Gunduz Caginalp

  • Professor, PhD

Prof. Caginalp joined the Mathematics Department in 1984. He completed his PhD in Applied Mathematics with Prof. Michael Fisher in 1978. He worked as a Research Associate with Prof. James Glimm at the Rockefeller University, and was the first recipient of the Zeev Nehari position at Carnegie-Mellon University in 1980.

His work has been featured in Science, The New York Times, SIAM, Wilmott and other newspapers and magazines

Prof. Caginalp's PhD thesis established the existence and properties of the thermodynamic limit for the surface free energy in lattice systems, consisting of three papers in the Comm. Math Physics. In Pittsburgh, he developed the phase field approach to interface problems, publishing a paper in the Archive for Rational Mechanics and Analysis, that is among the most cited paper in this prestigious journal during the subsequent quarter century. He has published about 50 papers on this topic in mathematics, physics and materials journals.

During the past twenty years, Prof. Caginalp has been a pioneer in Quantitative Behavioral Finance, using both differential equations as well as statistical methods. In addition, he developed an interest in experimental economics, publishing nine papers with 2002 Nobel Laureate Vernon Smith and David Porter. The theory and experiments established price momentum and liquidity (or excess cash) as key factors in resolving the paradox of bubbles. His economics/finance papers on have been downloaded over 12,000 times.

Education & Training

  • Cornell University, AB, MS, PhD

Research Area

Research Interests

Various aspect of the phase field equations such as anisotropy continue to be investigated together with Prof. Xinfu Chen. Another area of research is the application of renormalization ideas to differential equations. In economics/finance, Prof. Caginalp and collaborators are investigating the dynamics and stability through the asset flow approach he developed in the early 1990's. Another facet of research involves statistical analysis of large scale financial data, with the aim of uncovering trader motivation beyond value considerations.