Gergely Gidofalvi

Gergely Gidofalvi was born in Budapest, Hungary, in 1977 and moved to the United States in 1996. He received his B.S. degree in 2002 from San Diego State University, his M.S. degree from The University of Chicago in 2003, and in 2006 his Ph.D. from The University of Chicago under the supervision of David A. Mazziotti. The latter work consisted of the development and application of variational reduced-density-matrix theory to strongly correlated systems. Between 2007 and 2010, he worked with Dr. Ron Shepard on the graphically contracted function method as a Director's Postdoctoral Fellow at Argonne National Laboratory. From 2010 to the present time, he has been on the faculty of Gonzaga University in the Department of Chemistry and Biochemistry. His research focuses on the development and implementation of efficient computational algorithms for modelling molecular properties and energetics of reactions.

With recent advances in the computational resources available to chemists, computational methods that describe the electronic structure of atoms and molecules have become an increasingly useful tool for understanding/interpreting molecular properties as well as the energetics and dynamics of reactions. Nonetheless, to help establish electronic structure theory as a truly predictive tool in chemistry, research in our group focuses on the development of more accurate and cost-effective methods. Our recent work (in collaboration with Florida State University and Q-Chem, Inc.) aims to implement two-electron reduced density matrix based approaches that are orders of magnitude more efficient than conventional models and can accurately capture the complex electronic structure of strongly correlated molecules and materials. In conjunction with collaborators at Argonne National Laboratory, we are also actively pursuing the development of the Graphically Contracted Function approach for electronic structure theory; this method, although still in its infancy, has the potential to significantly improve the cost-effectiveness and accuracy of current state-of-the-art computational models.