Project Grant 2403431
- This $150,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program supports the development of a cloud-based research infrastructure to enable studies on news recommender systems. The project will create an experimental news recommender community that allows researchers nationwide to test different algorithms and interfaces, and study how they affect user behavior and the diversity of information presented. The...
- The National Science Foundation awarded $349,079 under its Computer and Information Science and Engineering program (CFDA 47.070) to Stony Brook University for a collaborative research project titled "COLLABORATIVE RESEARCH: SATC: CORE: MEDIUM: APP-DRIVEN WEB BROWSING: NOVEL RISKS, VULNERABILITIES, AND DEFENSES." The project, taking place from October 1, 2022 to September 30, 2026, will analyze security risks inherent in non-browser applications that enable web browsing. Researchers...
- This Project Grant award of $400,000 from the National Science Foundation's Division of Computer and Network Systems supports research at the University of North Carolina at Chapel Hill (UNC) to develop techniques for monitoring network traffic and estimating Internet performance experienced by users. The project aims to design deep learning frameworks for classifying end-user segments based on access networks, client platforms, and application usage, as well as online sampling, hashing, and...
- The National Science Foundation Division of Computer and Network Systems awarded Princeton University a $500,000 Project Grant titled "CNS CORE: SMALL: FAST OR DYNAMIC WEBSITES? ELIMINATING THE NEED TO CHOOSE." The three-year award period runs from October 1, 2021 to September 30, 2024. The grant supports research under the NSF's Computer and Information Science and Engineering program (CFDA #47.070). This program aims to advance computing and information sciences through...
- This Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $600,000 over 3 years to the University of California, Santa Cruz (UCSC) to develop a next-generation framework for in-network lookup engines. The project, titled "NETS: SMALL: Develop Core Techniques and Applications of Learned and Disaggregated In-Network Lookups," aims to replace traditional hash functions with machine learning...
- This Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $400,000 to The Leland Stanford Junior University to research methods for encoding societal values into social media ranking algorithms. The key goals are to: 1) develop techniques for translating social science constructs into algorithmic objective functions, 2) create a library of societal objective functions based on validated theory, and...
- This Project Grant award of $720,000 from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program supports a collaborative research project between the Massachusetts Institute of Technology (MIT) and the University of Washington. The project aims to develop new machine learning-based models that can quickly and accurately predict the performance of computer networks, overcoming the trade-offs of traditional network modeling approaches. The research...
- The National Science Foundation (NSF) awarded a $916,767 Project Grant under the Computer and Information Science and Engineering (CISE) program to the University of California, Merced. The award supports the "COLLABORATIVE RESEARCH: PPOSS: LARGE: CROSS-LAYER COORDINATION AND OPTIMIZATION FOR SCALABLE AND SPARSE TENSOR NETWORKS (CROSS)" project. The project aims to develop efficient tensor network methods for handling high-dimensional and sparse data, which are prevalent in...
- This NSF CAREER project award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $599,707 to Rutgers, The State University to develop innovative algorithms, systems, and interface designs to enable efficient and scalable training of large foundational deep learning models on supercomputers. The research aims to address key challenges in the performance, scalability, and human effort required for large-scale...
- This Project Grant award from the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) provides $165,874.00 to The Johns Hopkins University for the project "COLLABORATIVE RESEARCH: CNS: MEDIUM: SCALABLE LEARNING FROM DISTRIBUTED DATA FOR WIRELESS NETWORK MANAGEMENT". The project aims to develop scalable machine learning-based analytics for wireless network management, including data compression...
COLLABORATIVE RESEARCH: NETS: MEDIUM: SCALABLE CRAWLING OF THE WEB AS EXPERIENCED BY USERS -MANY SYSTEMS THAT ARE CENTRAL TO MODERN SOCIETY ? SUCH AS WEB SEARCH ENGINES, SMART ASSISTANTS, GENERATIVE AI, AND WEB ARCHIVES ? RELY ON THE ABILITY TO AUTOMATICALLY LOAD (A.K.A. CRAWL) LARGE NUMBERS OF WEB PAGES QUICKLY. HOWEVER, WEB CRAWLER SOFTWARE THAT HAS BEEN TRADITIONALLY USED TO CRAWL THE WEB IS NOW INSUFFICIENT FOR THREE REASONS. FIRST, MANY PAGES REQUIRE USERS TO BE LOGGED IN. AS A RESULT, A TRADITIONAL CRAWLER SEES ONLY THE LOGIN PAGE AND IS BLIND TO CONTENT THAT ACTUAL USERS WOULD SEE. SECOND, THE NUMBER OF WEB PAGES IS EVER-INCREASING, AND INTERACTIVE PAGES AND WEB APPLICATIONS HAVE SIGNIFICANTLY INCREASED THE AMOUNT OF COMPUTATION NECESSARY FOR A CLIENT TO IDENTIFY ALL THE RESOURCES ON A TYPICAL PAGE. IN COMBINATION, THESE FACTORS MAKE IT SIGNIFICANTLY MORE EXPENSIVE THAN BEFORE TO CRAWL EITHER A LARGE CORPUS OF SITES OR TO RECRAWL PAGES FREQUENTLY TO CAPTURE CHANGES. THIRD, MANY PAGES ARE DYNAMIC OR INTERACTIVE, AND MANY USE EMBEDDED THIRD-PARTY SERVICES SUCH AS MAPS, SOCIAL MEDIA WIDGETS, AND LANGUAGE TRANSLATION ARE EITHER HAMPERED OR FAIL TO WORK ON CRAWLED PAGE COPIES. AS A RESULT, SYSTEMS AND STUDIES THAT RELY ON CONTENT CRAWLED FROM THE WEB LACK VISIBILITY INTO A LARGE PORTION OF THE WEB, ARE UNABLE TO KEEP UP WITH THE RATE AT WHICH THEY NEED TO CRAWL PAGES AND END UP REPLAYING CRAWLED PAGES WITH POOR FIDELITY. TO ADDRESS THESE CHALLENGES, THIS PROJECT WILL DEVELOP SPRINTER, A MODERN WEB CRAWLER CAPABLE OF CAPTURING THE WEB AND ITS RICH SERVICES AS SEEN AND EXPERIENCED BY USERS. SPRINTER WILL CRAWL ANY PAGE SUCH THAT THE CONTENT CRAWLED IS REPRESENTATIVE OF WHAT USERS SEE ON THE PAGE. ITS OVERHEADS WILL GROW SUB-LINEARLY WITH THE NUMBER OF PAGES AND THE FREQUENCY OF MONITORING. ANY PAGE CRAWLED USING SPRINTER WILL BE RENDERABLE IN A MANNER THAT CLOSELY APPROXIMATES THE ORIGINAL PAGE, BOTH VISUALLY AND FUNCTIONALLY. TO DEVELOP SPRINTER, THE PROJECT WILL MAKE RESEARCH CONTRIBUTIONS ALONG THREE DIMENSIONS. FIRST, THE PROJECT WILL USE WIDESPREAD SUPPORT FOR AUTHENTICATION VIA SINGLE SIGN-ON (SSO) PROVIDERS SUCH AS GOOGLE AND FACEBOOK AND GENERATE REPRESENTATIVE BROWSING PROFILES FROM PRIVACY-PRESERVING NETWORK TRACES. SECOND, TO MAKE SPRINTER?S CRAWLING EFFICIENT, THE PROJECT WILL DEVISE TECHNIQUES TO REUSE APPLICATION COMPUTATIONS ACROSS SIMILAR PAGES AND TO IDENTIFY A SMALL REPRESENTATIVE SUBSET OF PAGES THAT SPRINTER NEEDS TO MEASURE AT HIGH FREQUENCY. LASTLY, TO ENABLE HIGH-FIDELITY REPLAY OF THE CRAWLED COPY OF A PAGE, THE PROJECT WILL DEVELOP METHODS TO CRAWL ALL OF THE PAGE?S RESOURCES THAT WILL BE NEEDED TO SERVE ANY COMMON LOAD OF THAT COPY. A MAJOR BROADER IMPACT IS IN THE RESEARCH AND USE CASES THAT SPRINTER ENABLES FOR THE COMMUNITY. FURTHER, SPRINTER AND THE RESULTS OF ITS CRAWLS WILL BE MADE AVAILABLE TO OTHER RESEARCHERS. THIS AWARD REFLECTS NSF'S STATUTORY MISSION AND HAS BEEN DEEMED WORTHY OF SUPPORT THROUGH EVALUATION USING THE FOUNDATION'S INTELLECTUAL MERIT AND BROADER IMPACTS REVIEW CRITERIA.- SUBAWARDS ARE NOT PLANNED FOR THIS AWARD.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $50.0k | 9/11/25 | ||
| Not listed | $400.0k | 7/15/24 |