The Goergen Institute for Data Science (GIDS) is currently seeking proposals for the 2022-2023 GIDS seed funding program. Proposals must be submitted by 5 p.m. (ET) on Wednesday, June 15, 2022.
Award Amount: $20,000
Award Period: September 1, 2022 to August 30, 2023
The Goergen Institute for Data Science (GIDS) seed funding program aims to support collaborative research efforts toward attracting major external funding, with a particular focus on work aligned with at least one of the following research priorities in data science:
- Foundations of machine learning and artificial intelligence (AI)
- Imaging, optics, and computer/human vision
- Life sciences and biomedical data science
- Health analytics and digital health
- Human-data-system interfaces (including human-computer interaction, augmented and virtual reality (AR/VR), robotics)
- AI-augmented learning and work
PIs will be expected to identify which of these six areas their proposal is most closely aligned with, although exceptional proposals in other data science-related research areas will also be considered. PIs will also be expected to identify specific external funding opportunities they are planning to pursue based their seed grant.
Proposals will be evaluated based on their intellectual merit and on their potential to attract collaborative external funding in areas of interest for GIDS. Proposals will be reviewed by anonymous reviewers from the University community with relevant expertise.
The maximum budget for 2022-23 GIDS seed grants is $20,000. Grants will be awarded for one year from September 1, 2022 to August 30, 2023. Funds can be used for student/post-doc support, equipment, materials and supplies, software, publication costs, travel, and other justified expenses. Funds may not be used for faculty salaries.
Principal investigators (PIs) must be full-time faculty members at the University of Rochester. Each proposal must have a PI, and may include up to three co-PIs, and other senior personnel, as needed. The PI or at least one of the co-PIs must be a GIDS affiliated faculty member. There are no limits on the number of proposals per faculty member.
The following information must be submitted as part of the application. All documents should be in 11 point, Times New Roman font, single column.
- Project title, names of the PI, co-PIs, senior personnel, amount of funding requested, GIDS research area (this information will be entered into an online form).
- Project abstract (up to 400 words).
- Project description (3 pages maximum). The project description must clearly describe the intellectual merit of the proposed work. It should also indicate the specific external collaborative funding opportunities the project team is planning to pursue based on their seed grant. References can also be included as additional pages in the project description document.
- CVs/biosketches of the PI, co-PIS, and all senior personnel (NSF or NIH style is preferred but not required).
- Budget spreadsheet.
- Budget justification (1 page maximum).
For questions about the GIDS seed funding program, contact Mujdat Cetin (email@example.com). For questions about the submission process, contact Sylvia Tiballi (firstname.lastname@example.org).
Proposals must be submitted by 5 p.m. (ET) on Wednesday, June 15, 2022.
Physics-aware Learning-based Ultrasound Tumor Ablation Monitoring
Co-PIs: Ajay Anand, Mujdat Cetin, Diane Dalecki
The objective of this project is to develop physics-guided learning-based medical ultrasound imaging solutions for monitoring and guidance of tumor ablations. Tumor ablation—commonly performed by heating tissue to cytotoxic levels for a few minutes using RF, laser, and high intensity focused ultrasound (HIFU)— is an established minimally invasive technique for the treatment of tumors of the liver, kidney, prostate, bone, and lungs. The measurement of temperature (thermometry) during thermal therapy is an attractive means of mapping the region of thermal damage. While MRI imaging has been shown to be effective for noninvasive thermometry due to its superior accuracy, spatial and temporal resolution, it has several disadvantages: it is expensive, not portable, and not directly compatible with custom therapy systems to work within the strong magnetic field of the scanner. In contrast, image-guidance with ultrasound remains particularly attractive in simplicity, portability, accessibility, and cost. Ultrasound-based thermometry has been previously proposed as a means of therapy monitoring but existing methods suffer significant limitations impeding clinical usability. Existing methods of ultrasound thermometry are ineffective beyond 50°C due to multiple physical limitations: non-monotonic relationship between temperature and sound speed including plateauing around 60°C, tissue phase transitions and deformation, stochastic generation of cavitation bubbles, and tissue necrosis. Hence, an ultrasound-based technology that can successfully monitor treatment over the entire therapeutic temperature range is highly desirable clinically. Towards this end, the proposed research uses a hybrid learning-based approach to combine real-time ultrasound thermometry data measured at the periphery of the heating zone (which is thus in the favorable sub-ablative temperature range up to 50°C) with the underlying heat transfer process (implemented via the diffusion equation) to infer real-time temperature maps throughout the treatment zone. The work will also explore tradeoffs between approaches which incorporate more or less information from the physical models. Incorporating a learning-based approach in the inversion process offers significant advantages over relying solely on inline patient-specific finite element-based models which is cumbersome and computationally inefficient to use in the clinical setting.
The project brings together a multidisciplinary team of faculty with expertise in computational imaging, data science, biomedical engineering, and ultrasound technology to develop the learning-based therapy monitoring approach and evaluate it in ex-vivo experimental settings. Successful technical feasibility, facilitated by this seed funding, holds promise for translational research collaborations with URMC clinicians and leading biomedical research groups around the country to pursue long-term external federal grant funding opportunities in image-guided surgery.
Automatic Rendering of Augmented Effects in Immersive Concerts
Co-PIs: Zhiyao Duan, Matthew Brown, Raffaella Borasi
The project will develop a computational system to automate the coordination between music and other multimedia during a live performance to provide audiences with a more immersive experience. As such, the project will not only address a specific need identified by a current NSF-funded Future of Work planning grant, but more importantly serve as a pilot study to provide “proof of concept” for work at the core of a planned $2.5M Future of Work research grant proposal in March 2022.
Our preliminary exploration of the “pain points” experienced by artist-technologists (i.e., individuals working at the intersection of arts and technology) revealed the need for new and more user-friendly technical tools to enable musicians to better leverage advanced technology. This is especially true for immersive concerts, where the music listening is augmented by other multimedia such as text, lighting, animations, smoke and sound effects, using AR/VR technologies, to provide the audience with a more impactful and engaging experience. The key to creating successful immersive experiences is to coordinate the music precisely with other multimedia during a live performance, yet currently there are no good solutions to make this happen smoothly.
The proposed system will follow musical scores in real time and automatically trigger multimedia events that have been annotated on the musical score, thus replacing or greatly reducing the workload of conductors and operators and making immersive events easier to schedule and present. Developing this new system will require adapting score following algorithms to immersive concerts so they can cope with a wide range of music ensembles, background noises, as well as acoustic echo of augmented sound events - which in turn will improve the robustness of state-of-the-art score following algorithms. For the rendering of immersive events, the proposed system will connect the score following algorithm with a widely used multimedia content management software named QLab through the Open Sound Control (OSC) protocol.
The new system will build on preliminary work by PI Duan, who already developed algorithms for score following and employed such algorithms in automatic lyric display applications for choral concerts. We will develop and test the system with TableTopOpera, a chamber ensemble from the Eastman School of Music that specializes in multimedia projects. The project will include a rigorous evaluation of these implementations, including interviews with TableTopOpera musicians to derive broader implications for musicians and technicians working to produce immersive music experiences.
Artificial Intelligence for effective communication on health effects of electronic cigarettes through Instagram
Co-PIs: Dongmei Li, Chenliang Xu
Social media platforms such as Twitter, Instagram, and YouTube are trendy in the United States, especially among youth and young adults. Previous studies have found that social media platforms are widely used to promote electronic cigarette (e-cigarette) products by vape shops and companies. However, they are under-used by public health authorities for educating the community about the health risks of e-cigarette use (vaping). Social media marketing of e-cigarette as healthier alternatives to conventional cigarettes resulted in youths' common perception that vaping is a harmless activity. The National Youth Tobacco Survey showed that e-cigarette use among high school students has skyrocketed from 12% in 2017 to 28% in 2019. Thus, it is urgent to communicate effectively with the public about the risks of e-cigarette use.
The purpose of this project is to identify potentially effective ways of communicating with the public about the health risks of electronic cigarette use on the most popular social media platform in youth, i.e., Instagram. The key to our approach is applying cutting-edge artificial intelligence and statistical learning techniques to ease the epidemic of e-cigarette use. To achieve our objectives, we will characterize essential features of Instagram images educating/warning of e-cigarette use risks associated with high social media engagement through advanced deep-learning algorithms and statistical learning methods. Instagram images have been widely used by the vaping industry and vaping proponents to attract Instagram users and promote the sale of e-cigarettes. Using deep learning techniques (such as convolutional neural networks), we will identify the most crucial image features associated with high user engagement (number of likes) to educate/warn the public about the health risks associated with vaping. Such information can guide us to design useful images that convey the health risks of e-cigarette use to the public. This project will provide much-needed information to the Center for Tobacco Products (CTP) on understanding how to effectively communicate with the public regarding the potential health effects of e-cigarette use through Instagram. Moreover, it will guide future campaigns of CTP to address the current epidemic of vaping, particularly among youth and young adults, to protect public health.
Designing Effective Intervention to Promote Green Products in Online Shopping Platforms
PI: Ehsan Hoque
Although the increasingly devastating effects of climate change have drawn global attention, it is still difficult to motivate people to take action against climate change. In fact, around two-thirds of global greenhouse gas emissions can be attributed to household consumption . Despite individuals being concerned about the environment and willing to opt for greener consumption, these intentions are often not translated into appropriate actions due to a lack of incentive for going green . High price and difficulty of identifying green products, not having enough time for research, lack of environmental information in the product description, and lack of trust in the eco-friendliness labels provided by the manufacturers have been identified as the major barriers for the gap between consumer attitude and their behavior . However, hardly any solutions have been proposed by current literature on how to overcome these barriers.
Online shopping has been gaining massive popularity - in 2020, the worldwide e-retail sale was more than 4.2 trillion USD with over two billion e-commerce customers . We identify that e-commerce platforms can play a significant role in tackling climate change. We propose a redesign of existing online shopping platforms by introducing the addition of eco-friendliness ratings (how eco-friendly a product is), environmental impact summary, and highlighting eco-indicator keywords indicative of environmental impact. The eco-friendliness rating of a product would enable users to identify greener products quickly and conveniently as climate-conscious consumers can sort relevant products based on their eco-friendliness. The environmental impact summary briefly explains the impact of a product on the environment, and eco-indicator keywords are the words/phrases in a product description that can be related to the environmental impact of the product. These explanations and highlights can justify the eco-friendliness rating provided, and thus increase consumer trust. In addition, they can work as a continuous reminder of how one's buying choices can make a difference towards a greener earth.
Our hypothesis is that the proposed components, if introduced in existing e-commerce platforms, will significantly reduce the “attitude-action gap”, by addressing many of the major barriers identified including consumer’s inconvenience, lack of knowledge, and lack of trust. Since billions of people shop online, motivating even a smaller percentage of the consumers can make a massive contribution towards tackling climate change. We aim to design a prototype of the proposed e-commerce platform and run a quasi-randomized case-control study to investigate whether the prototype can significantly influence individual consumption behavior.
Interactive Climate Network Exploration over Real-Time Data
Co-PIs: Fatemeh Nargesian, Gourab Ghoshal
To identify and analyze patterns in global climate, scientists and climate risk analysts model climate
data as complex networks (networks with non-trivial topological properties). The climate network
architecture represents the global climate system by a set of anomaly time-series (departure from the
usual behavior) of gridded climate data and their interactions. Several studies have applied network
science on climate data assuming dynamic networks. To study the stability of a network over time,
scientists compare the similarity between networks in different years with the patterns of daily data.
For example, it is found that networks constructed from temperature measurements on different sites
in the world are changed dramatically during El-Nino events in a similar way.
Intellectual Merit: The common way for network dynamics analysis is to construct networks for each
hypothesized time window and analyze them separately, which is a laborious task for data exploration
and becomes impractical on real-time data. To bridge the gap between climate data and network
analysis, we propose to build a data platform that enables climate scientists and decision-makers to
select/filter data and efficiently and interactively construct and process climate networks on historical
and real-time data. The key requirements of this platform are: performance (low latency and
overhead), data and analytics integration (data access is seamlessly integrated into network analysis
algorithms), and query workload (supporting necessary data serving building blocks for climate
network analysis). The main objectives included: 1) real-time network construction on various
time-windows and resolutions to meet selection and visualization needs of users, 2) change detection
in the topology of a network as the underlying time-series change, and 3) real-time clustering of nodes
and community detection in a network at user-specified time-windows.
Expected outcomes of this project include a suite of algorithms for efficiently sketching and analyzing
massive and frequently updated time series to enable climate network analytics (particularly real-time
network construction, clustering, and community detection); the software library of a light-weight data
layer on top of existing open-source streaming engines (such as Trill) that bridges the storage and
analytics layers by implementing the building blocks of climate data processing (sketching and
correlation calculation on uni- and multi-variant time series); a climate network analytics dashboard for
visualization and analysis of real-time data.