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
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
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
Human–AI Interaction Research Platform
TS-074578 —
The Need
As AI systems are rapidly deployed across healthcare, education, and high-stakes decision-making, organizations lack rigorous tools to evaluate how humans actually interact with, trust, and respond to AI in real-world conditions.
Opportunity Overview
Researchers at the Ohio State Univ…
- College: College of Arts and Sciences (COAAS)
- Inventors: Meng, Jingbo
- Licensing Officer: Panic, Ana
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
PS3 Algorithm: Scalable Mixed‑Integer Optimal Control for Electrified Powertrains
TS-073768 —
Electrified and hybrid vehicle powertrains are increasingly complex, integrating mechanical, electrical, thermal, and emissions subsystems with both continuous and discrete decision variables. Existing energy management and co‑optimization approaches typically rely on simplified models, sequenti…
- College: College of Engineering (COE)
- Inventors: Anwar, Hamza; Ahmed, Qadeer; Fahim, Muhammad
- Licensing Officer: Ashouripashaki, Mandana
Data‑Driven Powertrain Recommender Systems (PRS) for Optimized Truck Fleets
TS-073692 —
Fleet operators face increasing pressure to reduce operating costs and emissions while maintaining performance and reliability. Choosing the “right” truck (diesel, alternative fuel, or battery electric) for a specific duty cycle remains largely heuristic, conservative, and error‑prone. As a …
- College: College of Engineering (COE)
- Inventors: Ahmed, Qadeer; Subraya-Hegde, Sharat; Villani, Manfredi
- Licensing Officer: Ashouripashaki, Mandana
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
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