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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

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

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

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, Muhammad Ahmad
  • Licensing Officer: Randhawa, Davinder

Modular Generative AI Framework for Efficient Molecular Discovery
TS-074599 — Discovering molecules that simultaneously satisfy multiple competing design criteria is a resource-intensive challenge across the pharmaceutical, energy, and materials industries. The enormity of chemical space makes exhaustive screening impractical, while existing AI-guided methods either restric…
  • College: College of Engineering (COE)
  • Inventors: Paulson, Joel; Muthyala, Madhav Reddy; Sorourifar, Farshud; Tan, Tianhong
  • Licensing Officer: Randhawa, Davinder

DIAMOND: Risk-Based Cyber Vulnerability Management with Business Context Analytics
TS-074364 — Senior executives struggle to understand and prioritize cybersecurity risk in business terms. Existing vulnerability scoring systems rely on opaque or arbitrary measures that fail to connect cybersecurity decisions to financial impact, staffing costs, or operational tradeoffs. As a result, organiz…
  • College: College of Engineering (COE)
  • Inventors: Allen, Theodore "Ted"; RoyChowdhury, Sayak
  • Licensing Officer: Zinn, Ryan

DEEP Phaser: AI Powered Automation for NMR Phase Correction
TS-073932 — DEEP Phaser enables fully automated, expert level phase correction to improve NMR data quality, consistency, and throughput across routine and high-volume workflows. Problem Overview Accurate phase correction is essential for reliable NMR interpretation and quantitative analysis. Despite its impo…
  • College: College of Arts and Sciences (COAAS)
  • Inventors: Li, Da-Wei; Bruschweiler, Rafael
  • Licensing Officer: Panic, Ana

Personalized Over-The-Air Federated Learning with Personalized Reconfigurable Intelligent Surfaces
TS-073841 — Current federated learning (FL) systems face significant challenges in efficiently aggregating model updates over wireless networks, especially in environments with diverse data and varying channel conditions. There is a critical need for a solution that enhances bandwidth efficiency, personalizat…
  • College: College of Engineering (COE)
  • Inventors: Mao, Jiayu; Yener, Aylin
  • Licensing Officer: Ashouripashaki, Mandana

Vehicle-in-Virtual-Environment (VVE) Method for Autonomous Driving System Development and Evaluation
TS-073818 — Autonomous vehicles and advanced driver-assistance systems require extensive testing across rare, hazardous, and edge-case scenarios to ensure safety and regulatory readiness. Existing approaches (pure simulation, hardware-in-the-loop, proving grounds, or public-road testing) each suffer from trad…
  • College: College of Engineering (COE)
  • Inventors: Guvenc, Levent; Aksun Guvenc, Bilin
  • Licensing Officer: Randhawa, Davinder

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