Congratulations to our 2022 first round seed grant winners!
The following projects were funded in our first round call for seed funding.
Machine Learning for Computational LiDAR Imaging in Earth Science
PI: Gonzalo Arce (Department of Electrical and Computer Engineering):
Co-PI: Rodrigo Vargas (Department of Plant and Soil Sciences)
Project Abstract: The Earth is continuously changing due to both natural processes and the impacts of humans on their environment. Observing and understanding our planet in order to predict future behavior as reliably as possible is of great importance today and for the foreseeable future. It will help manage resources, mitigate threats from natural and human-induced environmental change, and to capitalize on social, economic, security opportunities. Among the many tools available in remote sensing, light detection and ranging (LiDAR) imaging is an unmatched technology that provides 3D sensing for Earth science.
This research aims to advance and innovate LiDAR satellite sensing as well as translate these advances towards beneficial societal outcomes. This collaborative work engages scientists & engineers across the Colleges of Engineering and Agriculture and Natural Resources
Data-driven Explanations for Knowledge Discovery in Seabed Morphodynamics Analysis
PI: Xi Peng (Department of Computer and Information Sciences)
Co-PI: Arthur Trembanis (Department of Marine Science and Policy)
Project Abstract: Many naval applications require the ability to consistently and reliably solve large-scale machine learning (ML) problems on local, regional, and global scales. Examples include seabed morphodynamics and acoustic imagery analysis. Such applications often collect large-scale geo-distributed data at unprecedented resolution. Making use of this data to gain scientific insights into complex phenomena requires characterizing dynamics and uncertainty among a large number of variables. Characterizing by large non-stationary distributions, such data poses an unprecedented out-of-distribution (OOD) challenge where ML models often operate on unseen distributions due to diverse noise sources, unknown uncertainty levels, and long-range spatiotemporal dependence.
This research departs from conventional spatiotemporal – or time and space – modeling to develop a first-of-its-kind OOD-resilient methods for explainable seabed morphodynamics analysis. The OOD-resilient data-driven explanations will enforce scientific consistency and plausibility to deepen understanding or reveal discoveries that were not known before.
Radical Fashion: Digital Historical Reconstruction of 1920’s Garments for Virtual Exhibition
PI: Kelly Cobb (Department of Fashion and Apparel Studies)
Co-PI: Dilia Lopez-Gydosh (Department of Fashion and Apparel Studies)
Co-PI: Belinda Orzada (Department of Fashion and Apparel Studies)
Project Abstract: Museum exhibits transform scholarship from private act to a public experience because they reach a wider audience than most journal articles and referred presentations. The Department of Fashion and Apparel Studies is developing a historic fashion exhibition “The Twenties”, where the researchers will develop a digital, historical reconstruction of 1920’s garments housed in a virtual gallery. The virtual gallery will feature selected digital garments that are too fragile to be displayed on mannequins.
The research team will build digital assets including digital textiles and texture maps, embellishments applying TEXTURA – a cloud native AI technology by SEDDI, who is an industry partner of the team. Digitally reconstructing fragile garments allow viewers to experience the drape, silhouette and textile details on the body and in movement – something that garments exhibited on a mannequin or laid flat are unable to communicate.
Transformers for Socioemotional Behavior Assessment of Children with Autism
PI: Leila Barmaki (Department of Computer and Information Sciences)
Co-PI: Anjana Bhat (Department of Physical Therapy)
Co-PI: Kenneth Barner (Department of Electrical and Computer Engineering)
Project Abstract: The number of children diagnosed with Autism Spectrum Disorder (ASD) has increased in the past few years. Children with ASD have significant social-cognitive impairments in imitation, motor planning, emotional connections, as well as primary motor impairments in posture, balance, gait and coordination.
This research will use AI and deep learning methods for further advancement of the understanding of socioemotional behaviors in children with ASD. The aim is to eventually integrate existing models into a data collection pipeline to collect and analyze socioemotional behaviors of the children almost in real-time.
Predicting After-effects of ExoSkeleton-assisted Gait Training to Inform Human-in-the-loop Control Optimization
PI: Fabrizio Sergi (Department of Biomedical and Mechanical Engineering)
Co-PI: Austin Brockmeier (Department of Electrical and Computer Engineering)
Project Abstract: Robot-assisted rehabilitation is a promising intervention for supporting recovery after neuromotor injuries. In this context, robotic devices such as wearable powered exoskeletons are used to physically interact with the participants during their movements. Training is usually provided to target changes in neuromotor coordination that will translate into improvement in walking function measurable after completion of intervention. Currently, there is a limited basis to form a prediction of how effects measurable during training will translate into changes in coordination after training.
The aim is to identify the relationship between features of propulsion mechanics measured during training and the features of propulsion mechanics after training. Being able to predict the after-effect will enable real-time adjustment of the exoskeleton control to achieve desired outcomes.