Michael is a seasoned finance scholar with over fifteen years of experience in research and teaching, specializing in credit risk modeling, financial stability, and the regulatory frameworks that shape modern banking. He earned his Ph.D. in Finance and Economics from Rutgers University and has held tenure-track positions at leading institutions, including Claremont Graduate University and Lehigh University.

Michael’s research spans a broad range of topics, from bank capital requirements and financial regulation to the use of big data analytics in finance—particularly in risk management and securitization. His recent work explores the dynamic intersection of finance and innovation, with a focus on venture capital (VC) financing and financial technology (FinTech) as drivers of economic transformation and modernization of traditional financial practices.

A passionate advocate for responsible AI in finance, Michael is actively engaged in research on the application of machine learning to financial risk management, emphasizing transparency and the interpretability of predictive models. His contributions extend beyond academic research; as a visiting scholar at the Federal Reserve Bank of San Francisco and through leadership roles in financial engineering programs, he has bridged the gap between theory and practice.

Michael is deeply committed to mentoring and educating the next generation of finance professionals. His teaching portfolio includes advanced topics such as derivatives, risk management, financial engineering, and securitization—equipping students with the analytical and practical skills needed to thrive in today’s complex financial environment.