Education: B.S. and PhD, Physics
Courses Taught: NA
Dr. Jacobs' research expertise spans the areas of Computational/Statistical Physics, Condensed Matter, Molecular Biophysics, Structural Biology, Computational Biology, Econophysics, modeling and optimization of algorithms. His research is saliently described as modeling and analyzing complex systems with the goal to understand, predict and control their emergent properties. With over 100 publications, he has made contributions in the areas of diffusion in disordered media, where fractal spatial correlations induce anomalous long-time correlated behavior, cellular automata and Boltzmann lattice gas models for fluid dynamics, graph-rigidity theory applied to covalent glass networks that elucidates self-organizing dynamics, fast computational models for protein thermodynamic stability, prediction of allosteric effects in proteins, peptide design, and developed a novel holistic income tax system. His data science contributions include accurate probability density estimation suitable for high throughput analysis using non-parametric models, novel projection pursuit machine learning methods for discriminate analysis, predicting protein coding regions in DNA sequences, and discriminate analysis of EEG signals. Dr. Jacobs is currently developing new combinatorial optimization algorithms for quantum computing. Most recently, Dr. Jacobs is interested in understanding cognitive thinking and the learning process of a system by connecting models of human thought to artificial intelligence, quantifying perception, modeling emotion, and simulating collective human behavior under different social-economic conditions to guide public policy through quantitative analysis.