jar filled with coins on a wooden deck with a plant growing out of it

Funding Opportunities

ICDS periodically awards seed grants for projects for which investigators plan to seek major external grants. ICDS typically solicits proposals in the fall semester.

If you have questions about the program, please refer to the ICDS Seed Grant FAQ. If your question has not been answered, please contact Guido Cervone, associate director of ICDS, at ics-seed-grants@ics.psu.edu.

To see what projects have been funded by ICDS in the past, view past seed grant recipients.

Eligibility

  • Projects can originate from tenured, tenure-track, or research faculty; however, either the PI or Co-PI must be a tenured or tenure-track faculty.
  • Recipients (either PI or Co-PI) of last year’s ICS Seed Grant are not eligible for funding under this solicitation.
  • Students and postdocs cannot serve as PIs or Co-PIs.

Awards

  • Up to $10,000 for awards to a single researcher
  • Up to $25,000 for awards that bring together two faculty members who engage in an interdisciplinary project
  • Up to $35,000 for awards whose PI is an ICDS Co-Hire or Associate faculty

Additional budgetary information and restrictions will be available when ICDS is accepting seed grant proposals.

Themes

  • Exascale Computing
  • Quantum Computing
  • High-Performance Computing and Numerical Simulations
  • Research on Cyberinfrastructure
  • AI/ML and Big Data Analytics
  • Data Assimilation
  • Data Visualization and Immersive Experience
  • Data Privacy and Law

FAQ

Q: May I utilize seed grant funds to finance an international collaborator?
A: You may not allocate salary for visitors, but you may allocate travel funds up to the maximum allowed under the solicitation.

Q: May I utilize seed grant funds for an ICDS-ACI allocation (for example, access to GPU nodes)?
A: Yes, you are permitted to use seed grant $$ on ICDS-ACI computing resources.

Q: I’m a visiting professor. May I apply as a PI or Co-PI?
A: Unfortunately visiting professors are not eligible to be PIs or CoPIs, but they may be named in the proposal.

Q: Is there a limit to the number of investigators for an application?
A: There is a limit to two investigators, with a strong preference that they are at different ranks, such as a more senior investigator serving as a mentor for a more junior one.

Q: What ICDS affiliation is required to be eligible for the maximum grant ($35,000)?
A: The PI must be an ICDS Associate or Co-Hire.

Q: I am a research faculty, and I wish to apply as a lone PI.  Can I do that?
A: Please contact Dr. Guido Cervone at ics-seed-grants@ics.psu.edu.

Q: We are a group of two research faculty and wish to apply together as a PI – CoPI team. Can we do that?
A: Please contact Dr. Guido Cervone at ics-seed-grants@ics.psu.edu.

Q: Are there formatting requirements for the proposal in addition to what is specified in InfoReady?
A: No.  The only restrictions include number of pages, font size, spacing and margins.

Q: Can we use funds to purchase data and services?
A: Yes, as long as the funds are used in line with all applicable policies of Penn State and/or the PI’s college/unit. Please check with your unit’s financial officer if you are unsure of how the funds can be spent. 

Q: What are the review criteria for seed grants?
A: The criteria are:

  • Fit with the topics of the RFP
  • Scientific merit
  • Probability of success
  • Project sustainability (external funds)
  • Team expertise and makeup
  • Budget realism

Past Seed Grants Funded

2020

  • 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)
  • Automated Design of Multi-Scale Structures: ADeMS (PI: Saurabh Basu, assistant professor of industrial and manufacturing engineering; Co-PI: Christopher McComb, assistant professor of engineering design and mechanical engineering)
  • A Novel Computational Framework for Large-scale Dam Structural Analysis (PI: Pinlei Chen, assistant professor of civil and environmental engineering)
  • Creation of a maternal-child Medicaid birth cohort to investigate risks of child welfare system involvement in low income households (PI: Christian Connell, associate professor of human development and family studies; Co-PI: Sarah Font, assistant professor of sociology)
  • 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)
  • Artificial Intelligence Method for Fast and Reliable Interpretation of DFIT and Flowback Data (PI: Arash Dahi Taleghani, associate professor of petroleum and natural gas engineering)
  • Using Network Science to Understand and Counteract Political Opinion Polarization (PI: Daniel DellaPosta, assistant professor of sociology and social data analytics)
  • Pennsylvania Stream Network Modernization:  Leveraging LiDAR data to delineate stream networks and inform the regulatory landscape (PI: Jon Duncan, assistant professor of hydrology; Co-PI: Lara Fowler, senior lecturer, Penn State Law)
  • Application of the causal inference methods in the analyses of obesity paradox: body mass index and mortality in 17,457 older adults (PI: Xiang Gao, associate professor of nutritional sciences; Co-PI: Prasenjit Mitra, professor of information sciences and technology)
  • Advancing Arctic Sea Ice Prediction Through the Application of Deep Learning (PI: Melissa Gervais, assistant professor of meteorology and atmospheric science and ICDS co-hire; Co-PI: Jian Sun, postdoctoral research scientist, Department of Geosciences
  • Exploring Machine Learning to Accelerate Quantum Computing (PI: Swaroop Ghosh, Joseph R. and Janice M. Monkowski Career Development Assistant Professor, School of Electrical Engineering and Computer Science)
  • 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)
  • Empirical evaluation of causal inference-based tests of algorithmic fairness (PI: Vasant Honavar, professor and Edward Frymoyer Chair of Information Sciences and Technology; Co-PIs: Sarah Rajtmajer, assistant professor of information sciences and technology; José Soto, associate professor of psychology and Sherwin Early Career Professor in the Rock Ethics Institute; and Daniel Susser, assistant professor of information sciences and technology)
  • Integrating High-Throughput Materials Simulations and Deep Machine Learning for Optimizing Microstructures of Advance Energy Storage Materials (PI: Sharon Huang, associate professor of information sciences and technology; Co-PI: Long-Qing Chen, Donald W. Hamer Professor of Materials Science and Engineering)
  • Computational Phenotyping: Creating a High Performance Computing Infrastructure (PI: John Liechty, professor of marketing and statistics)
  • Solving Surface Shallow Water Equations using Machine Learning Algorithms (PI: Xiaofeng Liu, associate professor of civil and environmental engineering and ICDS co-hire; Co-PI: John Harlim, professor of mathematics and meteorology and atmospheric science, and ICDS co-hire)
  • Towards Dynamic Patient-centric Personal Health Libraries (PI: Fenglong Ma, assistant professor of information sciences and technology and ICDS co-hire; Co-PI: Sharon Huang, associate professor of information sciences and technology
  • Supervised Machine Learning Analysis of Sound in Music Video Mashups (PI: Eduardo Navas, associate research professor, School of Visual Arts; Co-PI: Robert Fraleigh, assistant research professor, Applied Research Laboratory)
  • 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)
  • Identifying robust near-term renewable energy policies to ensure a healthy and climate-friendly future (PI: Wei Peng, assistant professor, School of International Affairs and Department of Civil and Environmental Engineering; Co-PIs: Klaus Keller: professor of geosciences; Vivek Srikrishnan: assistant research professor, Earth and Environmental Systems Institute)
  • Direct Numerical Simulations (DNS) of pollutant dynamics near human and indoor surfaces (PI: Donghyun Rim, assistant professor of architectural engineering; Co-PI: Yuan Xuan, assistant professor of mechanical engineering)
  • An Open-Source Machine Learning Tool for Predictive Modeling of Thermochemical Properties (PI: Shun-Li Shang, research professor of materials science and engineering; Co-PI: Allison Beese, associate professor of materials science and engineering and mechanical engineering)
  • Refining the pedagogical and learning impact of a machine-learning powered learning analytics dashboard (PI: Priya Sharma, associate professor of education; Co-PI: Mahir Akgun, assistant teaching professor of information sciences and technology)
  • Measuring and Modeling the Label Dynamics of Online Anti-Malware Engines (PI: Linhai Song, assistant professor of Information sciences and technology)
  • 2020 International Conference on Complex Adaptive Systems (PI: Satish Srinivasan, associate professor of information science, Penn State Great Valley)
  • Through the eyes of a child: Simulating the visual input to a developing mind (PI: James Wang, professor of information sciences and technology; Co-PI: Bradley Wyble, associate professor of psychology)
  • A Machine-Learning Method for Predicting Hotspots in Laser Powder Bed Fusion Additive Manufacturing (PI: Qian Wang, professor of mechanical engineering; Co-PI: Abdalla Nassar, head of Process Physics, Analytics, and Engineering Department (acting), Applied Research Laboratory)
  • 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)
  • Utilizing geometric deep learning to predict the rapid intensification of tropical cyclones (PI: Manzhu Yu, assistant professor of geography)
  • Machine learning of massive real-time environmental monitoring data from Penn State fiber-optic array for mitigating urban geohazards (PI: Tieyuan Zhu, assistant professor of geosciences; Co-PI: Chaopeng Shen, associate professor of civil and environmental engineering)

2019

  • Machine Learning and the Preservation of Cultural Heritage on Madagascar (PI: Kristina Douglass)
  • 2019 Complex Adaptive Systems Conference (PI: Nil Ergin)
  • Traffic signal control using reinforcement learning (PI: Vikash Varun Gayah)
  • BehAV: A computational framework for the automated analysis of human behavior and physiology from video (PI: Rick Gilmore)
  • Mathematics and Applications of Machine Learning (PI: John Harlim)
  • Leveraging AI for Game-Theoretic Models of Judicial Decision Making (PI: Ben Johnson)
  • Datafication of Human Behavior Through Immersive Technologies – xR / AI Analytics for Advancing the Human-Technology Frontier (PI: Alex Klippel)
  • Improving the Effectiveness of Team Peer Evaluations using Artificial Intelligence (PI: Abdullah Konak)
  • Using AI to Improve Youth Employment in Morocco and Beyond (PI: Dongwon Lee)
  • Improving Success Rate of Atrial Fibrillation Surgeries via Reinforcement Learning (PI: Eunhye Song)
  • AI for Identifying and Optimizing Interactions Between Transit Systems (PI: Elizabeth Traut)
  • Show Me or Tell Me: Robots that Learn Games from People (PI: Alan Wagner)
  • Numerical Modeling of Volcanic Flank Instability and Failure Forecasting using Machine Learning (PI: Christelle Wauthier)
  • Towards Accountable Decision-making in Cybersecurity via Explainable Machine Learning (PI: Xinyu Xing)
  • Deep Learning for CALPHAD Database Development and Uncertainty Quantification (PI: Jinchao Xu)
  • The Study and Simulation of the Mechanisms Driving Species Migration (PI: Katherine Zipp)

2018

  • Individual-level brain parcellation using an integrative multi-network clustering approach (PI: Xiao Liu)
  • Harvesting Data and Models for Water Forecasting (PI: Li Li)
  • Predicting Relapse Onset in Bipolar Disorder from Online Behavioral Data (PI: Saeed Abdullah)
  • 2018 Astroinformatics Summer School (PI: G. Jogesh Babu)
  • Theoretical Study of Novel Dielectric Nanocomposites (PI: Adrianus Van Duin)
  • Combined experimental and multi-scale simulation investigation on binder jetting (PI: Guhaprasanna Manogharan)
  • Predictive Personalized Public Health (P3H): A Novel Paradigm to Treat Infectious Disease (PI: Steven Schiff)
  • The Generalizability and Replicability of Twitter Data for Population Research (PI: Guangqing Chi)
  • Fair Crowds: User-Centered Algorithms for Equitable Distribution of Work (PI: Benjamin Hanrahan)
  • Learning and Modeling of Fracture Mechanisms of Carbon Fiber Reinforced Polymer Composites from Spatiotemporal Image Data (PI: Jingjing Li)
  • A computational approach to predicting well-being through environmental, social, and physical measurement (PI: Catherine Mello)
  • Countrywide Rodent Densities In A Snap (PI: Kurt Vandegrift)
  • Coupled Statistical and Dynamical Models to Project Changing Risk of Extreme Floods due to Climate Change and Urbanization (PI: Ben Shaby)
  • Deep Learning for Astronomical Image Processing (PI: Derek Fox)
  • SETI@PSU: Partnering with the $100 million Breakthrough Listen Initiative (PI: Jason Wright)
  • Deep Learning and Parallel Computing to Accelerate Large-scale Simulation Modeling of Spatiotemporal Cardiac Systems (PI: Hui Yang)
  • Distributed Visual Perception for Urban Autonomous Driving (PI: Zihan Zhou)
  • Theory of fractional viscoelastic wave propagation and its efficient solver for processing ‘Large-N’ seismic data (PI: Tieyuan Zhu)