| 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 | |
| Tarek Abdelzaher | zaher@illinois.edu | Id be very interested in finding collaborators from epidemiological modeling! | My recent work models cascade propagation (primarily information cascades on social media). Since information (not unlike viruses) propagates by human contact via a “facilitating medium� (perhaps an online subreddit or a Facebook wall, or perhaps proximity at a grocery store), there are interesting analogies and possibly novel insights that I’d like to bring to the table for understanding/predicting COVID-19 spread; especially understanding the effects of social distancing (removal of some “facilitating media�). My other interest is to use public data collected from social media, such as Twitter and Reddit, to fill-in missing pieces in cascade propagation and after-effect evolution dynamics (e.g., measuring prevalence of symptom discussions/queries, understanding social response to distancing, estimating economic impact, mapping emerging shortages, surveying attitudes to executive orders, etc). My other projects relate to Internet of Things (IoT) and AI applications. Here is my publications page: http://abdelzaher.cs.illinois.edu/publications.html | http://abdelzaher.cs.illinois.edu/publications.html | |
| Kevin Leicht | kleicht@illinois.edu | Web scraping, twitter, facebook, reddit and anything having to do with cell phones/geo-tracking. | I've got two research teams going right now. One is an extension of my NCSA fellowship and will attempt to track, via twitter, facebook, and social media, the spread of bogus or dubious coronavirus information. The goal will be to construct network models that tie social media spread to existing cultural, economic and political conditions in localities. The group is already preparing a RAPID via NSF that should be submitted before the C3ai deadline of May 1st. We presently have Loretta Auvil on the project. We would like to keep her, but if you have any other suggests that would be very helpful. Thanks! | https://forms.illinois.edu/fileAuth/433489_Q4_date_20200408_id_318906.docx | |
| Lavanya Marla | lavanyam@illinois.edu | AI and ML | Supply chain management and logistics solutions to (a) plan supplies of devices, equipment and people, (b) demand management using community engagement, (c) logistics of restarting the economy through collaboration between multiple agencies by scheduling how workforce is gradually released from stay-at-home. | https://publish.illinois.edu/lavanyam/ | https://forms.illinois.edu/fileAuth/717318_Q4_date_20200408_id_318958.pdf |
| Weihao Ge | wge2@illinois.edu | epidemiology, agile app development, cloud computing, GIS | I would like to use the data from some self-reporting app and the WHO data to select models that would be suitable to predict the risk in certain areas. The program starts with several hypothetical models for epidemiology and select the best model for prediction with real-time data. | https://forms.illinois.edu/fileAuth/268046_Q4_date_20200408_id_318980.pdf | |
| Liudmila Sergeevna Mainzer | lmainzer@illinois.edu | virology, epidemiology, civil engineering (e.g. transport), community service | My team can provide software development support, including agile update of machine learning models, and deployment in the cloud; we have worked with West Nile Virus incidence predictive models. | https://wiki.ncsa.illinois.edu/display/CPRHD also see https://wiki.ncsa.illinois.edu/display/LH | https://forms.illinois.edu/fileAuth/325829_Q4_date_20200408_id_319085.doc |
| Jessie Chin | chin5@illinois.edu | I would like to study the generation of crowd wisdom in the emerging science of COVID-19, especially how collective acquisition or creation of knowledge about an uncertain area of knowledge. | https://forms.illinois.edu/fileAuth/357132_Q4_date_20200409_id_319152.pdf | ||
| Kevin Wise | krwise@illinois.edu | Since my own research focuses on the processes and effects of media use, I'm keenly interested in exploring the role of media use as both antecedents and consequences of human behavior pertaining to COVID-19. Both media use and human behavior can be construed very broadly. Media use variables could include things like viewership of live press briefings, social media activity, primary news sources (e.g. television, newspaper, social media) or even daily visits to public-facing data aggregators (e.g. Johns Hopkins). Human behavior could include both perceptions (e.g. efficacy of precautionary measures, source credibility of various actors) and actions (e.g. social distancing, shopping). I would expect that data relevant to these phenomena are available within the C3.ai and Azure platforms and I would love to join a team (even if only at the periphery) that would facilitate their exploration. | https://forms.illinois.edu/fileAuth/162544_Q4_date_20200409_id_319191.pdf | ||
| Tamas Ambrisko | tambrisk@illinois.edu | Digital Signal Processing expert Medical Doctors on the field of ICU or Cardiology | Developing an ECG telemetry system that is capable of data collection from multiple patients and facilitates advanced signal analysis such as high frequency electrocardiography. We plan to use this device to organize a clinical study using COVID-19 patients. The aim is to correlate ECG signal features with patient outcome using Machine Learning. | https://forms.illinois.edu/fileAuth/457490_Q4_date_20200409_id_318949.pdf |
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