Call for DataX Research Proposals

Center for Statistics and Machine Learning

Call for DataX Research Proposals

 

With the support of a major gift from Eric Schmidt ’76,  his wife Wendy, and the Schmidt Futures Foundation, the Center for Statistics and Machine Learning (CSML) is pleased to announce the first call for proposals on DataX: Data Science at Princeton.

This call seeks to support innovative and collaborative research in data science and machine learning that accelerates scientific discovery across the University. Our objective is to fund transformative inter-disciplinary projects with high potential for subsequent impact on the research community. To achieve these ends, we invite proposals based on a diversity of intellectual approaches and personal backgrounds.  

Research proposals can be speculative (seed funds for new innovative research projects), or request support to help push existing projects to a new level. An illustrative list of across-discipline topics includes:

  • An exploration of machine learning and statistical methods for chemical reaction prediction. 
  • The development of a deep convolutional networks for protein secondary structure prediction.
  • Privacy and security management in the era of big-data and machine learning.
  • Large scale data-driven research in healthcare, neuroscience, psychology, materials science, etc. 
  • Novel data-driven approaches to natural language understanding and machine translation.
  • Improving the foundational understanding of machine learning to enhance its potential.
  • Physics based reinforcement learning for the control of novel robotic systems.
  • Machine learning systems for modelling human behavior and human interactions.
  • Machine learning with fair, explainable, and unbiased behavior.

The above list is intended to be descriptive, not prescriptive. Bold new ideas that involve an appropriate mix of data science, modern statistical analysis, machine learning, and computation are welcome.

PI Eligibility

Tenured and tenure-track Princeton faculty are invited to apply. Ideally proposals will be from teams of two or more PIs drawing on expertise from multiple disciplines. However single PIs with bold new ideas are welcome to apply. A faculty member can participate on at most two proposals.

Two Types of Proposals are Invited

Type 1): Interdisciplinary proposals seeking support for a well-formed research idea backed up by some preliminary results, but not quite ready for external funding.  Up to $125,000 of funding over one year. Expenditures may include graduate student stipend and tuition, post-doctoral salary and benefits, research-related travel by graduate students or post-docs, lab supplies and fees.

Type 2): New project seed proposals. (a) One year of support for a current graduate student: 75% academic year funding plus full summer funding at the appropriate department level. Students funded are expected to hold a 3 hour TA in a data science related course (in SML or other approved courses); or (b) 100% calendar year funding for a current or new post-doc. Supported post-docs are expected to spend 4 hours per week participating in DataX activities (e.g., helping with discussion groups, seminars, reading groups, workshops, or as preceptors when appropriate.)

Dates

Proposal submission deadline: July 15, 2019. Funding could start as early as September 1, 2019

Proposal Preparation and Submission Instructions

Proposals should be concise, up to two pages in length, with a separate detailed budget page and separate references (if needed). Clearly specify the research objectives, the novelty of the approach, and how the funds will be utilized. If relevant, letters of support from collaborating units or people can be included as an appendix.

Submit proposals online using the DataX website. Complete the form on the webpage and upload the proposal as a single pdf document.

Review Criteria

An ad hoc faculty committee will evaluate the proposals on the basis of quality, originality, and potential impact (on the campus and/or the field of study).

Reporting Requirements

For funded proposals, a final presentation and an outcomes slide will be due at the end of the award. There will be a wrap-up session for all awardees, where they will present the results of the award. The final outcomes slide will be used for reporting purposes. Additionally, awardees agree to assist in stewardship efforts with the donors, if requested, including sharing descriptions of the research supported and its impact. A story about the project and its progress may be used on the CSML website.

Submission of Proposals

Please submit your proposal through the DataX Proposal Submission Webform.

Contact

Contact DataX Project Manager, Ellen DiPippo (edipippo@princeton.edu), with questions about proposal preparation and submission.

About CSML

The Center for Statistics and Machine Learning collaborates on research and teaching that combines insights from computation, machine learning, statistics and the application of these ideas to specific application domains. To learn more, visit the CSML website at  https://csml.princeton.edu/