Bo Luo - US grants

The Treatise on Invertebrate Paleontology, founded in 1948 by an international consortium of paleontological societies, is considered to be the most authoritative compilation of data on invertebrate fossils. It holds an almost biblical significance and is to be found in every good library. The Treatise has found applications in many areas, such as understanding evolution, studying climate change, and finding fossil fuels.

In order to maximize the possible benefit from this landmark effort, there is a strong desire in the paleontological community to make this vast repository of paleontological data available in electronic form for current and future scientists and laypeople. The object of the proposed research is to facilitate knowledge discovery activities in invertebrate paleontology by providing scientists with a general framework that takes advantage of the rich information extracted from the Treatise. The first mission is to turn the Treatise data into available knowledge. The second mission is to develop computational tools for analyzing, modeling, and visualizing paleontological data in order to facilitate knowledge discovery.

The knowledgebase we propose to develop will play a central role in paleontological data management. It will facilitate paleontologists to further explore unexploited areas and to raise and answer research questions that are unsolvable under the current paradigm. Furthermore, it will provide a paradigm shift from the book-based knowledge system, which is perceived as supporting evidence of mainstream research and provides little knowledge regarding the patterns and relationships that are embedded within them, to a unified framework in which computational analysis and modeling are integrated with knowledge to derive a new era of paleontological research. Consequently, the knowledgebase will likely open transformational opportunities in scientific discovery to help understand the complexity of nature. Additionally, the website we will create will enable anyone to explore the world of fossils on the world-wide web, and to link with the University of Kansas Natural History Museum and their outreach programs for K-12 students and the general public.

The Greater Kansas Area Security Workshop (KanSec) provides a regular forum for researchers and students across Kansas and the neighboring states to present research and educational activities in the field of cybersecurity, and to promote interactions and collaborations. With three NSA/DHS National Centers of Academic Excellence in Information Assurance Education (CAE/IAE) and IA Research (CAE/R) in the State of Kansas, substantial cybersecurity research and educational activities are taking place. KanSec was formed to encourage collaboration despite the geographical remoteness of the area. The first two KanSec workshops, in Spring and Fall 2012 respectively, included over 50 participants each, with about 10 student presentations at each event. The next workshop is scheduled to be hosted at the University of Kansas in the Spring of 2013, and has received inquiries about participation from universities in neighboring states including Oklahoma, Nebraska, and Arkansas. To improve the quality of the workshop and to give students better experiences, KanSec will invite renowned researchers as keynote speakers for future KanSec workshops.

This grant supports student travel to the KanSec workshops in spring and fall 2013.

The 2014 Ebola epidemic is the largest in history, affecting multiple countries in West Africa, and now impacting the US and other countries worldwide. The US Center for Disease Control and Prevention (CDC) and partners are taking precautions to prevent the further spread of Ebola within the United States. There is a lack of public understanding of the risks associated with Ebola; witness the inconsistently applied local responses (such as quarantines) that do not match CDC recommendations. This project will develop technology to enable individuals to evaluate risks associated with their own past and planned activities and travel. This will both enable those at risk to take appropriate action, and reduce unwarranted demand on the healthcare system by reassuring those whose activities have not placed them at risk. This project will use data gathered from the CDC and other public sources to develop risk models, and develop a mobile app that will use this data along with the user's own location and activity history and plans to report individual risk to the user. An individual's data never leaves their own device, ensuring personal privacy. The resulting lessons learned will ease the process of developing similar individualized risk assessment tools for future epidemics, providing long-term benefits beyond the Ebola virus epidemic.

The research will address three main issues. The first is focused crawling of structured (CDC Contact Tracing reports) and unstructured (social media, web blogs) information on time, location, and activities of Ebola patients. A second research challenge is patient activity modeling: Given the returned information, developing a time/space/activity model determining the risk of the patient acting as a transmission agent. Finally, the project will develop a mobile app that tracks time, location, and activities of the mobile device user, and retrieves the patient activity models developed from public data to determine if the user is at risk of infection. This is a complex problem, as the data may be non-specific and require inferential techniques to estimate risk (e.g., being in the same time/location as a transmission agent poses very different risk if the location is a sports stadium as opposed to a restaurant); the project will develop ontologies for activities to use in estimating risk. The project will use expert opinion to seed regression models for risk assessment. Lessons learned from this project will also identify challenges for future research in information integration, risk analysis, machine learning, and privacy preserving technologies.

Online social networks, such as Facebook, Twitter, and Google+, have become extremely popular. They have significantly changed our behaviors for sharing information and socializing, especially among the younger generation. However, the extreme popularity of such online social networks has become a double-edged sword -- while promoting online socialization, these systems also raise privacy issues. To protect user privacy without compromising socialization functions, this project articulates a unifying framework that bridges the gap between the human-oriented and technology-centered perspectives. In particular, this project is developing methods to (1) detect the discrepancies between users' information sharing expectations and actual information disclosure; (2) design a user-centered yet computationally-efficient formal model of user privacy in social networks; and (3) develop a mechanism to effectively enforce privacy policies in the proposed model. The potential long-term social benefits are significant, since such awareness may gradually change people's privacy perceptions and affect their behavior in privacy-centric scenarios.

This project develops a concept of "Social Circles" to model social network access within a Restricted Access and Limited Control framework. Methods are being developed to derive social circles from a variety of types of existing information within the social network; these are used to determine appropriate access control settings. The project is assessing information flow and risk of leakage given such settings, including the issues raised by heterogeneity of systems. In addition to theoretical analysis of potential information flows with respect to a variety of adversary models, the project is conducting user studies to determine if this approach reduces the gap between perceived and actual privacy.

This proposal will inaugurate a CyberCorps®: Scholarship for Service (SFS) program at the University of Kansas under the name of Jayhawk SFS. The goals of the proposed Jayhawk SFS program are: establish an active, well-managed, student-centered SFS program; recruit outstanding students who are dedicated to cyber-security profession and government service; provide them with superior education and professional training, achieve 100% student retention and placement; and expand research and education capacities at the host institution. The Jayhawk SFS program will be evaluated internally and externally to ensure the quality and continuous development of the program. Promotion, recruitment, education, research, professional development, outreach, placement and evaluation plans are carefully crafted to identify well-qualified and highly-motivated students, provide them with outstanding education and training experience, and in-turn expand the education and research capacities at the host institution.

The proposed project will produce well-trained security professionals to serve the needs of government agencies. The program will closely work with the 1st Infantry Division at Fort Riley, Kansas National Guard, U.S. Army Cyber Command (ARCYBER), and the Graduate Military Program at KU to recruit transitioning soldiers and veterans. In addition, the Project Team will extensively collaborate with University TRIO programs, such as the McNair Scholars Program, to recruit underrepresented and underserved students (e.g., African-Americans, Hispanic-Americans, indigenous students, and first-generation college students). The proposed program will also increase the representation of minorities in cybersecurity education through a variety of enrichment activities. Through outreach and community-service activities, the Jayhawk SFS program will highlight cybersecurity issues to the community, introduce educational opportunities, and stimulate public awareness with the long-term goal of changing people?s security and privacy perceptions and improving their security behaviors in daily life.

As the world becomes more connected every day, the topic of information security becomes more important. While researchers and students from universities across the nation are conducting important research and outreach that results in advances in information security, the impact of these efforts could be widened at a forum to share research findings, best practices, foster collaboration, and provide a forum for students to interact with other scientists and practitioners in the field. The proposed project from the University of Kansas requests funds to provide travel support for students attending the Central Area Networking and Security Workshop (CANSec). The CANSec Workshop is an annual regional workshop that aims to bring together researchers and practitioners in security and networking related fields across the Great Plains region. Since its inception, eleven workshops have been successfully held. The workshop will offer a variety of sessions including keynote talks, research presentations, posters, industrial/academic panels, and cyber defense competitions. It provides students a chance to present their ideas and research to other students, faculty, and professionals in a constructive environment, and to learn and practice cyber defense skills in a real-world scenario.

The objective of the Central Area Networking and Security Workshop (CANSec) is to provide a regular forum for researchers and students across several Midwestern states (Kansas, Missouri, Colorado, Iowa, Arkansas, Oklahoma, Nebraska, Texas, and Indiana) to present research and educational activities, and to promote interactions and collaborations. This grant would provide travel support for students attending CANSec, so they can interact with peers and faculty from across the region. The CANSec organizing team strives to keep low costs for the attendees to accommodate more student participants and a broader audience. The CANSec workshop also promotes the development and collaboration in information security research and education in the Great Plains region with several EPSCoR states including Kansas, Oklahoma, Arkansas and Nebraska.

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.

This IUCRC planning project will establish the feasibility of a multi-site Center for High-Assurance Secure Systems and Internet-of-Things (CHASSI) that will focus on areas where both security and high assurance are necessary to support operations of high mission criticality, due to safety or economic impact. Examples include medical devices, manufacturing, the energy grid, real-time financial markets, construction, and defense. Combining security and high-assurance is hard, however, intentionally combining them will lead to new models, techniques, designs, architectures, and systems that will be applicable across of range of important U.S. industries.

CHASSI has five sites: University of Kansas (KU), University of Minnesota, Syracuse University, Case Western Reserve University, and Indiana University. CHASSI research falls into four main thrusts: (1) Architectures, design and formal modeling for systems-level security, privacy, stability, and performance; (2) Secure communication, sensing, and devices; (3) Scalable trust and privacy; and (4) Human behavior for privacy and security. KU brings expertise in attestation, networking, cyber-physical systems, plus hardware, software, database, and mobile security. Complementary expertise at other sites includes mission assurance and systems security, assurance of medical devices, industrial Internet-of-Things (IIoT), manufacturing and energy applications, and human factors.

CHASSI faculty members will gain an understanding of the specific interests and actual needs/barriers of industrial companies. Likewise, companies will benefit from exposure to: cutting-edge university research across all sites; networking with and learning best practices from other industry colleagues in and out of their sector; students who may be potential hires; and faculty that might perform center projects or proprietary research. During the planning period, KU will explore ways to advance diversity and outreach with IHAWKe (Indigeneous, Hispanic, African-American, Women KU Engineering) and Women in Computing through recruiting prospective students, educating current students, and identifying student researchers.

The collaborators from this multi-university-industry Center will host a single Center-wide repository at: https://chassi.ku.edu. This shared repository will include meeting materials, program information, publications, etc., and will be made available for a minimum of 5 years after conclusion of the award or until the Center transitions to the next phase.

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.

Federated learning enables hospitals to collaboratively learn a shared global model while ensuring patient privacy; however, there is a big statistical challenge for our application owing to EHR heterogeneities, i.e. difference in patient characteristics and clinical observations made or feature space. Thus, real-world EHR data from different hospitals are never independently and identically distributed (IID). The proposed research is to overcome this statistical challenge while improving security for federated learning by
leveraging a large integrated EHR dataset with medical records for more than 21 million patients from 12 healthcare systems spanning across 9 US states. A novel privacy-preserving federated transfer learning framework is proposed for building a robust and accurate AKI prediction model that require learning on real-world EHR data from siloed healthcare systems. This project will (1) develop novel transfer learning solutions to address three distinct non-IID EHR data analytic scenarios, (2) develop a novel federated learning framework with a dynamic weighting aggregation mechanism to build a robust and accurate Acute kidney injury (AKI) prediction model; and (3) develop a comprehensive privacy-preserving federated transfer learning framework with novel privacy-preserving solutions to address the unique privacy challenges in the proposed transfer learning applications.


The project proposes new transfer learning solutions to combat the non-IID challenge in federated learning and new security building blocks tailored for homogeneous and heterogeneous transfer learning tasks. Together the project will develop a privacy-preserving federated transfer learning framework to provide a first practical solution for non-IID clinical data scenarios. Our research methods and findings will provide promising new directions to machine learning for healthcare and will contribute to both academic research and potential commercialized products. More importantly, the interpretable nature of the base gradient boosting machine model in the proposed federated transfer learning framework will provide better understanding of the predictors from which clinicians can use to design prevention and management strategies for high-risk patients.
This project is jointly funded by Smart and Connected Health and the Established Program to Stimulate Competitive Research (EPSCoR).

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.