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Skill Gap Analytics and Workforce Training Recommendation Platform
TS-075512 — Organizations invest substantial resources in employee training and professional development, yet many struggle to demonstrate a clear connection between training expenditures, workforce performance, and career advancement. Human resources teams often lack effective tools to identify specific skil…
  • College: Fisher College of Business
  • Inventors: Goffe, Gretchen
  • Licensing Officer: Zinn, Ryan

Automated Cross-Document Table Consolidation and Visualization Platform
TS-075489 — Organizations across healthcare, government, finance, market intelligence, and regulatory sectors routinely generate large collections of reports containing valuable tabular data. While these reports often follow consistent formats, extracting, comparing, and analyzing information across multiple …
  • College: College of Engineering (COE)
  • Inventors: Bajaj, Goonmeet Kaur; Parthasarathy, Srinivasan
  • Licensing Officer: Giles, David

Ontology-Based Fault Propagation Analysis for Safety-Critical Systems
TS-075450 — Safety-critical systems in industries such as energy, aerospace, industrial automation, and advanced manufacturing are becoming increasingly complex, integrating hardware, software, controls, sensors, and communication networks. Existing fault analysis methods are often domain-specific and require…
  • College: College of Engineering (COE)
  • Inventors: Diao, Xiaoxu; Smidts, Carol
  • Licensing Officer: Giles, David

Virtual Myelography: a machine learning method for differentiating cerebrospinal fluid from spinal cord tissue in lumbar spine CT exam
TS-075230 — Problem Computed tomography (CT) is commonly used for its widespread availability, rapid acquisition times, and depiction of fine bony detail, but it struggles to differentiate spinal cord tissue from cerebrospinal fluid (CSF) due to their similar mass densities. Magnetic resonance imaging (MRI) of…
  • College: Office of Health Sciences
  • Inventors: Nguyen, Xuan; Dikici, Engin; Prevedello, Luciano
  • Licensing Officer: Hampton, Andrew

System and method for prediction of artificial intelligence model generalizability for unseen data
TS-075229 — Problem Artificial intelligence models often perform well during development but show unpredictable drops in accuracy and reliability when deployed on data that differ from their training sets. In high‑risk settings such as clinical care, these shifts can arise from changes in hardware, protocols…
  • College: College of Medicine (COM)
  • Inventors: Dikici, Engin; Nguyen, Xuan; Prevedello, Luciano
  • Licensing Officer: Hampton, Andrew

Powering Next-Generation Cryptography with Nanopore True Random Number Generation
TS-075198 — Enabling stronger, more reliable randomness for modern cybersecurity.
The Need As digital infrastructure scales and cyber threats become more sophisticated, the integrity of cryptographic systems increasingly depends on the quality of their randomness. From encryption keys to authentication protocols, weak or predictable entropy can undermine even the most advanced s…
  • College: College of Arts and Sciences (COAAS)
  • Inventors: Bandara, Nuwan; Amarasekara, Dhanush; Gussenhoven, Katherine
  • Licensing Officer: Panic, Ana

TL-iGeneCombo: Transfer Learning for Individual Sample Specific Gene Combination Effect Prediction and Selection in Cancer Cells
TS-075184 — Problem Synthetic lethality (SL) is defined as a gene combination effect (GCE) of two genes where loss-of-function of both genes, rather than one, causes cell death. While carrying strong therapeutic potential in the treatment of cancer, SL interactions are context specific and sparse, thereby limi…
  • College: College of Medicine (COM)
  • Inventors: Li, Lang; Gökbağ, Birkan
  • Licensing Officer: Hampton, Andrew

A novel machine learning model for prediction of ICI responsive patients based on ICI pharmacokinetics
TS-075183 — Problem Current eligibility biomarkers for Immune checkpoint inhibitors (ICIs) poorly predict patient’s response. Drug clearance, both at baseline and over time, is a stronger predictor of ICI effectiveness, however the current non-linear mixed effects modeling (NLME) approach for estimating …
  • College: College of Pharmacy (COP)
  • Inventors: Phelps, Mitchell "Mitch"; Adeluola, Adeoluwa; Coss, Christopher; Kim, Kyeongmin; Mo, Xiaokui "Molly"; Owen, Dwight
  • Licensing Officer: Hampton, Andrew

EmitGCL: Prediction of Future Metastasis based on Graph Contrastive Learning
TS-075168 — Problem Metastasis is the primary driver of cancer-related deaths, yet accurately predicting its onset is a major clinical challenge. Current diagnostic tools often fail to detect occult metastatic cells and metastatic precursor cells, resulting in either overdiagnosis, with unnecessary treatments,…
  • College: College of Medicine (COM)
  • Inventors: Ma, Qin; Wang, Xiaoying
  • Licensing Officer: Hampton, Andrew

Adaptive Multi-User Biofeedback Ambience Control Platform
TS-074925 — There is a growing demand for non-invasive, scalable solutions to improve mental well-being, reduce stress, and enhance productivity across diverse environments such as homes, workplaces, healthcare settings, and education. Existing approaches operate largely in isolation and fail to dynamically r…
  • College: College of Engineering (COE)
  • Inventors: Passino, Kevin
  • Licensing Officer: Randhawa, Davinder

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