it is IBM Global University program’s expectation that two of the four applications are for diversity candidates or underrepresented populations in technology.

Students may not self-nominate; nominations must be submitted by the department to GEPA, which will review and select the nominations to be forwarded for consideration by IBM.

Faculty should coordinate their nomination(s) with their department chair as there is a limit of three (3) nominations per department. 

Submissions should support one of the subsets of a major category in the following list:

Hybrid Cloud

Optimization of incorporating open standards and open-source code resulting in the enablement and creation of a seamless hybrid cloud platform that can be deployed anywhere. Proposals can include Platform Enablement and Optimization with an open-source component; or use this category for interdisciplinary entries.

Flexibility and Scalability.

Submissions could propose research that leads to the creation of hybrid cloud platforms enables more flexible, scalable computing, unifying local environments with a virtually limitless pool of computing power and capabilities, making bits, neurons, and qubits available on-demand.

Accelerating adoption. Making hybrid cloud adoption easier and safer by enhancing agility through automation.

Submissions could address leveraging AI for code to help automate essential tasks like application modernization, vulnerability detection in code, troubleshooting of IT reliability issues, and research focusing on cutting edge technologies in antivirus and other protection mechanisms.

Security. Focus on designing for security and compliance in niche areas or across the stack: from the hardware, encryption technologies, the hybrid cloud platform, to the SecDevOps pipeline. Trusted service identity/ identity access management across the stack or address niche areas such as high assurance through Encrypted Container Images. Research covering any and all arrays of software and platforms are encouraged.

Edge Computing

AI advancements in hardware or training models, characterizing and classifying unknown instances, and federated learning. Security topics are highly encouraged: data encryption, storage advancements, unified endpoint management, and firmware or chip-level proposals.

AI Hardware

Processing efficiency. The next improvements in devices, architectures, and algorithms Nominations could include research that combines these topics and apply them to Deep Neural Networks far beyond the present architectures of GPUs and CMOS Accelerators.

Digital AI Cores. New accelerators for existing semiconductor technologies that use reduced precision to speed computation and decrease power consumption using reduced precision techniques.

Analog Cores. Memory-based technology to advance AI at VLSI, analog memory devices and hardware accelerators, mixed precision in-memory computing, hybrid design for AI Software, and other 8-bit breakthroughs.

Heterogeneous Integration. AI applications drive the need for a system level optimization of AI Hardware through Heterogeneous Integration of Accelerators, Memory and CPU to enable high-speed/high-bandwidth connectivity components. Proposals can bridge these areas.

Machine Intelligence/Neural Networks. Machine Intelligence differs from machine learning. Solving some of AI's greatest challenges using associative reasoning to mimic human intelligence. with brain science.

AI Engineering

Optimization. Tools for AI creators to reduce the time they spend training, maintaining, and updating their models. New approaches, strategies, and research to explore advanced problems automatically. Best models for ML and data science pipelines, best architectures for deep learning, and best hyperparameters for AI models and algorithms.

Privacy and Security. IT Infrastructure consumption models, privacy assurance, hybrid cloud strategies, storage infrastructures, privacy and security assurance held in the hardware, and progressive hybrid cloud infrastructure including storage optimization.

Neuro Symbolic AI

Deep Learning to combine the power of neural networks with symbolic methods to advance AI reasoning effectiveness.

Neuro-symbolic AI NLP and QA.

Submissions that cover applied challenges posited by neural networks, like symbolic AI, Q&A, probabilistic physics inference models, or new neuro-symbolic technique. New systems for knowledge-based question answering.

Secure, Trusted AI

Secure, Trusted AI. Building evaluating, and monitoring for trust. AI is developing diverse approaches for how to achieve fairness, robustness, explainability, accountability, value alignment, and how to integrate them throughout the entire lifecycle of an AI application.

Techniques to detect and mitigate bias in datasets and models. Addressing the need for understanding and removing gender stereotypes, as well as citing and/or rating AI services for bias.

Robustness, and Privacy. Evaluating and defending machine learning models and applications against adversarial threats and/or conform to required privacy.

Explain-ability, Accountability, and/or Transparency. Advancing an AI system to ‘explain itself.’ Exploring the inner workings of an algorithm to provide stakeholders explanations for different purposes and objectives that are tailored to their needs.

Quantum

Advanced foundational quantum information science. Exploring and developing new quantum algorithms to reducing error rates to ensure more accurate and reliable results.

Quantum hardware. Specialized quantum hardware and systems to scale Quantum volume while also increasing qubit count.

Quantum Circuits and Software. Development of quantum circuits and software to explore and develop compelling use cases for this powerful new form of computing.

Award recipients will be selected based on their overall potential for research excellence, the degree to which their technical interests align with those of IBM, and their progress to-date, as evidenced by publications and endorsements from their faculty advisor and department head.