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Data Science Approaches for Neuroscientists

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Neuroscientists are now collecting datasets of unprecedented scale thanks to technological advances. Yet, there are many unanswered questions that must be addressed to keep moving the field forward.

In this webinar moderated by Gardiner von Trapp, panelists Michael Miller, Richard Myers, and Pascal Wallisch will discuss:

  • What types of research problems can a data-science approach solve?
  • How will neuroscientists analyze large-scale data sets most effectively, and with what tools?
  • What training challenges do mentors and trainees face for implementing large-scale data science practices?
  • What skills are valuable for neuroscientists to have to successfully understand and adopt this approach?

Register now to learn why a data-science approach is an exciting horizon in neuroscience research, and what it means for training, whether you are a PI, professor, or trainee.

Check out these additional data science resources from the webinar speakers. 

Register here


Speakers:

Gardiner von Trapp, PhDGardiner von Trapp, PhD

Gardiner von Trapp is a member of SfN’s Trainee Advisory Committee and a National Research Service Award winner. He recently received his PhD from New York University's Center for Neural Science. His dissertation work focused on using quantitative and experimental methods to understand the relationship between auditory cortical neurophysiology and perception in normal and hearing loss populations.


Michael Miller, PhD

Michael Miller, PhD

Michael Miller is a University Gilman Scholar and the Herschel and Ruth Seder Professor of Biomedical Engineering at Johns Hopkins University (JHU). He is also the director for the JHU Center for Imaging Science and co-director of the Kavli Neuroscience Discovery Institute, which brings together neuroscience, engineering, and data science researchers to pursue the ultimate goal of reaching a unified understanding of brain function. Miller’s laboratory pursues research interests in computational anatomy, brain mapping, computational neuroscience, and medical imaging. He received his PhD in biomedical engineering from Johns Hopkins University.


Richard H. Myers, PhD

Richard H. Myers, PhD

Richard Myers is a professor of neurology and the Aubrey Milunsky Chair in Human Genetics at the Boston University School of Medicine, where he is also the director of the Genome Science Institute. He is a PI for several NIH-funded projects investigating the transcriptional regulation of genes implicated in Parkinson’s disease and Huntington’s disease. Myers has published more than 250 peer-reviewed articles. He earned his PhD in behavior genetics from Georgia State University, and completed postdoctoral training in human genetics at Emory University. 


Pascal Wallisch, PhD

Pascal Wallisch, PhD

 Pascal Wallisch serves as a professor in the department of psychology at New York University, currently teaching statistics, programming, and the use of mathematical tools in neuroscience and psychology. He received his PhD in psychology from the University of Chicago and worked as a postdoctoral fellow at the Center for Neural Science at New York University. He co-founded and co-organizes the “Neural Data Science” summer course at Cold Spring Harbor Laboratory and co-authored Matlab for Neuroscientists.

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Sparking Global Conversations Around Neuroscience