Our research develops probabilistic models and computational methods to study markets as dynamic and information-driven processes.
Applying quantum probability to asset pricing and portfolio theory. We develop non-Kolmogorovian probability models and path-integral methods to capture interference effects in market dynamics and option pricing under non-Gaussian distributions.
Using artificial intelligence to model economic systems under uncertainty. We develop data-driven methods for inference, prediction, and signal extraction.
Extracting structural signals and behavioral patterns from on-chain transaction data. Focus on network topology analysis, transaction flow modeling, and data-driven insights for decentralized ecosystems.
Modeling liquidity, mechanism design, and risk in protocol-governed financial systems. Research spans automated market makers, lending protocols, and tokenomics.
We develop a quantum-inspired framework for asset valuation, where prices are modeled as probabilistic states rather than fixed quantities. Transactions act as measurements that collapse these states into observed market prices, providing a new perspective on valuation under uncertainty.
A novel price-to-utility ratio grounded in blockchain accounting effectively predicts long-term Bitcoin returns and supports an automated trading strategy that outperforms buy-and-hold.
EIP-1559 improves user experience by mitigating intrablock gas price differences and reducing waiting times, with limited measurable impact on consensus security.
Novel approach to understanding Bitcoin transaction patterns and user behavior through advanced cohort analysis techniques applied to blockchain data.
A comparative study of core-periphery network structure across four decentralized banks — Liquity, Aave, MakerDao, and Compound — finding that MakerDao and Compound exhibit greater decentralization in transactions, while all four protocols show concentration around primary external addresses such as Huobi, Coinbase, and Binance.