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: Zinn, Ryan
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; Arrey, Evelyn; Booth, Matthew; Liu, Enhao; Mashayekhi, Medhi
- Licensing Officer: Zinn, Ryan
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; Liu, Enhao
- Licensing Officer: Zinn, Ryan
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: Zinn, Ryan
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
- 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
An information extraction, enrichment, and caching framework for augmented reality applications
TS-063109 — The Need
In the age of augmented reality (AR), there's a growing opportunity for a comprehensive solution that facilitates the seamless exploration of real-world data through camera-based AR applications. These applications require the ability to extract, cache, and enrich information, enhancin…
- College: College
of
Engineering
(COE)
- Inventors: Nandi, Arnab; Burley, Codi; Sarkhel, Ritesh "Ritesh"
- Licensing Officer: Mess, David
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
Method and system for generating data-enriching augmented reality applications from a domain-specific language
TS-063032 — The Need
In the age of augmented reality (AR), there's a growing opportunity for a comprehensive solution that facilitates the seamless exploration of real-world data through camera-based AR applications. These applications require the ability to extract, cache, and enrich information, enhancin…
- College: College
of
Engineering
(COE)
- Inventors: Nandi, Arnab; Burley, Codi; Sarkhel, Ritesh "Ritesh"
- 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: Mess, David
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