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.
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
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!
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/
Seven outstanding young data scientists from the US, Asia and Europe will join the Michigan Institute for Data Science (MIDAS) at the University of Michigan (U-M), as the inaugural cohort of the Michigan Data Science Fellows program. They will work at the boundaries of data science methods and domain sciences in an intellectually vibrant environment and develop collaborative relationships with the U-M data science community. The Fellows and their data science application areas are:
- Arya Farahi, coming from Carnegie Mellon University: Cosmology and its intersection with fundamental physics.
- Qianying (Ruby) Lin, coming from Hong Kong Polytechnic University: Epidemic inferences and trends.
- Patrick Park, currently at U-M: Structure and evolution of large-scale human social networks.
- Elyas Sabeti, currently at U-M: Theory and algorithms for the analysis of medical Big Data.
- Maria Veiga, coming from the University of Zurich: Developing techniques for multi-scale modeling.
- Edgar Vivanco (joint postdoctoral fellow with the National Center for Institutional Diversity), coming from Stanford University: Utilizing machine learning to examine how colonial-era institutions and contemporary criminal violence shape economic under-performance.
- Blair Winograd, currently at U-M: working with M-Write to combine conceptual writing prompts, automated peer review, natural language processing, and automated personalized feedback to create an infrastructure for writing at scale.
The two-year Fellows Program accepts recent PhDs who are stars in their respective fields and whose work is in data science. They are expected to be more independent than the average postdoctoral researchers at the same career juncture; however, each Fellow also has two faculty sponsors, one a methodology expert, and the other an expert in an application domain, to ensure scientific and career guidance.
The Fellows program is a new component of MIDAS’ effort to catalyze the transformative use of Data Science in a wide range of disciplines to achieve lasting societal impact, through research, education, outreach and partnership. “This is the first postdoctoral training program at U-M, and one of the few in the nation, with data science as the explicit focus,” says Dr. H.V. Jagadish, MIDAS Director and Professor of Computer Science and Engineering, “and we hope this program will foster the next generation of data science leaders with both a strong scientific vision and a commitment to using data science for positive societal impact.”
One of the Fellows, Elyas Sabeti, expressed great enthusiasm: “This is such a unique opportunity. It’s amazing that I will be working side by side with people who study Physics, Education, Political Science… I can’t wait to find out how many great ideas we can come up with together.”
For more information on the Fellows, please click here.