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
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
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
AI-Enhanced Predictive Control for Hybrid Powertrain Energy Management
TS-074095 —
Hybrid and electrified vehicles face increasing pressure to simultaneously reduce fuel consumption and tailpipe emissions, particularly during transient operating conditions such as cold start. Conventional rule-based or static control strategies struggle to optimally manage the tradeoffs among en…
- College: College
of
Engineering
(COE)
- Inventors: Liu, Yuxing; Canova, Marcello
- Licensing Officer: Zinn, Ryan
Robust Training of Spiking Neural Networks via Generative AI
TS-073717 —
Spiking Neural Networks (SNNs) promise ultra-low-power, low-latency AI for edge and neuromorphic computing, but their adoption is constrained by fundamental training challenges. SNN performance is highly sensitive to how training data are collected (e.g., lighting, sensor settings, noise), leading…
- College: College
of
Engineering
(COE)
- Inventors: Baietto, Anthony; Stewart, Christopher
- Licensing Officer: Randhawa, Davinder
Tunable Ferrite Nanoparticles for Optimized Heating and Magnetic Performance
TS-073587 —
Magnetic nanoparticles are widely used in applications such as magnetic hyperthermia, catalysis, sensing, and data storage, yet their performance is often limited by poor control over key magnetic properties. Existing materials typically rely on size or shape control alone, which provides limited …
- College: College
of
Engineering
(COE)
- Inventors: Getman, Rachel; Punyapu, Rohit
- Licensing Officer: Randhawa, Davinder
A Novel Machine Learning Approach for Classification at the Network Edge
TS-073225 —
In today's world, we are increasingly using low-cost devices with limited resources (often referred to as "edge devices") which are supported by connected high-performance servers. However, these edge devices often can't handle complex tasks such as classifying data. To make this possible, we need…
- College: College
of
Engineering
(COE)
- Inventors: Li, Chengzhang; Eryilmaz, Atilla; Ju, Peizhong; Shroff, Ness
- Licensing Officer: Giles, David
Propagation-Based Fault Detection and Sensor Optimization for Complex Industrial Systems
TS-072175 — The Need
Modern industrial and energy systems are increasingly complex, making timely fault detection and discrimination critical for safety, reliability, and cost control. Existing fault diagnosis methods often struggle with transient states, require extensive historical data, or lack interpretabil…
- College: College
of
Engineering
(COE)
- Inventors: Smidts, Carol; Diao, Xiaoxu; Li, Boyuan
- Licensing Officer: Giles, David
SPARKLE: Machine Learning Platform for Rapid Organic Battery Material Discovery
TS-071386 — The Need
The search for sustainable, high-performance battery materials is hindered by reliance on finite metal-based resources and slow, trial-and-error development cycles. Organic electrode materials (OEMs), composed of earth-abundant elements, offer a more sustainable path but present challenges …
- College: College
of
Engineering
(COE)
- Inventors: Paulson, Joel; Muthyala, Madhav; Park, Jay; Sorourifar, Farshud; Zhang, Shiyu
- Licensing Officer: Mess, David
Efficient Machine Learning Prediction of Solvation Thermodynamics
TS-071267 — The Need
Modeling solvent effects on catalytic surfaces is critical for designing industrial processes like biomass conversion, fuel synthesis, and electrocatalysis. Traditional multiscale simulations combining density functional theory (DFT) and molecular dynamics (MD) offer accuracy but are comp…
- College: College
of
Engineering
(COE)
- Inventors: Getman, Rachel; Punyapu, Rohit; Shi, Jiexin
- Licensing Officer: Randhawa, Davinder
AI-Driven Intersection Safety System for Vulnerable Road User Protection
TS-070954 — The Need
Intersections are among the most dangerous areas on U.S. roadways, accounting for approximately 25% of traffic fatalities and nearly half of all injuries annually. Vulnerable Road Users (VRUs), including pedestrians and cyclists, face increasing risk due to complex traffic dynamics and limi…
- College: College
of
Engineering
(COE)
- Inventors: Yurtsever, Ekim; Giuliani, Michele; Rizzoni, Giorgio
- Licensing Officer: Ashouripashaki, Mandana
GPS Independent Lane Level Vehicle Localization
TS-069654 — Technology bundle containing T2025-148 and T2024-150.
The Need
Reliable vehicle localization is critical for autonomous and human-driven vehicles, especially in GPS-denied environments such as dense urban areas, tunnels, and off-road farms and construction sites. Current localization methods relying solely on GPS are prone to signal loss and inaccurac…
- College: College
of
Engineering
(COE)
- Inventors: Javed, Nur Uddin; Ahmed, Qadeer
- Licensing Officer: Ashouripashaki, Mandana
SyMANTIC – Novel Symbolic Regression to Discover Accurate Models from Data
TS-069523 — The Need
In many scientific and industrial fields, there is a critical need for interpretable and accurate models that can be derived from complex datasets. Traditional machine learning methods often produce black-box models that lack transparency and interpretability, making it difficult to unders…
- College: College
of
Engineering
(COE)
- Inventors: Muthyala, Madhav Reddy; Paulson, Joel; Sorourifar, Farshud
- Licensing Officer: Randhawa, Davinder
Joint Activity Testing (JAT): A Testing & Evaluation Methodology for Human-Machine Teams
TS-068443 —
In high-stakes industries, the integration of humans and advanced automation systems demands evaluation methods that reliably predict performance under varying challenges. Current testing methods often focus on individual components, failing to assess how human-machine teams operate as a unit, par…
- College: College
of
Engineering
(COE)
- Inventors: Morey, Dane; Rayo, Michael
- Licensing Officer: Giles, David
Optimal and Pure Leaf Classification Trees for Machine Learning (ML) Decision-Making
TS-067550 — A method to improve the performance and accuracy
of ML-based decision trees.
Decision trees are popular machine learning (ML) methods used in classification and regression problems, and they have numerous applications in the real world. Various industries use decision trees to help decide strategies, investments, and operations. In addition, they are used in healthcare to he…
- College: College
of
Engineering
(COE)
- Inventors: Allen, Theodore "Ted"; Arrey, Evelyn; Booth, Matthew; Liu, Enhao; Mashayekhi, Medhi
- Licensing Officer: Giles, David
A Cybersecurity Vulnerability Prioritization System Including Identifying "Super-Critical" Vulnerabilities, predicting "Dark Host" Vulnerabilities, and Addressing Economic Costs
TS-066063 — Our cybersecurity vulnerability maintenance system stands as a pillar
of modern security strategy, transforming reactive security measures into a preemptive defense mechanism. This integration
of technology and economics ensures that your most critical assets are protected efficiently and effectively, making it an invaluable tool for any organization serious about security.
In today’s hyper-connected world, the escalation in cyber threats poses significant risks to organizational data and systems. Vulnerabilities within network infrastructures can lead to massive security breaches, as demonstrated by incidents like the 2017 Equifax hack. Effective vulnerability…
- College: College
of
Engineering
(COE)
- Inventors: Allen, Theodore "Ted"; Liu, Enhao
- Licensing Officer: Giles, David
SimulationAI -- AI-Enabled Software Solution for Physics-Based Simulations
TS-066058 — By adopting our AI-driven solution,
engineering teams can achieve more in less time, push the boundaries
of innovation, and significantly cut down costs, all while maintaining or increasing the reliability and accuracy
of their structural and material analysis. This is not just an evolution in FEM technology—it's a revolution.
In an era where precision and efficiency drive the success of
engineering projects, the finite element method (FEM) remains indispensable but is burdened by high operational and computational costs. These costs often lead to overlooked uncertainty factors, suboptimal designs, and significant finan…
- College: College
of
Engineering
(COE)
- Inventors: Soghrati, Soheil; vemparala, Balavignesh; Yang, Ming
- Licensing Officer: Giles, David
A Regularized Conditional GAN for Posterior Sampling in Inverse Problems
TS-063238 — A novel regularization technique applicable for medical imaging applications that leverages conditional generative adversarial networks (cGANs) to generate reconstructed images in significantly shorter timeframes.
The Need
Several techniques are used for image reconstruction in the medical aren…
- College: College
of
Engineering
(COE)
- Inventors: Bendel, Matthew; Ahmad, Rizwan; Schniter, Philip "Phil"
- Licensing Officer: Hampton, Andrew
Convolutional Neural Network to Assess Phayngeal and Laryngeal Pathology and Function on Nasopharyngolaryngoscopy
TS-063154 — Worldwide, 686,000 new head and neck (H&N) cancers are diagnosed yearly, and 375,000 people will die annually. Human papillomavirus (HPV) is responsible for an increasing subset of H&N malignancies called oropharyngeal squamous cell carcinomas (OPSCC). Although it has a better prognosis than…
- College: College
of
Engineering
(COE)
- Inventors: Krening, Samantha; Gifford, Ryan; Jhawar, Sachin; VanKoevering, Kyle
- Licensing Officer: Hampton, Andrew
Auditing Fairness Online through Interactive Refinement
TS-063038 — The Need
In the era of machine learning, high-stakes decisions are increasingly being made by black box models, leading to concerns about accountability and fairness. These models can exhibit inherent biases, raising the need for a system that ensures accountability and fairness in decision-making …
- College: College
of
Engineering
(COE)
- Inventors: Maneriker, Pranav; Burley, Codi; Parthasarathy, Srinivasan
- Licensing Officer: Mess, David
Novel Deep Learning Model for Reconnaissance of Infrastructure on Drones
TS-063007 — The Need
In disaster-stricken areas, timely and accurate reconnaissance is paramount for effective response and recovery efforts. Traditional methods of assessing damage to critical infrastructure, such as power distribution poles, often involve time-consuming manual inspections, leading to delays …
- College: College
of
Engineering
(COE)
- Inventors: Shafieezadeh, Abdollah; Bagheri Jeddi, Ashkan
- Licensing Officer: Giles, David
Concept Discovery from Text via Knowledge Transfer
TS-050856 — A better way for systems to organize, file, or index documents or content based on actual or anticipated information needed in the form
of a user query or natural language question.
Data Processing and (IT)-related activities, ranging from web hosting to automated data entry services are more important than ever due to the large amounts of data collected through technology. According to IBIS World, "Companies will increasingly capture more data, requiring the outside exp…
- College: College
of
Engineering
(COE)
- Inventors: Das, Manirupa; Fosler-Lussier, Eric; Ramnath, Rajiv
- Licensing Officer: Giles, David
Emergency Response Tool for Industrial Facilities
TS-041844 — Artificial intelligence s
oftware for making risk-informed decisions to prevent and mitigate emergencies.
No solutions exist for predicting the likelihood of future undesirable consequences across various industrial settings. The lack of these solutions subjects personnel, the public, and the environment to potentially catastrophic consequences.
Essential services such as power plants, pump stations,…
- College: College
of
Engineering
(COE)
- Inventors: Yilmaz, Alper; Ajam Gard, Nima; Aldemir, Tunc; Denning, Richard; Lee, Ji Hyun
- Licensing Officer: Zinn, Ryan
Learning Optimal Empirical Reward iNtelligence (LOERN) with Cyber Maintenance Applications
TS-040341 — A s
oftware solution designed to optimize cyber security decision making and reduce maintenance costs.
As the world becomes more and more connected, our systems become more and more vulnerable. If cyber security can become more automated, risk-based decisions can be made more quickly and rationally. Organizations can then leverage this decision-making to improve their security without increasing co…
- College: College
of
Engineering
(COE)
- Inventors: Allen, Theodore "Ted"; Hou, Chengjun
- Licensing Officer: Zinn, Ryan
Subject Matter Expert Refined Topic Models
TS-015175 — Human-Assisted Modeling (HAM), Subject Matter Expert Refined Models (SMERM), and Subject Matter Expert Refined Topic (SMERT) Models.
Currently, unstructured data represents up to 80% of the data within an organization. This means the traditional data, such as sales figures or other statistics, are separated into different documents without a meaningful and effective way to aggregate the data. Due to this there is now an opportu…
- College: College
of
Engineering
(COE)
- Inventors: Allen, Theodore "Ted"; Xiong, Hui
- Licensing Officer: Zinn, Ryan