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Predictive Computational Platform for Selective Rare Earth Ligand Design
TS-075918 — Rare earth elements (REEs) are essential for advanced manufacturing, energy technologies, electronics, and defense applications, yet their separation remains one of the most challenging and costly steps in the supply chain. Conventional separation methods often require extensive processing, hazard…
  • College: College of Engineering (COE)
  • Inventors: Getman, Rachel; Biswas, Sayani
  • Licensing Officer: Randhawa, Davinder

AI-Enabled Quantitative Assessment of External Heart Pump Performance
TS-075868 — Clinicians managing pediatric patients supported by a ventricular assist device must routinely assess pump performance by visually estimating membrane fill and ejection during pump cycles. These evaluations are critical for patient management but are inherently subjective, time-consuming, and pron…
  • College: College of Engineering (COE)
  • Inventors: Eichaker, Lauren; Fabian, Benjamin; Florence, Megan; Jain, Amara; Juarez, Alejandro; Nandi, Deipanjan; Robinson, Tavi; Sharma, Meghna
  • Licensing Officer: Sharick, Joe

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

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

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
  • 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 Electroceutical Wound Dressing with AI-Driven Therapy
TS-074767 — Chronic and complex wounds remain a major clinical and economic burden, with high infection rates, slow healing trajectories, and limited real-time monitoring capabilities. Conventional dressings are largely passive and do not adapt to dynamic wound environments, while existing advanced therapies …
  • College: College of Engineering (COE)
  • Inventors: Karnes, Michael
  • Licensing Officer: Randhawa, Davinder

Ultra‑Fast 3D Real‑Time Cardiac MRI Without Gating or Binning
TS-074669 — Current cardiac MRI workflows rely heavily on breath-holds, ECG gating, and retrospective binning, which break down in patients with arrhythmias, irregular breathing, or limited ability to cooperate. Existing 3D approaches often average away beat-to-beat variability or suffer from motion artifacts…
  • College: College of Engineering (COE)
  • Inventors: Ahmad, Rizwan; Arshad, Syed Murtaza; Chen, Chong; Sultan, Ahmad
  • Licensing Officer: Randhawa, Davinder

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