3 papers in the archive (2025), average empirical rigor 7.5/10, average math complexity 4.8/10. Quadrant mix: Holy Grail ×2, Street Traders ×1.
Works mostly in Crypto Markets & DeFi, NLP & LLMs in Finance.
Frequent co-authors: Fernando Spadea (2), Lorenzo Carta (1), Aaron Green (1), Zihan Nie (1), Hanzhen Qin (1), Kristin P. Bennett (1).
Scores are our LLM-assisted heuristics (methodology), not peer review or citation counts. Author pages are generated from paper metadata; name variants may split one person across pages. arXiv author page →
Ordered by a blend of empirical rigor (60%) and math complexity (40%).
We present the first application of federated learning (FL) to the U.S. National Financial Capability Study, introducing an interpretable framework for predicting consumer financial distress across all 50 states and the District of Columbia without centralizing sensitive data. Our cross-silo FL setu
Most financial recommendation systems often fail to account for key behavioral and regulatory factors, leading to advice that is misaligned with user preferences, difficult to interpret, or unlikely to be followed. We present FLARKO (Financial Language-model for Asset Recommendation with Knowledge-g
Survival modeling predicts the time until an event occurs and is widely used in risk analysis; for example, it’s used in medicine to predict the survival of a patient based on censored data. There is a need for large-scale, realistic, and freely available datasets for benchmarking artificial intelli