IEE Research Seed IGSB Software Funding Programs
UC Santa Barbara's Institute for Energy Efficiency is an interdisciplinary research institute committed to improving energy efficiency across three key “IEE 2.0” themes:
- Smart Societal Infrastructure encompassing the Smart Grid and everything that attaches to it like cities, buildings, homes, vehicles and lighting, and everything that makes the Smart Grid work like control systems, a hierarchy of energy storage solutions, and market mechanisms.
- Computing and Communications (C&C) including “Green and carbon neutral AI” for both training and inference, Carbon-neutral C&C from the cloud through the edge, and Power Use Effectiveness (PUE) methods and improvement for IoT and across the computing landscape.
- The Food-Energy-Water Nexus focusing on achieving energy-water resilience such as “Quadrupling the crop per drop”.
UCSB faculty focusing on areas like electronics and materials, computer science including AI/machine learning, policy and assessment, and in emerging fields like Quantum science will support and drive the interdisciplinary themes of the Institute.
Since 2019, IEE has provided research seed grants to faculty across the UCSB campus. This highly successful program has so far awarded 10 research seed grants (see below for details). Researchers are encouraged to apply for the 2022-23 IEE research seed grant program for amounts up to $50K. As in previous years, a proposal and review process will take place this summer with proposals due no later than Monday, August 1st. Multiple seed grants will be awarded. The anticipated award start date is October 1st.
In addition, we are pleased to announce a new IEE grant program funded by a generous donation from The Investment Group of Santa Barbara (IGSB) focusing on software for energy efficiency, including AI/machine learning. Researchers are encouraged to submit a proposal for 1 year funding in the range of $50K to $100K. Funding for subsequent years may be possible depending on progress during the first year. Applications are due no later than Monday, August 1st. Multiple grants will be awarded. The anticipated award start date is October 1st.
Eligibility
Ladder faculty members of all ranks are eligible to submit proposals provided the research focuses on an energy efficiency related topic. Graduate students and postdocs may apply but require the support of a faculty member in their area.
Application Process
Prior to submitting an IEE Research Seed or IEE IGSB Software Impact Grant, researchers are welcome to obtain feedback on the project idea from Mark Abel (Markabel@iee.ucsb.edu) or John Bowers (Bowers@iee.ucsb.edu). Applications should be submitted online prior to the deadline and include the following components:
- A Three-Page Project Summary, including goals, impacts and timeline
- One-Page Budget (please contact Bailey Clincy at brclincy@iee.ucsb.edu for assistance)
- Two-Page CV for the Lead PI and any Co-PIs.
Budget
The maximum allowable request for funding for the IEE Research Seed Grant is $50,000 and the maximum allowable request for an IGSB software impact grant is $100,000.
Allowable expenses
- Research assistance: UCSB graduate or undergraduate students; postdocs
- Research expenses, supplies, & services
- Travel: for the purpose of research, collaboration, or meeting with program directors at potential funding agencies
Review Criteria for IEE Research Seed Grants
We particularly encourage the submission of project proposals within our themes where IEE seed funding would make a big difference, i.e., potentially allow the recipient(s) to try something novel and innovative that might be in the early stages of development. We are also looking for projects that could lead to extramural funding through IEE.
Review Criteria for IGSB Software Impact Grants
We are looking to support high impact projects in software for energy efficiency and related areas where this funding level would provide a significant advance or reduction in risk. Ideas that could lead to commercialization and positive impact on society are encouraged.
Applying for one or both programs
You may designate an application for one or both programs. The same process will manage and review the grant applications for both programs. Please note that the criteria for selection are different for the two programs so please understand the criteria as you prepare your submission(s) and decide where to designate your submission. The IEE Research Seed program is more focused on driving forward new energy efficiency innovation across a broad research landscape and in allowing faculty to try something that will lead to extramural funding while the IGSB software impact grant is looking to support high impact projects in software for energy efficiency and related areas where this funding level would provide a significant advance or reduction in risk and could lead in a reasonable timeframe to commercialization and positive impact on society.
Reporting Requirements
A final report that summarizes the results of the project and how the Seed Funding moved this project forward. Reports due 30 days after the award end date.
Previous Seed Funding Recipients
-
2021-2022
Microarchitectural Checkpointing for Energy Efficiency Probabilistic Computing for a Green and Energy-Efficient Future
Kerem Camsari, Luke TheogarajanLiquid-Metal Microfluidic Portable Energy Transducer (LIMMPET)
Sumita Pannathur, Carl Meinhart, David Weld -
2020-2021
“Algorithm/ Hardware Co-Design of Energy Efficient Quantum Generative Learning with Integrated Photonics"
Galan Moody, Zheng Zhang“Distributed and Safe Real-Time Control Mechanisms for Community Energy Management”
Mahnoosh Alizadeh, Ramtin Pedarsani“Passive Water Harvesting Driven by Solar Energy and Radiative Cooling"
Yangying Zhu -
2019-2020
"Sensing and Lighting for Citrus Unders Protective Screens"
Chandra Krintz, Steven DenBaars, Rich Wolski"Synthesis and Advanced Magnetic Resonance Characterization of New Photoactive Materials"
Gabriel Menard, Raphaéle Clément"Energy-Efficient Mixed-Signal Neuro-Optimization Hardware"
Dimitri StrukovMaking AI More Energy Efficient
William Wang, Xifeng Yan