| Name | Research Expertise | Proposed Contribution | Website | CV or Biosketch | |
|---|---|---|---|---|---|
| Flavia C D Andrade | fandrade@illinois.edu | Many aspects of the COVID-19 require knowledge about demography (e.g. age and sex patterns; mortality), social disparities (i.e., access to healthcare, immigration status and testing), and public health. I have expertise in these areas that can be helpful for developing projects. However, I do not have training in the methods expected in the proposal, so I would not be in a position to be a PI, but can contribute to one. | https://forms.illinois.edu/fileAuth/137149_Q4_date_20200406_id_318371.docx | ||
| Kevin Tan | kevintan@illinois.edu | I am interested in developing a research project on studying the social and emotional impacts on K-12 students, teachers, and families. I am not sure how and where to start but looking for potential collaborators. I'm happy to serve as PI/Co-PI. It probably fits under this topic for research award: Improving societal resilience in response to the spread of COVID-19 Pandemic | I will be able to provide expertise on social and emotional development of young people and families. I have a number of pre-existing projects in our local community. | https://forms.illinois.edu/fileAuth/37029_Q4_date_20200406_id_318375.pdf | |
| Leyi Wang | leyiwang@illinois.edu | data analysis for covid-19 virus sequence | I am veterinary virologist and our lab is the first to detect covid-19 in tigers from bronx zoo. I can provide my expertise to another PI proposal as co-PI. | https://forms.illinois.edu/fileAuth/958322_Q4_date_20200406_id_318396.doc | |
| Kris Hauser | kkhauser@illinois.edu | Clinical expertise, human factors, telepresence | Robotic telepresence could have a major impact on COVID-19 response. By keeping human-human contact to a minimum, this technology could help keep healthcare workers from being infected, and could help other workers do their jobs safely even under pandemic conditions. My lab has two telepresence platforms, one a custom-built two-handed mobile manipulator, and another commercial telepresence robot. The former platform is intended for tele-nursing, but as a general-purpose mobile manipulator it could also be applied to other jobs. I would also be interested in studying how to cheaply augment existing commercial telepresence robots ("Skype on wheels") to produce better experiences, either through VR, or additional actuation capabilities. | kkhauser.web.illinois.edu/kris.hauser.cv.pdf | |
| Michel Regenwetter | regenwet@illinois.edu | Secure behavioral web experiment at very large scale. Access to and execution of large scale and fast collection, processing, and public release (to provide access to behavioral scientists world-wide), of behavioral data. This includes the need of expertise in recruiting a large number of healthy, sick and recovered patients. | Collect large scale behavioral economics and psychology data for public domain from healthy, sick, recovered covid patients, pandemic health workers (in collaboration with OSF who are on board, and maybe other health networks). Domains of inquiry include knowledge (risk literacy, health literacy), beliefs (related to covid), reasoning about risk, moral and ethical judgment related to covid, attitudes towards relevant policies. Quantitative behavioral analyses ranging from individual to collective behavior, including consensus analysis. Comparison of health professionals with healthy, sick, recovered patients, comparison of insured and uninsured. Generating a large de-identified behavioral database for scholars worldwide. | https://forms.illinois.edu/fileAuth/373176_Q4_date_20200407_id_318509.pdf | |
| Niao He | niaohe@illinois.edu | public health, medicine, or epidemiology disciplines | 1. Efficient and robust macro prediction of pandemics based on micro event histories under missing information 2. Using temporal point processes to understand the self and mutual excitation, clustering effects of COVID-19 pandemics | https://forms.illinois.edu/fileAuth/574397_Q4_date_20200407_id_318545.pdf | |
| Volodymyr Kindratenko | kindrtnk@illinois.edu | ML/DL models development datasets development | development of a dataset and models for NLP-based analysis of the relevant literature | https://forms.illinois.edu/fileAuth/774417_Q4_date_20200407_id_318573.pdf | |
| Jana Diesner | jdiesner@illinois.edu | Can offer: - Expertise in social computing/ computational social science, human-centered data science, social network analysis, FATE (fairness, accountability, transparency, ethics), natural language processing under consideration of culture and context Interested in: - impact of crisis mode, uncertainty, distancing on socio-economic environments and coping mechanisms | http://jdiesnerlab.ischool.illinois.edu/publications/CV_JanaDiesner.pdf | https://forms.illinois.edu/fileAuth/971592_Q4_date_20200407_id_318671.pdf | |
| Hongyan Liang | liangh@illinois.edu | 1). Modeling, simulation, prediction of COVID-19 propagation and efficacy of interventions 2) Logistics and optimization analysis for design of public health strategies and interventions | https://forms.illinois.edu/fileAuth/37353_Q4_date_20200407_id_318703.pdf | ||
| Venera Bekteshi | vb456@bath.ac.uk | Sociodeterminants of health | Compare and contrast comprehensive U.S. and U.K, Germany and China responses to COVID-19 including medical, political and social responses to preventing the spread. Suggest recommendations for future more effective responses. | https://forms.illinois.edu/fileAuth/102701_Q4_date_20200407_id_318721.docx | |
| Nathan Castillo | nathanc@illinois.edu | Digital learning design with an ability to develop solutions for impoverished, under-resourced communities. | My aim is to develop a digital learning solution so that future pandemics or other disasters don't erase progress toward learning equity. COVID-19 has has presented a major barrier for continuing progress toward learning objectives among children of poorer households without computers or high-powered connectivity. Importantly, this proposal would develop a pro-poor solution to provide continuity of learning and instruction for affected communities through an appropriate, low-bandwidth digital learning approach. | https://forms.illinois.edu/fileAuth/9822_Q4_date_20200407_id_318734.pdf | |
| Brighten Godfrey | pbg@illinois.edu | Looking for a PI who can use my group's expertise -- specifically, networking, systems, cloud, algorithms; and recently some work in fast maximum likelihood estimation. (Details below.) I'm not currently planning to lead a proposal on my own. | Interests: networked systems and algorithms, including low-latency communication, high performance transport algorithms, cloud & data centers, network security, applications of ML to networked systems, and network analysis. COVID-related work could possibly involve apps needing low-latency communication or cloud support, new kinds of bandwidth demands, data center performance, network security, or graph algorithms & analysis. In addition, we have a project on fast maximum likelihood inference in discrete domains, which could possibly be relevant. | http://pbg.cs.illinois.edu/tmp/cv.pdf | |
| Yuguo Chen | yuguo@illinois.edu | Build network models to study the spread of the disease, estimate the number of infected but untested people based on observations of those that have been diagnosed, use simulation to study the spread of the disease under different types of interventions. | https://publish.illinois.edu/yuguo/ | ||
| Yi Lu | yi-lu@illinois.edu | Experts in machine learning (ML), artificial intelligence (AI) or the internet of things (IoT) who are interested in collaboration with my group on transforming the SARS-Cov-2 infect-ability data we can collect using smartphone-based biosensors (see my group's expertise in the next item) on patients and surfaces in public places into data in cloud and analyzing them using AI algorithm. | While many COVID-19 diagnostic tests have been developed and used, few test, if any, can inform infect-ability (i.e., whether the SARS-Cov-2 virus is infectious or not). We have developed a method for rapid, direct and portable detection of viruses that can inform infect-ability of the the virus of both samples in patients and surfaces of the public places (e.g., hospitals, airports and grocery stores). When interfaced the biosensors with smartphones, the information about infect-ability of the virus will allow cloud-based analyses to inform actions for COVID-19 and future viral outbreak. | https://forms.illinois.edu/fileAuth/225765_Q4_date_20200408_id_318864.pdf | |
Overview
Content Tools