Read more: https://midas.umich.edu/wp-content/uploads/sites/3/2020/03/WEF_Digitizing_Transforming_Mobility_Systems_2020.pdf
Read more: theconversation.com/medical-supply-chains-are-fragile-in-the-best-of-times-and-covid-19-will-test-their-strength-133688?utm_medium=email&utm_campaign=Latest%20from%20The%20Conversation%20for%20March%2025%202020%20-%201573615061&utm_content=Latest%20from%20The%20Conversation%20for%20March%2025%202020%20-%201573615061+Version+B+CID_64f8e825eff4ffb7195f35a3d1c517fc&utm_source=campaign_monitor_us&utm_term=Medical%20supply%20chains%20are%20fragile%20in%20the%20best%20of%20times%20and%20COVID-19%20will%20test%20their%20strength
To see Qianying’s presentation, “COVID-19 Outbreak in Wuhan, China: in Retrospect and in Prospect” please click here. A captioned version will be available soon.
Abstract: Since first confirmation in December 2019, the novel coronavirus diseases (COVID-19) infected more than 50,000 people and claimed over 2000 lives in Wuhan, China. It was transmitted across the whole country shortly, and now swept the world by causing more 20,000 infections in countries other than China. Using official reported cases and assuming changing reporting ratio, we investigated the early stage of the epidemic of COVID-19 in Wuhan and analysed its transmissibility. We then built up a conceptual model and incorporated the zoonotic introduction, emigration, individual reaction, and governmental action to simulate the trends of the outbreak in Wuhan and predicted the disease would be completely controlled by the end of April under current policies. These studies provide insights into not only the characteristics of COVID-19 itself, but the impact of governmental actions.
- Education Deserts: Education deserts are geographic areas removed from post-secondary educational institutions. The presence of these institutions have a pretty big impact not only on educational access of people in their vicinity, but also on local economies and demographics. Take U of M and Ann Arbor as one outstanding example of this type of relationship. We would like to examine what features about these educational institutions have what type of impact on local socioeconomic factors.
- Oscar Winners: How can we predict which movies will win the 2020 Academy Awards? Features students are currently considering include IMDB reviews, ratings, and potentially even Twitter responses.
- Music Generation: This team is working on generating music (MIDI files) using deep learning with a transformer model.
- r/rateme analysis: rateme is a subreddit where people post pictures of themselves and ask to be rated on appearance. We’re more interested in: What are the demographic distributions (age/gender) of posters and commenters? How do these differ, and how do they interact? How predictive are age/gender in predicting ratings? How does the rating-seeking language affect the ratings on a post (i.e. if you display less confidence in posting, are people less likely to rate you harshly?)
- Congestion Pricing: Some large cities have implemented congestion pricing policies in which they charge a price for vehicles which enter the city center during peak traffic hours. The idea is that this will incentivize public transportation usage and decrease traffic during rush hours. Students are looking at London traffic data to see how effective this policy has been (London is one of the cities with this type of policy).
- Blood Pressure Estimation: We are working with Dr. Byrd from the medical school on this project, so mentors are less necessary, but I figured I’d include this just to be comprehensive. Blood pressure tends to be in flux, so a single sample is less informative than an average over the course of a day. We’ll be looking at clinical trial data and data from the UM hospital clinical warehouse to see if lab results (such as complete blood count) can be used as a good predictor of average blood pressure.
By Alex Piazza
Data science is an important tool that can help researchers tackle important societal challenges ranging from mobility and health to public safety and education.
But data science techniques and technologies also pose enormous potential for harm by reinforcing inequity and leaking private information. As a result, many sensitive datasets are restricted from research use, impeding progress in areas that impact society.
The University of Michigan, with a $2 million grant from the National Science Foundation (NSF), plans to establish a framework for a national institute that would enable research using sensitive data, while preventing misuse and misinterpretation.
“Data science has proven time and time again to be an invaluable resource when addressing emerging challenges and opportunities in areas of broad potential impact,” said H.V. Jagadish, director of the Michigan Institute for Data Science. “But having access to information comes with a great deal of responsibility, so our first priority is to ensure data science is not misused to disproportionately harm underrepresented groups.”
U-M researchers will partner with colleagues at New York University and the University of Washington over the next two years to deploy new techniques and technologies that enable responsible data science, while establishing an interdisciplinary community focused on the study, design, deployment and assessment of equitable data systems.
Equity is an important facet of data science that NSF aims to strengthen in the coming years, as the federal agency partners with universities such as U-M to enable new modes of data-driven discovery that will transform the frontiers of science and engineering.
The centerpiece of its ongoing effort, called Harnessing the Data Revolution at NSF, is the development of national institutes that address multidisciplinary problems in big data. U-M will help lay the groundwork for developing these institutes, which will eventually serve as a point of convergence for researchers from multiple disciplines to share expertise and address pressing challenges in data science.
“Information is being gathered about all of us, from our Google searches and online purchases to property tax records and social media activity,” said Margaret Levenstein, director of the Inter-university Consortium for Political and Social Research at U-M, which maintains the world’s oldest and largest archive of research and instructional data for the social and behavioral sciences. “You would assume the usage of data to be accurate and fair, but that is not always the case. That is why building a framework is so important because, in order for us to harness the enormous potential of big data, we need to ensure equity and privacy.”
H.V. Jagadish (U-M) is the principal investigator on this grant. Robert Hampshire (U-M), Bill Howe (UW), Margaret Levenstein (U-M) and Julia Stoyanovich (NYU) are co-principal investigators.
Dr. Robert Hampshire, MIDAS core faculty and Associate Professor of Public Policy at the Ford School, and his team, receives nearly $1 million in funding from the National Science Foundation’s Convergence Accelerator. The team leaders also include MIDAS faculty members Carol Flannagan, H.V. Jagadish and Margaret Levenstein. Read more at http://fordschool.umich.edu/news/2019/hampshire-receives-national-science-foundation-convergence-accelerator-grant.
MIDAS affiliated faculty and Associate Professor in Computer Science and Engineering, Dr. Mike Cafarella, receives funding from the National Science Foundation, in its program of Convergence Accelerator in Harnessing the Data Revolution. This project, “Simultaneous Knowledge Network Programming and Extraction”, is a direct result of his team’s project funded by MIDAS. Read more at https://www.nsf.gov/od/oia/convergence-accelerator/index.jsp.
See the publication at https://www.nature.com/articles/s41591-019-0548-6
For information on the press release: https://precisionhealth.umich.edu/news-events/features/taking-machine-learning-models-in-health-care-from-concept-to-bedside/