{"id":26753,"date":"2020-01-10T10:48:10","date_gmt":"2020-01-10T15:48:10","guid":{"rendered":"https:\/\/www.bu.edu\/cise\/?page_id=26753"},"modified":"2023-04-26T15:33:05","modified_gmt":"2023-04-26T19:33:05","slug":"seed-grants","status":"publish","type":"page","link":"https:\/\/www.bu.edu\/cise\/cise-research\/seed-grants\/","title":{"rendered":"CISE Seed Grant Awards"},"content":{"rendered":"

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The Center for Information and Systems Engineering (CISE) awards seed grants to affiliated CISE faculty to enable new innovative collaborations and development of new research directions. The program is designed to help faculty further research goals by funding graduate student researchers. Investigators are awarded for projects that: (1) have potential for future external funding, (2) are contingent on gathering initial results, proof of concept or significant data collection, and (3) display intention to develop new innovative collaborations and research directions.\u00a0<\/span><\/span><\/a><\/p>\n

The solicitation period is currently closed. <\/span>\u00a0<\/strong>Check back for announcements on the next solicitation period.\u00a0<\/em><\/p>\n

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Funded Research Projects<\/span><\/strong><\/h2>\n

CISE-ENG Seed Awards – Spring 2021<\/strong><\/span>
\nSolicitation Theme: Intelligent, Autonomous, and Secure Systems<\/strong><\/span><\/h3>\n

Securing Wireless Ingestible Medical Devices<\/h4>
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PI:<\/b>\u00a0\u00a0<\/span><\/b>Rabia Yazicigil<\/a>, CISE affiliate and Assistant Professor (ECE) College of Engineering<\/span>
\nCo-PI:<\/strong>\u00a0<\/span>David Starobinski<\/a>, CISE affiliate and Professor (ECE, SE) College of Engineering<\/span><\/p>\n

Project Summary:\u00a0<\/span><\/b>Wireless ingestible and implantable medical devices (IMDs), such as on-demand drug delivery\u00a0systems, allow continuous monitoring and adjustment of healthcare delivery, potentially\u00a0resulting in improved health outcomes. Security is a critical component in the design of\u00a0these connected medical devices. Attacks on wireless IMDs are dangerous due to the life-critical nature of these devices and the sensitivity\/privacy of the data being exchanged.\u00a0Given hard constraints on energy and computation, securing these devices cannot solely\u00a0rely on cryptographic mechanisms. To harden the security of wireless IMDs and as part of\u00a0a new collaboration between the PIs, this project proposes (i) to assess the vulnerability\u00a0of wireless IMDs to different types of attacks, such as denial-of-service, privacy breaches,\u00a0and spoofing; (ii) to develop innovative counter-measures leveraging the physical layer, and\u00a0(iii) to concretely demonstrate these solutions in the context of low-power wireless ingestible\u00a0capsules used for inflammatory bowel disease monitoring. Our goal is to obtain preliminary\u00a0results on these fronts and apply for funding from programs run by various agencies focusing\u00a0on the cybersecurity of connected devices.<\/p>\n

Seed Grant Report: Securing Wireless Ingestible Medical Devices (Final Report)<\/a><\/p>\n

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Learning from Interactions with Blind Users for Customized and Scalable Navigation Assistance Systems<\/h4>
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PI:<\/strong>\u00a0<\/span>Eshed Ohn-Bar<\/a>, CISE affiliate and Assistant Professor (ECE) Boston University School of Engineering
\nCo-PIs:<\/strong>\u00a0Calin Belta<\/a>, CISE affiliate and Professor (ME, SE, ECE) Boston University School of Engineering, and\u00a0Venkatesh Saligrama<\/a>, CISE affiliate and Professor (ECE) Boston University School of Engineering<\/span><\/p>\n

Project Summary:<\/b><\/strong>\u00a0<\/span>Navigating to a destination in a new and unfamiliar environment, from finding the button of an elevator, to identifying landmarks along the path while avoiding dynamic obstacles, is an everyday task that we perform predominantly using sight. Due to challenges in non-visual navigation, the most practical solution today for blind individuals traveling across unfamiliar scenarios is to seek help from a sighted person or guide. To improve independence and quality-of-life, researchers have recently developed a variety of carefully engineered prototypical technologies for addressing assistive navigation, from robotic platforms to smart-canes and smartphone-based systems. However, when moved from small lab settings to the real-world, these solutions have limited use in meeting the needs of blind users because they primarily rely on\u00a0<\/span>significant manual setup for their operation and guidance feedback properties<\/i>. Based on our preliminary analysis, the lack of customization can result in sub-optimal guidance, in particular during the most challenging navigation scenarios where certain users may need additional assistance for completing the task, e.g., open spaces, elevators, doors and entrances, etc. Consequently, existing systems are developed over pre-assumed users performing highly controlled and simplified navigation tasks. When encountering a new user (e.g., with different mobility skills or aids) or a new environment (e.g., various acoustic and layout properties), the interaction settings must be manually adjusted in a cumbersome, non-scalable process. Towards advancing the state-of-the-art of navigation technologies, our goal in this project is to develop automatically customizable assistive solutions in the context of guiding diverse blind users in unfamiliar environments. Our proposal studies user-based customization for increasing the utility of assistive navigation solutions beyond their current small-scale development scope, i.e., of narrow navigation tasks with a handful of users (generally between 3-10).<\/p>\n

Seed Grant Report:\u00a0Learning from Interactions with Blind Users for Customized and Scalable Navigation Assistance Systems (Final Report)<\/a><\/p>\n

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Task-Directed Semantic Exploration with Sparse Sensing<\/h4>
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PI:<\/b>\u00a0<\/span>Sean Andersson<\/a>, CISE affiliate and Professor (ME, SE) College of Engineering
\nCo-PI:<\/strong>\u00a0Roberto Tron<\/a>, CISE affiliate and Assistant Professor (ME, SE) College of Engineering<\/span><\/p>\n

Project Summary:\u00a0<\/span><\/b>We propose a novel approach for exploiting prior information about an environment\u00a0in robot motion planning and control, with the goal of improving efficiency in terms of power, sensing,\u00a0computation, and data storage in resource-constrained systems. Such constraints arise from the use of\u00a0centimeter-scale robots such as the Harvard Robobee, the DelFly, or similar vehicles that have extremely\u00a0limited on-board resources due to their size, from the use of robots for long-duration autonomous missions,\u00a0or requirements in other application domains. We propose to efficienctly solve a task (such as, e.g.,\u00a0finding a specific object in the environment) under this resource-constrained setting by using the fact that\u00a0broad types of environments have predictable layouts with predictable elements (such as structured indoor\u00a0environments with office rooms, hallways and other structures; or unstructured outdoor environments with\u00a0groupings of trees or meadows), and that tasks can be achieved with predictable sequences of actions that\u00a0have context-dependent success probabilities. The prior information, formally encoded via graphical models\u00a0and machine learning models, will be used to direct the limited resources of the robot to specific parts of\u00a0the environment while taking educated guesses about what was not observed, allowing the robot to take the\u00a0next action with the highest probability of overall success. By combining prior information, sparse mapping,\u00a0and perception-aware planning, we will reduce the amount of sensing needed (thus minimizing the power,\u00a0computation and on-board memory for acquiring, processing, and storing measurements), exploration time\u00a0(by acquiring only information needed to complete the task), and overall computations.<\/p>\n

Seed Grant Report: Task-Directed Semantic Exploration with Sparse Sensing (Final Report)<\/a><\/p>\n

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EasyCSPeasy: Automatic XSS Prevention<\/h4>
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PI:<\/b>\u00a0Gianluca Stringhini<\/a>, Assistant Professor (ECE) College of Engineering
\nCo-PI:<\/strong>\u00a0Manuel Egele<\/a>, CISE affiliate and Assistant Professor (ECE) College of Engineering<\/span><\/p>\n

Project Summary:<\/b>\u00a0<\/span><\/strong>Web-security is the cornerstone of our online life, and allows us to safely engage in online activities such as shopping, banking, and the management of medical records (e.g., BU\u2019s Healthway to curb the spread of COVID-19 on campus<\/span>1<\/span>). The Content Security Policy (CSP) framework ratified by the World Wide Web Consortium (W3C) has developed into a central pillar to enable a secure and trust worthy Web. Unfortunately, the policy language has become sufficiently expressive and complicated leading to most web sites eschewing the use of CSP altogether.<\/span>2\u00a0<\/span>As hypothesized by prior work [1], the reason is that defining the policy that guards a given web-site is a labor-intensive and largely manual task that does not scale well with the ever-changing nature of today\u2019s Web. Hence,\u00a0<\/span>the goal of this project is to research and develop a novel and automated capability that intelligently builds a security policy for arbitrary web-sites.\u00a0<\/span><\/i>To this end, the project will take a holistic viewpoint and address two complementary and synergistic thrusts of the web security challenge. First, the project will feature an automatic system that extracts a fine-grained CSP based on a web-site\u2019s code. However, previous research highlighted that while CSP significantly reduces the attack surface in a web application, some attacks are still possible. To mitigate this, the second thrust will automatically rewrite a web-application\u2019s source code to retrofit existing applications with the strong security primitive of Trusted Types.\u00a0<\/span><\/p>\n

Seed Grant Report: EasyCSPeasy: Automatic XSS Prevention (Final Report)\u00a0<\/a><\/p>\n

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