Dr. Hadi Susanto was born and grew up in Lumajang, East Java province of Indonesia. He was educated at Institut Teknologi Bandung in Indonesia (BSc Cum Laude in Mathematics, 2001) and the University of Twente, The Netherlands (MSc in Applied Mathematics, 2003; Dr in Applied Mathematics, 2006).
He was then a visiting assistant professor in Mathematics (2005-2007) at the University of Massachusetts Amherst, lecturer in Applied Mathematics (2008-2013) at the University of Nottingham, senior lecturer in Applied Mathematics and then professor of Applied Mathematics (2014-2022) at the University of Essex. He joined Khalifa University as a professor in 2020 and currently serves as the Director of Graduate Studies in Mathematics. He is also adjunct professor (Guru Besar Luar Biasa) in the Department of Mathematics, Institut Teknologi Bandung and member of its Advisory Board.
He currently serves as Associate Editor of Optical and Quantum Electronics (Springer Nature), Frontiers in Photonics (Nonlinear Optics section), and Communication in Biomathematical Sciences (Indonesian Bio-Mathematical Society).
Building next generation orbit propagation and analysis capabilities (Competitive Internal Research Award (CIRA) 2022, PI: E. Fantino - Aerospace Engineering, Co-I: Hadi Susanto)
The project seeks to expand the trajectory analysis software of the Astrodynamics group of Khalifa University with capabilities for:
1) Improved capabilities for propagation of mean orbital elements in N-body systems. Mean elements are ideal tools for designing station-keeping strategies and propagating trajectories into the far future. The differential equations governing their evolution are obtained by analytical averaging of the effect of perturbing forces over one orbit. This task focuses in modeling the long-term evolution of the orbits of spacecraft and planetary systems, including the complex interactions between non-spherical shapes of celestial objects.
2) Application of Lagrangian descriptors to the identification of dynamic structures in systems containing a spacecraft and several celestial bodies. These structures create low-energy pathways enabling efficient interplanetary and interlunar transfers. Lagrangian descriptors promise improved robustness, versatility and efficiency compared with the techniques in current use.
Machine learning for dynamical systems analysis (Faculty Start-up Grant (FSU) 2021, PI: H. Susanto)
Numerical computations will become necessary to analyze complex mathematical models, such as those in higher dimensions, and dissect their dynamics and characteristics. Nonetheless, even with the current computing power and performance, it is easily said than done due to the so-called ‘curse of dimensionality’, i.e., adding extra spatial dimensions in mathematical models will make the computational cost for solving them increase exponentially. The same curse is also present in Data Science that involve learning a "state-of-nature" from a finite number of data samples in a high-dimensional feature space. However, highly scalable solutions to such problems have been provided successfully by Machine Learning algorithms, such as deep neural networks. This project aims at employing and developing novel Machine Learning algorithms to provide solutions to the curse of dimensionality in the analysis of higher dimensional dynamical systems, particularly in the area of nonlinear waves.
Director of Graduate Studies in Mathematics, 2020 – present
KU Graduate Student Committee member, 2021 – present
