The rapidly-changing landscape of the novel coronavirus outbreak and the social-distancing requirements therein have required us to make some major adaptations to our work, academics, & life.
The Michigan Institute for Data Science (MIDAS) is offering a special round of Propelling Original Data Science (PODS) mini-grants for U-M researchers leveraging data science and artificial intelligence for COVID-19 research.
"Where America Didn’t Stay Home Even as the Virus Spread" - The New York Times
A Business Associate Agreement (BAA) was signed yesterday between U-M Michigan Medicine and Zoom. U-M entered into this BAA with Zoom in order to maintain confidentiality when Zoom might receive access to Protected Health Information (PHI).
COVID-19 time off banks are available to all full, part-time, and temporary staff. This new time, effective April 1
Congratulations, Marcus!
As U-M moves toward alternative formats for teaching in place of in-person courses for the rest of the semester, the Center for Academic Innovation has created a resource guide for teaching remotely.
Congratulations Professor Marcin Cieslik!
Congratulations to both Louis Joslyn and Edison Ong, Ph.D. candidates and recipients of the Rackham Predoctoral Fellowship !
DYNAMICS, TOPOLOGY, COMPLEXITY, AND BIOLOGY - A conference in honor of Professor Steve Smale
The first of its kind at U-M, the MIDAS Reproducibility Challenge aims to highlight high-quality, reproducible work at U-M by collecting examples of best practices across diverse fields.
Second Annual Conference of the Metabolomics Association of North America
Congratulations to Jie Cao!
Congratulations to professors Arvind Rao and Reza Soroushmehr!
The University of Michigan led the nation with 22 faculty members elected as 2019 fellows of the American Association for the Advancement of Science (AAAS). Congratulations professors Brian and Jun!
Congratulations to professors Maureen Sartor, Ryan Mills, Alan Boyle, and Arvind Rao!
Single-cell RNA sequencing (scRNA-seq) offers new opportunities to study gene expression of tens of thousands of single cells simultaneously. We present DeepImpute, a deep neural network-based imputation algorithm that uses dropout layers and loss functions to learn patterns in the data, allowing for accurate imputation.
The Mills Lab review modern approaches for investigating structural variations (SV) and proffer that, moving forwards, studies integrating biological information with detection will be necessary to comprehensively understand the impact of SV in the human genome.
