Funding Opportunities

The Penn State Institute for Computational and Data Sciences (ICDS) Seed Grant Program fosters innovative data- and computation-enabled research. A major goal of the ICDS seed grants program is to support interdisciplinary research groups to develop competitive proposals for substantial external funding to tackle complex problems. For the purposes of this solicitation, the research team is expected to be interdisciplinary, comprising a diversity of researcher expertise.  We especially encourage proposals that bring together junior and senior faculty, or faculty from multiple academic units/colleges to lay the groundwork for ambitious interdisciplinary project.

View the RFP on InfoReady here.

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Overview

Key Dates

  • The deadline for receipt of applications is no later than 5:00 p.m. EST on January 23, 2022.
  • Awards will be announced in mid- to late-March 2022.
  • Funding will be available beginning in May 2022.

 Eligibility

Eligible Principal Investigators (PIs) and co-PIs are ICDS faculty co-hires, faculty associates, and faculty affiliates at any Penn State campus. While proposals will include multiple investigators, investigators may only participate in a single proposal. Recipients (either PI or Co-PI) are NOT eligible to submit proposals in the RFP following completion of an ICDS seed grant.

Thematic Priorities

Proposals that address one or more of the following ICDS strategic research themes will receive preferential consideration, although proposals on other topics in the computational and data sciences will receive consideration.

  • Scientific AI and Predictive Modeling: Novel AI methods and tools for accelerating science, e.g., for extracting, annotating, and assembling assertions from literature and databases; generating and prioritizing hypotheses; integrating data and models across modalities and scales; creating, constructing and refining predictive, mechanistic, and causal models; reconciling competing scientific arguments; designing and optimizing experiments; explaining data, hypotheses, models, predictions; supporting human-human, human-AI, and AI-AI collaboration.
  • Applied Scientific AI and Predictive Modeling: Transformative applications of AI, predictive modeling, and simulation with application to climate science, material sciences, biomedical and health sciences, life sciences, social and behavioral sciences, earth sciences, agricultural sciences, urban/smart city applications, physical sciences, weather and hydrological sciences, manufacturing and supply chains, and related applications.
  • AI-enabled Data and Computing Infrastructure: AI-enabled infrastructure for automated monitoring and optimization of the operation of data centers, scientific instruments, and end-to-end scientific workflows.
  • Human-Centered AI and Responsible Data Sciences: Innovative interdisciplinary approaches to fostering diversity, equity, and social justice in a world impacted by AI, including methods and tools for detection and correction of algorithmic bias, detection and prevention of data breaches and privacy violations; protection of the integrity of democratic processes; societal, legal, and ethical implications of AI and Data Science tools.

Proposal Guidelines

ICDS Seed Grant proposals must be submitted through InfoReady by completing all required fields and uploading a single proposal file (pdf).

The proposal must include the following:

  • Proposal Abstract (½ page maximum, 12pt Times Roman or 11pt Arial font, 1-inch margins, 1.5 line spacing).
  • Research Plan (4 ½ pages maximum, including figures, tables, and text, excluding references, 12pt Times Roman or 11pt Arial font, 1-inch margins, 1.5 line spacing).
  • References cited (additional 2 pages maximum, 12pt Times Roman or 11pt Arial font, 1-inch margins, 1.5 line spacing).
  • A Letter of Support from ICDS Client Engagement Team (if applicable – see below).
  • At least one Letter of Support from the PI’s department head, college dean (or designate), or institute director (or designate), including commitment of matching funds, and/or other support e.g., reduced teaching load, access to specialized core facilities, tuition, etc. (if any).
  • 2-page NSF or NIH CVs (prepared using https://www.nsf.gov/bfa/dias/policy/nsfapprovedformats/biosketch.pdf or NIH equivalent) for the PI and each Co-PI.
  • A document listing Current and Pending Support for the PI and each Co-PI (recommended format: https://www.nsf.gov/bfa/dias/policy/nsfapprovedformats/cps.pdf ).
  • Budget and budget justification, including availability of matching funds (if any) using the template provided.

Abstract

The proposal abstract should summarize the scientific aims, intellectual merit, and broader impacts of the project. The research plan should include: a brief background and significance, well-articulated research objectives, research approach (including description of data sets, methods to be developed or applied), anticipated outcomes/potential for impact, specific plan for how the seed grant will lead to competitive proposal for external funding (identifying existing or anticipated funding opportunities to be targeted, the role of the seed grant in the external proposal, and timeline).

Computational Resources

If applicable, proposals must document the computation, data, storage, software resources required for the project, and the availability of the resources. PIs seeking ICDS Research Innovations with Scientist and Engineers (RISE) resources will need to coordinate with ICDS Client Engagement (Derek Leydig, dml129@psu.edu and/or Chuck Pavloski, cfp102@psu.edu) to obtain a Letter of Support; RISE time will be included as an item in the budget. A Roar Resources Rate sheet is provided for reference.

Review Criteria

Each proposal will be reviewed based upon the following criteria:

  • Intellectual merit including relevance to the themes highlighted in the solicitation; rationale for the proposed interdisciplinary approach, and the expertise of investigators; creativity and innovation; significance of goals and anticipated results; soundness of research plan; and the likelihood of successful completion of the project.
  • Sustainability as evident from a credible and clearly articulated plan for leveraging this seed grant investment into exceptional scholarship and/or significant external funding.
  • Appropriateness of the requested budget (and where appropriate, matching funds) for supporting the proposed research.
  • The degree to which the project explores new, high risk, potentially high payoff interdisciplinary research collaborations.

Post Award Requirements

Once the seed grant has been awarded, ICDS requires the awarded faculty member to provide a set of reports to ICDS with the following timeline:

  • Interim Report: six months after the beginning of the project. This report will include a description of the progress made, progress towards submission for external funding, and a brief financial accounting. Non-performance may result in award suspension.
  • Project Outcomes Report: a report will be requested one year after the beginning of the project. The project outcomes report will include information on how many students (Ph.D., graduate, and undergraduate) were involved or supported in the research project, how many conference presentations and journal publications were enabled by the award, how many proposals have been or will be submitted as a result of the award, and where those proposals were/will be submitted. The report should report the award’s impacts suitable for uploading to the ICDS website. A template for the Project Outcomes Report will be provided.
  • Follow-up Reports: these will be due 1-year and 2-years after project completion. Follow-up Reports will include a list of publications, proposals submitted, and grants received, based upon the work done in the seed grant. Researchers should notify ICDS when they have relevant external funding success beyond 2 years.
  • Research Presentation: Present a research poster at an ICDS-sponsored event.
  • ICDS Seed Grant Review: Serve as an ICDS Seed Grant proposal reviewer in 2023-2024 (limit of 3 proposals).
  • External Grant Submissions: Submit at least one significant external-to-PSU research proposal within one year after completion of the Seed Grant. If a proposal is not submitted, an explanation for the lack of submission needs to be provided. Failure to do so will result in ineligibility for future ICDS funding.
  • Awardees will apply for a minimum of ICDS Affiliate status. Online application can be found here: https://www.icds.psu.edu/about/icds-affiliates-program/
  • Acknowledgement of Seed Grant Support: Acknowledgment of the seed grant in any publications resulting from the funded effort.

Full instructions and templates for preparing the Interim and Final Reports will be distributed to award recipients when the seed grant is awarded and closer to the report due dates.
Investigators should review the ICDS seed grant FAQ for answers to frequently asked questions. Any questions or requests for clarification may be directed to icds-seed-grants@lists.psu.edu.

Budget

ICDS Seed Grant research awards are intended to be for one year of funding. There are two classes of awards:

  • Awards with budgets up to $30,000 are typically targeted towards bringing together a new interdisciplinary team, developing proof of concept or early-stage methodologies.
  • Awards with budgets above $30,000 and up to $50,000 are expected to be for larger-scale research opportunities and are required to provide a clear pathway to a major external funding opportunity.

The budget may include:

  • ICDS Roar and/or RISE resources.
  • Travel support for PSU personnel or to cover PSU visits by external collaborators limited to $3,000 over the duration of the project.
  • Salary for postdocs or non-tenure track research faculty, limited to 25% of budgeted annual salary.
  • Graduate student stipend.
  • Undergraduate student stipend as appropriate.
  • Purchase of data and/or software, limited to $3,000 total over the duration of the project.

The budget cannot include:

  • Funds for computational resources beyond those administered by ICDS.
  • Tuition (researchers may seek departmental or college matching funds to cover tuition).
  • Tenured or tenure track faculty salary.

A second year of support may be available to teams demonstrating substantial progress and who provide a compelling plan for expanding the impact and long-term sustainability of the seed grant funded research, e.g., when the seed grant yields surprising findings, raises additional questions, or points to novel directions that must be explored prior to submitting a competitive proposal for consideration by an external agency, e.g., NSF, NIH, DOE, USDA, DOD, or a private foundation. To be considered for a second year of funding, the PI must notify ICDS prior to the seed grant deadline of the subsequent call. A short Phase 2 proposal must be submitted for consideration of these follow-on funds.

PIs are strongly encouraged to obtain matching funds from their department, college, or campus to supplement the ICDS seed grant and/or to cover project costs that are not covered through the seed grant, e.g., tuition for graduate students.  Proposers should contact their respective units directly to determine the availability of matching funds. Information on the source and amount of matching fund commitments shall be acknowledged in the letter(s) of support from the relevant units.

Past Award Recipients

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)