U-M approves new graduate certificate in computational neuroscience

By | Educational, General Interest, Happenings, News

The new Graduate Certificate in Computational Neuroscience will help bridge the gap between experimentally focused studies and quantitative modeling and analysis, giving graduate students a chance to broaden their skill sets in the diversifying field of brain science.

“The broad, practical training provided in this certificate program will help prepare both quantitatively focused and lab-based students for the increasingly cross-disciplinary job market in neuroscience,” said Victoria Booth, Professor of Mathematics and Associate Professor of Anesthesiology, who will oversee the program.

To earn the certificate, students will be required to take core computational neuroscience courses and cross-disciplinary courses outside of their home departments; participate in a specialized interdisciplinary journal club; and complete a practicum.

Cross-discplinary courses will depend on a student’s focus: students in experimental neuroscience programs will take quantitative coursework, and students in quantitative science programs such as physics, biophysics, mathematics and engineering will take neuroscience coursework.

The certificate was approved this fall, and will be jointly administered by the Neuroscience Graduate Program (NGP) and the Michigan Institute for Computational Discovery and Engineering (MICDE).

For more information, visit micde.umich.edu/comput-neuro-certificate. Enrollment is not yet open, but information sessions will be scheduled early next year. Please register for the program’s mailing list if you’re interested.

Along with the Graduate Certificate in Computational Neuroscience, U-M offers several other graduate programs aimed at training students in computational and data-intensive science, including:

  • The Graduate Certificate in Computational Discovery and Engineering, which is focused on quantitative and computing techniques that can be applied broadly to all sciences.
  • The Graduate Certificate in Data Science, which specializes in statistical and computational methods required to analyze large data sets.
  • The Ph.D in Scientific Computing, intended for students who will make extensive use of large-scale computation, computational methods, or algorithms for advanced computer architectures in their doctoral studies. This degree is awarded jointly with an existing program, so that a student receives, for example, a Ph.D in Aerospace engineering and Scientific Computing.


ACNN Big Data Neuroscience Workshop

By |


Organized by Advanced Computational Neuroscience Network (ACNN)


Come join the ACNN Big Data Neuroscience Workshop and enjoy:

❖ Keynotes and Invited Talks
❖ Data Sharing Initiatives
❖ Demonstration of Neuroscience Computational Platforms
❖ Reproducibility Best Practices
❖ Learning Environment for Students and Early-Career Researchers

Students, trainees, fellows, junior investigators from the Midwest as well outside academic institutions and industry partners are invited.

Research highlights: U-M group awarded Midwest Big Data Spoke award from NSF for Advanced Computational Neuroscience Network (ACNN)

By | General Interest, Happenings, News, Research

A group of three University of Michigan faculty members will lead the Advanced Computational Neuroscience Network project as a “spoke” in the Midwest Big Data Hub program funded by the National Science Foundation.

The Principal Investigator is Richard Gonzalez, Amos N. Tversky Collegiate Professor of the U-M Psychology Department, who has joint appointments in Statistics and Marketing, is Director of the Research Center for Group Dynamics, Research Professor in the Center for Human Growth and Development, and has affiliations with the U-M Comprehensive Cancer Center and the Center for Computational Medicine and Bioinformatics.

Co-PI’s are George Alter, professor in the History Department and the Institute for Social Research, and Ivo Dinov, associate professor in the School of Nursing and School of Medicine and Director of Statistics Online Computational Resources (SOCR), and associate director for Education and Training of the Michigan Institute for Data Science (MIDAS).

All three are affiliated faculty of MIDAS.

The ACNN program will leverage rapid technological development in sensing, imaging, and data analysis to facilitate new discoveries in neuroscience, and will foster new interdisciplinary collaborations across computing, biological, mathematical, and behavioral sciences together with partnerships in academia, industry and government. ACNN will address three specific problems relating to Big Data in neuroscience:

  • data capture, organization and management involving multiple centers and research groups
  • quality assurance, preprocessing and analysis that incorporates contextual metadata
  • data communication to software and hardware computational resources that can scale with the volume, velocity and variety of neuroscience data sets.

ACNN is a collaboration between U-M, Ohio State University, Indiana University, and Case Western Reserve University.

The BD Hubs and Spokes programs are part of a larger effort at NSF to advance data science and engineering. In Fiscal Year 2017, NSF will invest more than $110 million in Big Data research.