED:20221103T173443Z
LAST-MODIFIED:20221103T173443Z
UID:34202-1667390400-1667394000@coe.northeastern.edu
SUMMARY:Yuexi Zhang's PhD Proposal Review
DESCRIPTION:“Human Body and Activity Analysis” \nAbstract: \nHuman-related applications such as person detection\, human pose estimations and human activity recognition\, that always draw a lot of attentions in computer vision community. In this proposal\, we discuss several related topics that we are interested in\, and demonstrate how we improve the existing methods. The first problem we consider is video-based human pose estimation. For most general approaches\, researchers focus on collecting human poses from each frame independently and then associate them based on matching or tracking methods. However\, such the pipeline usually relies on complex computations and also consumes running time. To overcome such shortages\, we propose a light weighted network with the unsupervised training strategy\, that aims to reduce running time but remaining the performance. The next problem we explore is about cross-view action recognition (CVAR). The goal of CVAR is to recognize a human action when observed from a previously unseen viewpoint. This is important for some applications such as surveillance systems where is not practical or feasible to collect large amounts of training data when adding a new camera. In this case\, it requires methods that are able to generate reliable view-invariant information trained with given viewpoints and recognize the action from an unseen viewpoint. In general\, most approaches rely on 3D data\, but using 2D data is still under-discovered. Besides\, the performance of those approaches using only 2D data is far worse than 3D approaches. Therefore\, we propose a simple yet efficient CVAR framework that takes 2D data as input and close the performance gap between 3D and 2D input. The last problem we investigate is online action detection and we are interested in detecting action start at current stage. Online action start detection problem is to detect an action startpoint as soon as it occurs with its action category in untrimmed\, streaming videos\, and it has potential applications such as early alert generation in surveillance systems. The typical approaches usually heavily rely on frame-level annotations and also they are limited to pre-defined action categories. Therefore\, we propose a novel yet simple design\, 3D MLP-mxier based architecture that aims to detect the taxonomy-free action start without using frame-level annotations. \n  \nCommittee: \nDr. Octavia Camps(Advisor) \nDr. Mario Sznaier \nDr. Sarah Ostadabbas
URL:https://coe.northeastern.edu/event/yuexi-zhangs-phd-proposal-review/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221102T140000
DTEND;TZID=America/New_York:20221102T150000
DTSTAMP:20260420T100537
CREATED:20221103T173638Z
LAST-MODIFIED:20221103T173638Z
UID:34206-1667397600-1667401200@coe.northeastern.edu
SUMMARY:Kai Huang's PhD Dissertation Defense
DESCRIPTION:“Partitioning Data Across Multiple\, Network Connected FPGAs with High Bandwidth Memory to Accelerate Non-streaming Applications” \nAbstract:\nField Programmable Gate Arrays (FPGAs) are increasingly used in cloud computing to increase the run time of various applications. Flexibility\, efficiency and lower power enable FPGAs to be important components in modern data centers. Applications such as Secure Function Evaluation (SFE)\, graph processing\, and machine learning are increasingly mapped to FPGA-based adaptable cloud computing platforms. However\, due to resource limitations\, it is difficult to map applications to only one FPGA. Applications with a streaming data processing pattern can be mapped to a multiple-FPGA platform where the FPGAs are connected in a 1-D or ring topology\, thus communications overhead can be pipelined with computations. The communication\, merely passing data from boards to boards\, will not significantly affect the system performance if the bandwidth is sufficient. In a more general processing pattern involving non-streaming applications\, each FPGA is responsible for only a portion of the computation and the FPGAs must keep exchanging data during the run time of the application. The communication cost can be the bottleneck of such a system. The challenge is how to map and parallelize these applications to a multi-FPGA cloud computing platform in such a way that communication is minimized and speedup is maximized.\nIn this research\, we build a framework to map garbled circuit applications\, an implementation of SFE\, to a cloud computing platform that has FPGA cards attached to computing nodes. The FPGAs on the node are able to communicate directly through the network. The framework consists of two parts: hardware design and software preprocessing. The hardware design integrates with the Xilinx UDP network stack enabling the capability to exchange data through the network and thus bypassing the processor and its software stack. The framework also takes advantage of High Bandwidth Memory (HBM) for high off-chip memory throughput. The levels of memory hierarchy available on the FPGA are used for caching both local data and incoming and outgoing network data. Preprocessing will generate the reordered batches of each layer needed for processing\, efficient memory allocation and final memory layout. We also applied an effective partitioning algorithm to schedule executions to different FPGAs to minimize the communication between FPGAs. By generating different size of problems from the EMP-toolkit\, we can demonstrate that this hardware-software co-design framework achieves nearly optimal two times speedup on a two-FPGA setup compared to a one-FPGA implementation. We explore extremely large examples that cannot be mapped to one-FPGA\, proving that it is achievable to map large examples of billions of operations to this distributed heterogeneous system. \nCommittee: \nProf. Miriam Leeser(advisor) \nProf. Stratis Ioannidis(co-advisor) \nProf. Mieczyslaw Kokar
URL:https://coe.northeastern.edu/event/kai-huangs-phd-dissertation-defense/
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