Financial Technologies
Advanced encryption, risk modeling, and algorithmic optimization
UConn researchers in financial technologies are advancing secure, data-driven decision systems that power modern finance. Their work spans algorithmic optimization, risk modeling, and encryption—creating tools that enhance efficiency, resilience, and security across financial, business, and technology sectors while preparing students to lead in an evolving digital economy.
David Bergman
Associate Dean for Faculty & Research; Associate Professor, Operations & Information Management (School of Business)
Bergman develops algorithms for large-scale automated decision-making, discrete optimization, and data-driven analytics. His applied research—spanning scheduling, supply-chain optimization, and sports analytics—advances cross-sector applications and student training in real-world analytics.
Phillip G. Bradford
Associate Professor-in-Residence, Computer Science (UConn Stamford)
Bradford’s research focuses on FinTech, AI, algorithms, and cybersecurity. He applies his research to support industry, to advance course development, and to cultivate student achievement. He has a number of best-in-class research results. Bradford worked for several of the world’s largest asset managers as well as with a number of startups. He publishes in FinTech, AI, algorithms, security, modeling, and leverages his research and teaching to advance partnerships in FinTech.
Carlos Cardonha
Assistant Professor, Operations & Information Management (School of Business)
Cardonha’s research focuses on combinatorial optimization, mathematical programming, and algorithm design for data-intensive decision problems. His work develops computational tools for scheduling, assortment optimization, and resource allocation with practical applications in logistics and service operations.