MIDAS Fellow, Arya Farahi, publishes manuscript on how the age of Dark Matter halos govern their content.

By | News, Research
Dark Matter halos are the most massive gravitationally bound objects in our Universe. These halos host the majority of baryonic matter in the Universe in the form of hot gas and cold stellar phase. Determining how baryons are partitioned into these phases is challenging and requires detailed modeling of galaxy formation and their assembly history. By employing a suite of cosmological simulations, Farahi et al. show that formation time of the same mass halos is strongly correlated with their gas and stellar content. This implies that formation time is one of the key factors that governs the content and form of the Baryons within the dark matter halos. Incorporating this information has the potential to improve our understanding of the fundamental physics of our Universe using galaxy cluster abundance and allows us to gain insight into the matter evolution within these systems.
Read more on the publication here:

Rada Mihalcea named ACM Fellow

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Rada Mihalcea, has been named a Fellow of the Association for Computing Machinery (ACM).  ACM Fellows comprise an elite group that represents less than 1% of the Association’s global membership.  This distinction recognizes those with far-reaching accomplishments that define the digital age.

MIDAS affiliated faculty, Brian Athey, Jun Li, and Bill Currie earn the lifetime distinction of AAAS Fellow

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443 leading scientists are elected in 2019 as Fellows of the American Association for the Advancement of Science (AAAS), in honor of their invaluable contributions to science and technology.  U-M leads the way with 22 elected Fellows, including three MIDAS faculty members:

Brian Athey, Computational Medicine and Bioinformatics

Bill Currie, School for Environment and Sustainability

Jun Li, Computational Medicine and Bioinformatics

MDST Mentors

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The Michigan Data Science Team, sponsored by MIDAS and comprised of students of all levels from various schools/colleges at UM, is looking for additional faculty or post-doc mentors that could help guide student projects.  If you are interested in providing domain-specific expertise or contributing to any of the following topics, please contact MIDAS Education Program Manager, Trisha Fountain (tvfount@umich.edu), and she will connect you with the appropriate members of the team.
Student projects for this semester include:
  1. 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.
  2. 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.
  3. Music Generation: This team is working on generating music (MIDI files) using deep learning with a transformer model.
  4. 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?)
  5. 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).
  6. 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.

MIDAS faculty Yuanfang Guan and GuanLab member Xianghao Chen won first prize in the 2019 DDI National Data Science Challenge

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The 2019 Discover Innovate Impact (DII) National Data Science Challenge winning teams were recently announced and the GuanLab Team (Yuanfang Guan and Xianghao Chen) won the Task 1, Sepsis Onset Prediction prize.  This was a national data science challenge established to advance human health through machine learning hosted by The University of Texas (UTHealth) School of Biomedical Informatics, sponsored by Cerner Corporation, and powered by AWS.  Congratulations to GuanLab Team!

MIDAS core faculty, Dr. Robert Hampshire, leads a team of MIDAS faculty to receive NSF Convergence Accelerator grant

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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, Dr. Mike Cafarella, receives funding from NSF’s Convergence Accelerator in Harnessing the Data Revolution

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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.