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Forecast Amplifier-Triple Your Weather Ensemble Quickly and Affordably
TS-071915 — An ultra‑efficient probit‑space ensemble expansion approach triples the size of weather‑forecast ensembles by producing hundreds of realistic “virtual” members from existing runs using minimal compute time and expert statistical knowledge.
Modern weather models are extremely complex, and each forecast can only be simulated a limited number of times. This means forecasters often have just 50–100 versions of a prediction to work with, even though the atmosphere itself is vastly more complicated. With so few samples, important de…
  • College: College of Arts & Sciences
  • Inventors: Chan, Man-Yau "Joseph"
  • Licensing Officer: Panic, Ana

HyperXtract: Rapid, Memory-Efficient Data Extraction for Nanopore Data
TS-070331 — Accelerate discovery and streamline workflows with a next-generation platform for high-throughput nanopore data extraction.
The Need Nanopore sensing is revolutionizing genomics, proteomics, and diagnostics, but the explosive growth in data volume has outpaced the capabilities of current analysis tools. Researchers often face bottlenecks due to slow processing speeds, memory limitations, and the need for specialized har…
  • College: College of Arts & Sciences
  • Inventors: Bandara, Nuwan
  • Licensing Officer: Panic, Ana

Unlocking Hidden Opportunities: The Power of Multi-Solution Spatial Aggregation
TS-067434 — The Need Spatial aggregation is crucial in numerous industries where data from low-level spatial units, such as census blocks, must be grouped into larger, meaningful regions. Traditional approaches often struggle with the computational complexity of these tasks and tend to focus on finding a singl…
  • College: College of Arts & Sciences
  • Inventors: Xiao, Ningchuan
  • Licensing Officer: Dahlman, Jason "Jay"

Reservoir Computing: Revolutionizing Rapid Processing
TS-065446 — The Need: In today's fast-paced commercial landscape, there's an increasing demand for rapid processing of complex data sets. Traditional computing methods often struggle to keep pace with real-time requirements, leading to inefficiencies and missed opportunities. Addressing this need for sw…
  • College: College of Arts & Sciences
  • Inventors: Canaday, Daniel; Gauthier, Daniel; Griffith, Aaron
  • Licensing Officer: Dahlman, Jason "Jay"

Secure Your Digital Fortress: Revolutionizing Cybersecurity with Next-Gen PUF Technology
TS-065412 — The Need: In contemporary cybersecurity landscapes, the conventional methods of employing Physically Unclonable Functions (PUFs) necessitate maintaining a secure challenge-response database. However, this practice poses significant security risks as access to this database could lead to the comprom…
  • College: College of Arts & Sciences
  • Inventors: Gauthier, Daniel
  • Licensing Officer: Dahlman, Jason "Jay"

Visual Function Mapping Technology: Enhancing Vision Care and Diagnostics
TS-062335 — The Need: In the field of ophthalmology and vision care, there is a critical need for accurate and comprehensive measurements of visual function over the entire visual field. Conditions such as glaucoma, age-related macular degeneration (AMD), diabetes, stroke, pituitary disease, brain tumors, and n…
  • College: College of Arts & Sciences
  • Inventors: Lu, Zhong-Lin; Lesmes, Luis; Xu, Pengjing "PJ"; Yu, Deyue
  • Licensing Officer: Dahlman, Jason "Jay"

Graduate Education Management
TS-062332 — The Need: In the fast-paced and ever-evolving landscape of graduate education, universities and academic institutions face mounting challenges in efficiently managing their graduate students' academic journeys. With diverse departmental requirements, it becomes crucial to have a centralized solu…
  • College: College of Arts & Sciences
  • Inventors: Hardesty, Michael; Box-Steffensmeier, Janet; Carl, Tammy; Decot, Kyle; Freeman, Elizabeth; Griffin, Kathleen; Hurst, Nicholas "Nick"; Kerler, Thomas; Leaflight, Ren; Malone, Kelly "Kelly Renee Hopkins"; Papke, Julia; Plas, Rebecca "Becky"; Schweinfurth, Staci; Smith, Timothy; Van Dyke, Lisa
  • Licensing Officer: Panic, Ana

Finding rare events by knowing where NOT to look
TS-051417 — Traditional regression analysis has been a staple in predicting cause-effect relationships. Counltess industries use these methods to predict rare events, from economic issues to cancer research, causality is often a desired result. Given the vast amount of potential variables it sometimes becomes…
  • College: College of Arts & Sciences
  • Inventors: Melamed, David; Schoon, Eric
  • Licensing Officer: Hampton, Andrew

Machine-Learning Algorithm for Improved Speech Intelligibility in Noise
TS-042266 — A monaural machine-learning algorithm for classifying time-frequency units in an unknown signal, which results in marked speech-intelligibly improvements in noisy signals.
A primary complaint of hearing-impaired (HI) listeners is poor speech recognition in background noise. This issue can be quite debilitating and persists despite considerable efforts to improve hearing technology. Despite considerable effort, monaural (single-microphone) algorithms capable of incre…
  • College: College of Arts & Sciences
  • Inventors: Healy, Eric; Vasko, Jordan
  • Licensing Officer: Dahlman, Jason "Jay"

Design and apparatus for quantification of the mapping of the sensory areas of the brain
TS-039164 — A new tool that can quantify the sensory maps of the human brain. The tool can be used to quantify plasticity and pathology in the sensory areas of the human brain that are associated with normal and abnormal development, aging, and diseases in sensory systems. The first application of the tool, on retinotopic maps of the visual cortex, has generated excellent results.
By analyzing the stimulus­-referred fMRI responses in each voxel of an MR image, retinotopic mapping of human visual cortex can generate cortical maps of the visual space. These maps elucidate the spatial organization of the neuronal responses to visual images. Although numerous studies have b…
  • College: College of Arts & Sciences
  • Inventors: Wang, Yalin; Lu, Zhong-Lin
  • Licensing Officer: Dahlman, Jason "Jay"

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