Dr. Mahammad Khuddush is an Associate Professor in the Department of Mathematics at Vignan’s Institute of Information Technology (VIIT), Duvvada, Visakhapatnam, Andhra Pradesh, India. He is also currently serving as a Post-Doctoral Fellow (Remote) at Woxsen University, Hyderabad. His research interests center on the theory and applications of dynamic equations and calculus on time scales. He has worked extensively on linear and nonlinear Volterra integral equations on time scales, qualitative properties such as the existence, uniqueness, and stability of solutions, and Perron-type exponential stability results. Additional areas of focus include hybrid continuous–discrete systems, singular integro-dynamic boundary value problems, and the development of positive solutions for such problems. He is actively engaged in extending classical results to the time-scale setting and exploring connections with modern computational approaches, including Physics-Informed Neural Networks (PINNs) for inverse coefficient recovery and hybrid dynamic equations. His scholarly activities also encompass discretization methods, asymptotic analysis, and related theoretical developments in the field of dynamic equations. Through his dual roles at VIIT and Woxsen University, he continues to contribute to research projects, manuscript preparation, and collaborative academic work aimed at advancing the understanding of dynamic systems on time scales.
Dr. Mahammad Khuddush is an Associate Professor in the Department of Mathematics at Vignan’s Institute of Information Technology (VIIT), Duvvada, Visakhapatnam, Andhra Pradesh, India. He is also currently serving as a Post-Doctoral Fellow (Remote) at Woxsen University, Hyderabad. His research interests center on the theory and applications of dynamic equations and calculus on time scales. He has worked extensively on linear and nonlinear Volterra integral equations on time scales, qualitative properties such as the existence, uniqueness, and stability of solutions, and Perron-type exponential stability results. Additional areas of focus include hybrid continuous–discrete systems, singular integro-dynamic boundary value problems, and the development of positive solutions for such problems. He is actively engaged in extending classical results to the time-scale setting and exploring connections with modern computational approaches, including Physics-Informed Neural Networks (PINNs) for inverse coefficient recovery and hybrid dynamic equations. His scholarly activities also encompass discretization methods, asymptotic analysis, and related theoretical developments in the field of dynamic equations. Through his dual roles at VIIT and Woxsen University, he continues to contribute to research projects, manuscript preparation, and collaborative academic work aimed at advancing the understanding of dynamic systems on time scales.