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DTSTART;TZID=America/New_York:20211207T080000
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UID:29691-1638864000-1639155600@coe.northeastern.edu
SUMMARY:Experiential Entrepreneurship Intersession Opportunity
DESCRIPTION:Want to hone your entrepreneurial skills over winter break while working directly with tech startups? Registration is now open for the Experiential Entrepreneurship Intersession course running from January 3rd through January 14th. \nOffered virtually or in-person through the Roux Institute at Northeastern University\, students will learn about the venture creation process and work hand-in-hand with tech startups that have emerged from the accelerator and residency programs at the Roux Institute. \n
Past guest lecturers have included: \n\nAli Goldstein Norup\, co-founder of kpiReady and current Head of VC and Startup Ecosystem\, North Americas at Google Cloud\nBen Chesler\, co-founder of Imperfect Foods and current Associate Director of Entrepreneurship at the Roux Institute\nJesse Bardo\, co-founder of EverTrue and current Director at Silicon Valley Bank\n\nAnd\, if you register by December 8th\, you will receive an invite to the Techstars Demo Day in Portland\, Maine. The event will gather the Maine startup community for an in-person presentation from each of the 10 companies selected for the inaugural Roux Institute Techstars Accelerator class. Following you’ll be invited for a reception at the Roux Institute where guests will meet and mingle with the startups\, investors\, and community members. \nTo view the course: \n\n
Visit Banner and select the term\, Spring 2022 Semester. Even though Intersession Term courses meet between semesters\, they have been administratively assigned to Spring 2022 semester.\nClick Advanced Search on the Browse Classes page.\nIn the attribute field\, choose Intersession Term Course. All the Intersession Term offerings will appear.\n\n
Registration for intersession will close Friday\, December 10th at 11:59 (EST).
URL:https://coe.northeastern.edu/event/experiential-entrepreneurship-intersession-opportunity/
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UID:29575-1638885600-1638889200@coe.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Sara Banian
DESCRIPTION:PhD Dissertation Defense: Content-Aware AI-Driven Design Assistance Frameworks for Graphic Design Layouts \nSara Banian \nLocation: Zoom Link \nAbstract: Designing user interfaces (UIs) for mobile interaction is widespread but still challenging. It is important for the overall user satisfaction and application success. During the design process\, designers express their requirements through images describing the UI’s layout\, structure\, and content. Designers\, however\, encounter key challenges throughout the design process. For example\, searching for inspiring design examples is challenging because current search systems rely on only text-based queries and do not consider the UI structure and content. Furthermore\, these systems often focus on overall page-level layout over individual UI components. Also\, creating wireframe templates is difficult for many designers as it necessitates an understanding of different design guidelines. Therefore\, it is critical to support designers by developing effective design tools to help them be more productive and creative.\nIn this dissertation\, I aim to explore how to develop design assistance methodologies to augment the process of UI layout design\, with a particular focus on visual search and layout generation. Specifically\, for this exploration\, I seek to investigate the use of advanced deep learning models in the context of mobile UI layout design. Processing layouts differs from processing pixel-level images in that it necessitates processing both the semantic (e.g.\, labels) and spatial (e.g.\, coordinates) content of the layout to model the data properly. To achieve this\, I explore the design problems from both the data and the model side. First\, I present a large-scale UI dataset that accurately specifies the interface’s view hierarchy (i.e.\, UI components and their location). Second\, I contribute the VINS framework\, which is composed of three systems LayVis\, CompVis\, and TransVis that addresses layout-based visual search\, component-based visual search\, and layout generation\, respectively.\nFirst\, I introduce LayVis\, an object-detection layout-based retrieval model. It takes as input a UI image and retrieves visually similar design examples. Next\, I introduce CompVis\, a component-based visual search system to easily retrieve individual UI components via convolutional neural networks (CNNs). Specifically\, for a given query\, the system allows to retrieve (1) text label synonyms\, (2) similar UI components\, and (3) design examples containing such components. Finally\, I present TransVis\, a transformer-based generative framework that investigate how to generate UI layouts according to user specifications and following design practices. It specifically models UI layouts as an ordered sequence of elements based on spatial and semantic relationships for (1) generating complete UI layouts\, (2) auto-completing existing UI layouts seamlessly\, and (3) supporting many design elements per layout.\nOverall\, the work presented in this dissertation contributes to augmenting the UI layout design. Through quantitative and qualitative evaluation of VINS\, we conclude the following: (1) Advanced deep learning models can aid in the development of design assistance methodologies for layout design; and (2) Designers perceive the use of VINS inspiring and useful. Such insights\, combined with the open-sourced large-scale dataset\, can help the research community develop more effective AI-based data-driven design tools. This work presents future opportunities to investigate different deep learning models within the context of layout design and how designers interact with these AI-based models.
URL:https://coe.northeastern.edu/event/ece-phd-dissertation-defense-sara-banian/
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