We are delighted to announce the CITRIS Seed Funding Program awards for 2016. This year, 54 highly competitive teams from the four CITRIS campuses at UC Berkeley, Davis, Merced, and Santa Cruz submitted proposals for collaborative research projects within the University of California system.
Ten teams will receive a one-time award, averaging $57,000 each, for interdisciplinary work that can lead to larger research programs and extramural grant proposals. The winning proposals incorporate sensors, drones, and data analytics to advance cultural heritage preservation, online learning, and other timely applications in healthcare, energy, and agriculture. Of the successful proposals, 70% of lead investigators are receiving their first CITRIS Seed Grant and 40% of the projects include pre-tenured faculty members.
CITRIS has awarded annual seed grants to spur multi-campus, multidisciplinary collaborations among four University of California campuses since 2007. Numerous companies and centers have resulted from the program, including Cellscope, an imaging device that transforms ordinary cell phones into otoscopes for ear exams in the field; and the Center for Autonomous and Interactive Systems at UC Merced, which focuses on robotics, virtual characters, cognitive science, and human-computer interfaces.
Congratulations to faculty leaders of the 2016 CITRIS Seed Funding awards below.
Analyzing large corpora of code submissions to generate actionable hints for code correctness and style
We use machine learning and software analysis to generate immediate, customized, actionable feedback on the correctness and quality of students’ computer code with minimal instructor intervention, by using structural similarities between different students’ code submissions as the basis of targeted hints to help the students improve their code.
Avoiding Unnecessary Cesarean Section Deliveries: Informing the Decision via Transabdominal Fetal Oximetry
The project aims to develop a non-invasive trans-abdominal fetal brain oxygenation measurement system. The system will help obstetricians to easily distinguish between normal and critical drops in fetal heart rate during labor contractions, and thus, will enable patients and clinicians to avoid unnecessary Cesarean section surgeries.
A Biosensor for Early Detection of Increased Risk of Necrotizing Enterocolitis in Premature Infants
Principal investigators: Mark Underwood (UC Davis Medical Center) and Andre Knoesen and David Mills (UC Davis)
We propose development and testing of a multivariate biosensor pod to measure expelled gases from premature infants and to correlate these gases with the fecal microbiota. The ultimate goal is an early detection system for intestinal dysbiosis which precedes a common and devastating disease of premature infants: necrotizing enterocolitis.
Bodie Digital Community: Connect with your Past
This project is an augmented reality application for mobile devices created in collaboration with California State Parks. This app promotes public engagement in heritage preservation, fosters connection among visitors, and generates useful data that improve the management and preservation of California natural and cultural resources.
Enabling robots to express emotions based on human demonstrations
The goal of this project is for robots to leverage “body language” to express their state of awareness, hesitation, excitement, disappointment, etc. The project is a collaboration between roboticists, computer graphics experts and professional dancers. The team will develop and test methods for transferring motion capture data of a human dancer expressing emotions to a robot arm in a manner that preserves the emotional content of the motion. Experiments will use the PR2 and Kinova arm to evaluate these methods with human subjects via the Amazon Mechanical Turk platform.
Low-Cost Carbon Uptake Remote Sensing System and Training Module
Principal investigators: Kurt Kornbluth (UC Davis) and Sue Carter (UC Santa Cruz)
This project is a collaboration between the UCSC S-lab and UC Davis D-lab to build a low-cost, web-enabled remote sensor system and training module—based on the arduino platform—that locally monitors carbon uptake by plants. This system can be widely deployed by students from around the world to monitor carbon dioxide and water uptake by plants in their communities in order to facilitate sound land management practices that reduce the negative impacts of environmental and climate change.
Putting the Feedback Cycle in High Gear: Community-sourced, Data-driven Approaches for Sustainable Transportation Infrastructure
Stymied by the complex and opaque process involved in improvements to physical infrastructure? Tired of multiple trips to City Council? Distrustful of assumptions behind consultant projections? We will improve the planning process by collecting ongoing data using smartphones, and allowing cities to prototype changes and measure their impact before finalizing.
Robotic Exoskeleton For The Stabilization Of Tremors (RST) In the Hand and Wrist
Principal investigators: Lin Zhang (UC Davis) and Gabriel Hugh Elkaim (UC Santa Cruz)
Current treatment options fail to adequately support the millions of Americans living with Parkinson’s Disease or Essential Tremor. This collaboration between the UC Davis School of Medicine and UC Santa Cruz College of Engineering proposes a novel solution that achieves hand, wrist, and arm stabilization through a non-intrusive robotic exoskeleton.
Smart Infrastructure in Affordable Housing
Principal investigators: Ronnie Lipschutz (UC Santa Cruz), Elliott Campbell (UC Merced), and Chuck Mornard (Cabrillo College, Santa Cruz County)
UC Merced and Cabrillo College (Santa Cruz County) are developing smart “Tiny Row Houses” (TRHs) that can address the need for affordable housing in Northern California urban areas and Central Valley rural areas. These structures will be equipped with integrated smart systems to minimize resource inputs and operational consumption.
Under-Canopy Robots for Biofuel Plant Phenotyping Research
This project is developing a miniature high-throughput phenotyping robot to help plant breeders automate the measurement of plant architecture they require to accelerate the creation of more efficient energy crops that are better adapted to climate change and can be produced on marginal land without displacing food crops.