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ICDS awards computational and data sciences seed grants to Eberly researchers
6 May 2020
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diagram for ICDS seed grants

Understanding polarized political opinions, predicting Arctic Sea ice levels, and accelerating quantum computing with machine learning — these are just a few focuses of the dozens of new Penn State research projects that have been funded by Institute for Computational and Data Sciences seed grants, in conjunction with supplemental funding from across the University.

Of the 51 proposals received, 32 projects were funded. ICDS awarded more than $650,000 in funding; together with supplemental funds from colleges, the 32 projects received  $725,893.00 in support. The projects include 57 researchers from 12 Penn State colleges and 31 academic departments, as well as the Applied Research Laboratory. Eight of these projects include researchers from the Eberly College of Science.

This was the first round of seed grants awarded since the institute changed its name in response to its rapid growth and expanding mission. The record number of proposals received points to how prevalent computational and data sciences are at Penn State.

“The possibilities for applying data science and computational science approaches are endless,” said Jenni Evans, director of ICDS and professor of meteorology and atmospheric science. “Projects such as identifying robust renewable energy policies or investigating risks associated with the child welfare system speak naturally to Penn State's leadership in interdisciplinary research and to the diversity of research made possible in this environment.”

Awarded projects in the college include:

“WebSciV - Incorporation and visualization of various scientific sources using AI in a multi-layer web-based platform” 

  • PI: Jean-Paul Armache, assistant professor of biochemistry and molecular biology and ICDS associate

“Advancing the Use of Deep Learning in Research at Penn State”

  • PI: Doug Cowen, professor of physics and astronomy and astrophysics
  • Co-PI: Adri Van Duin, professor of mechanical engineering

“Using machine-learning methods to dissect the role of complex genetic interactions towards neurodevelopmental phenotypes”

  • PI: Santhosh Girirajan, associate professor of biochemistry and molecular biology
  • Co-PI: Naomi Altman, professor emeritus of statistics and bioinformatics

“Deep Clean: Gravitational wave inference in non-stationary noise”

  • PI: Chad Hanna, associate professor of physics and astronomy and astrophysics; ICDS co-hire
  • Co-PI: Bangalore Sathyaprakash, Elsbach Professor of Physics and professor of astronomy and astrophysics

“Computational Phenotyping: Creating a High Performance Computing Infrastructure”

  • PI: John Liechty, professor of marketing and statistics

"Web Services and Infrastructure for Bioinformatics and Biophysics”

  • PI: Ed O’Brien, associate professor of chemistry and ICDS co-hire

“From Zombie Ants to Constrained Interactive Networks”

  • PI: Christian Peco Regales, assistant professor of engineering science and mechanics
  • Co-PI: David Hughes, associate professor of entomology and biology

“New Methods and Algorithms for Non-convex Problems in Machine Learning and High-Dimensional Data Analysis”

  • PI: Lingzhou Xue, associate professor of statistics
  • Co-PI: Xiang Zhan, assistant professor of biostatistics, College of Medicine