Nickolas Kintos

Nickolas Kintos serves as Associate Professor of Mathematics at Saint Peter’s University since Fall 2011, holding a Ph.D. in Mathematical Sciences from NJIT and dual undergraduate degrees in Mathematics and Chemistry from Rutgers University – Newark. His academic home resides within the Department of Mathematics, where he teaches courses spanning freshman to senior levels including calculus, statistics, differential equations, and advanced mathematical modeling.

His educational foundation includes:

• Ph.D. in Mathematical Sciences, New Jersey Institute of Technology
• M.S. in Mathematical Sciences, New Jersey Institute of Technology
• B.A. in Mathematics, Rutgers University - Newark
• B.A. in Chemistry, Rutgers University - Newark

Dr. Kintos specializes in applied dynamical systems with concentrated research on computational neuroscience and neuromodulation of small rhythmic networks. His work employs mathematical modeling to dissect neural oscillations, synaptic feedback mechanisms, and central pattern generator functionality in biological networks. This interdisciplinary approach bridges mathematics and neuroscience through rigorous computational frameworks.

Analysis of his publication history reveals consistent focus on neuromodulatory effects in oscillatory networks, with particular emphasis on how divergent neuronal inputs generate network-level behaviors. His modeling comparisons of projection neuron dynamics demonstrate expertise in translating biological phenomena into mathematical constructs, primarily contributing to computational neurodynamics and rhythmic motor pattern research.

His scholarly recognition includes:

• Varrichio Award for Teaching Mathematics
• Research Associate Fellowship
• Kenny Fellowship

Dr. Kintos actively mentors students through course instruction and research collaboration while serving on multiple university committees. His teaching philosophy emphasizes challenging students within an engaging environment, as reflected in his diverse course load from Elementary Applied Mathematics (MA-105) to advanced topics courses. Though specific grant details aren't provided, his collaborative neuroscience research indicates ongoing external funding activity. Laboratory infrastructure isn't explicitly described, but his computational modeling work suggests affiliation with institutional high-performance computing resources supporting neural network simulations.