2026-2027 USC Capital One Center Call for Proposals

Each year, through a competitive process, the Center will provide support for several research projects focused on the development of new methodologies for AI with potential applications to solving complex financial problems. The Center will also provide annual fellowships to doctoral students working in this research area, enabling them to advance research frontiers. Fellowship recipients will be named as Capital One Fellows in recognition of their promise and achievements. In addition to funded research projects and annual fellowships for doctoral students, the collaborators will host an annual joint public research symposium to share their knowledge with the machine learning and AI communities.

We hereby invite USC faculty to nominate qualifying Ph.D. students for year 2025 Capital One Fellows‍

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Some of the research topics of interest to CREDIF are listed below. It is expected that PhD Fellowships will involve PhD students whose research thesis has some relevance to the topics below:

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1. Robust AI in the face of noisy data and labels: To train AI/ML models in the presence of noisy and imperfect data, we are interested in the development of a Robust AI

framework with focus on enhancing resiliency and accuracy of the AI system that can be built with data anomalies, inconsistencies, collection bias, and under representation of multiple data groups. The robust AI framework should include advanced methodologies for data cleaning, noise reduction, bias detection, and dealing with data drift and data

imbalance.

-> Proposal should outline integrating these capabilities into AI model training and development workflow. The evaluation criteria should focus on ability to handle

imperfect data and model stability.

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2. Synthetic data generation with an emphasis on generation of structured/tabular data: How might we leverage GenAI to synthetically generate structured (tabular) data

retaining the characteristics of real-world data as much as possible, either for the purposes of augmenting training data or for safely releasing proxy datasets with the right

characteristics (while preserving privacy) to enable research community involvement in our problems of interest.

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3. Explainability framework that improves transparency and interoperability in LLM based systems (particularly in the presence of multiple agents):

-> The proposal should highlight unique challenges in explainability in multi-agentic workflow and possible methodologies to enable users to understand the decision-making process inside a complex agentic environment with humans in the loop.

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4. Integration of graph knowledge representations with AI: Providing accurate and relevant information is key to the success of Generative AI systems. However, standard

methods often fall short when dealing with complex questions that require information from various sources. With potential application to financial services, we're interested in

methods that leverage graph-based knowledge representation to address these limitations and improve the quality of AI-generated responses.

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5. Robust GenAI evaluation methods: Evaluating the performance of LLMs has proven challenging despite the various automated metrics we have access to. Aligning these

metrics with human judgement is difficult but crucial for rapid experimentation and decision making. We need to continue investing in trustworthy LLM evaluation methods

to support many of our genAI use cases.

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Criteria

• Quality of research 55%
• PI qualifications 25%
• Relevance to CREDIF 20%

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Full proposals (compiled in a single-file PDF format) should be sent via email to the
administrative program manager Ariana Perez via email (arianape@usc.edu), by 5 pm Pacific time
on 02/23/2026.