AI Interactive Quiz Pedagogy is an AI-powered educational platform developed at SUNY Oswego that transforms traditional quizzes into adaptive learning experiences. The system automatically generates quiz questions from multiple content sources, including documents, PDFs, and web pages, and uses a Socratic teaching approach to provide personalized hints and guidance when students struggle. Rather than simply assessing knowledge, the platform promotes critical thinking, engagement, and deeper understanding through interactive feedback tailored to individual learning needs. Suitable for classroom instruction, homework, and self-paced study, the copyright-protected technology is at TRL 4 and available for licensing.

Use of Probenecid as an Anti-Virulence Agent Against Listeria monocytogenes is a drug-repurposing technology that uses the FDA-approved drug probenecid to reduce the disease-causing ability of Listeria monocytogenes without significantly affecting bacterial growth under tested laboratory conditions. Rather than killing the bacteria, the approach targets virulence mechanisms, including hemolytic activity and intracellular persistence, potentially reducing infection severity while limiting the selective pressure associated with traditional antibiotics. The technology may be developed as a standalone or adjunctive therapy for listeriosis in human and veterinary settings. The patent-pending technology is at TRL 3 (early proof of concept) and available for licensing.

Method and Device for Fabricating Step-Index Hydrogel Optical Fibers Using Hydrodynamic Microfluidic Focusing is a novel microfluidic manufacturing technology that enables the continuous production of ultra-thin hydrogel fibers and multilayered structures with diameters as small as 100 micrometers. Using hydrodynamic flow focusing rather than conventional coaxial extrusion, the platform reduces clogging while providing precise control over fiber architecture, including solid, hollow, core-shell, and step-index optical fiber designs. The customizable, 3D-printed system supports scalable fabrication for biomedical engineering, tissue engineering, drug delivery, optical sensing, and biohybrid devices. The patent-pending technology is currently at TRL 4 and available for licensing.

Intestinal apoM Inhibition as a Specific Approach to Reduce Plasma Chylomicron and Triglyceride Levels is a novel therapeutic strategy that targets apolipoprotein M (apoM) in the intestine to reduce the production of chylomicrons, a highly atherogenic class of lipid particles linked to cardiovascular disease. By acting at the source of post-meal lipid absorption, the technology lowers plasma triglyceride levels through a mechanism not addressed by current lipid-lowering therapies. Its intestine-specific approach may minimize systemic side effects while offering new treatment options for cardiovascular disease, metabolic syndrome, and hypertriglyceridemia. The patent-pending technology is at TRL 3, with proof of concept demonstrated in preclinical models, and is available for licensing.

Methods and Apparatus to Allocate Physical Qubits to Support Distributed Quantum Error Correction (QEC) Codes is a quantum computing technology developed at the University at Buffalo that enables more scalable and fault-tolerant distributed quantum computing systems. The invention provides a framework for allocating data and ancilla qubits across multiple quantum processing units (QPUs), optimizing resource utilization, balancing computational workloads, and reducing communication overhead associated with quantum error correction. By supporting distributed QEC architectures and heterogeneous qubit technologies, the system helps overcome limitations of current noisy intermediate-scale quantum (NISQ) systems and advances the development of utility-scale quantum computing. The technology is protected by U.S. Provisional Patent Application, has been validated in a laboratory environment at TRL 4, and is available for licensing or collaboration.

Template for Selective Area Growth of Group III-Nitride Devices is an advanced LED fabrication technology that improves the performance and manufacturability of group III-nitride devices, including gallium nitride (GaN)-based LEDs. By utilizing selective-area growth techniques, optimized epitaxial processes, enhanced doping methods, and refined device architectures, the technology reduces crystal defects and improves carrier injection and light extraction. These improvements increase brightness, energy efficiency, reliability, and operational lifespan while supporting more cost-effective manufacturing. Potential applications include solid-state lighting, display backlighting, automotive lighting, medical devices, and UV sterilization systems. The technology is protected by U.S. Patent Application, is at TRL 3, and is available for licensing.

Artificial Intelligence (AI) is rapidly advancing—and with it, the promise of solving some of academia’s toughest problems. But while many researchers focus on using AI to find answers, Associate Professor Hiroshi Kera of the Institute for Advanced Academic Research/Graduate School of Informatics is asking a different question: How can we design better problems for AI to solve?

A specialist in algebra—the branch of mathematics that explores equations using symbols and letters, from the quadratic equations taught in junior high and high school to far more abstract structures—Dr. Kera is working at the intersection of computational algebra and AI. By combining computer-based algebraic computation with AI, he aims to open new frontiers in mathematical discovery. We spoke with Dr. Kera about how AI could reshape—and expand—the world of mathematics.