Mohammad Shakiba was born in Kerman, Iran in 1995. He graduated with a BA in Civil Engineering from Shahid Bahonar University of Kerman (SBUK) in 2017. During his time in SBUK, Mohammad - worked on a project studying the effect of nanomaterials on concrete properties in Prof. Reza Rahgozar group. Since Mohammad was also interested in - differential equations and computer programming, he studied numerical analysis of differential equations such as spectral methods and - finite and boundary element methods which are highly used techniques in solving different problems in Civil and Mechanical Engineering. In 2018, he started his MS in SBUK - in Nanomaterials Engineering which is a branch of Materials Science Engineering. During his MS, he collaborated with Prof. Gholam Reza Khayati, - Prof. Ahmad Irannejad, and Prof. Shahriar Sharafi investigating the properties of CdSe and CdS quantum dots using density functional theory - and predicting the size of nano-hydroxyapatite using machine learning techniques. In May 2020, Mohammad started working in - Prof. Alexey V. Akimov's group remotely from Iran. During this time, he collaborated with one of Prof. Akimov's Ph.D. students, Dr. Brendan Smith, - interfacing the Libra code with different quantum chemistry codes such as CP2K, Gaussian, and DFTB+ which lead to publication of two papers studying - the role of many-body effects in hot carrier cooling dynamics in nanomaterials. In May 2021, Mohammad, under the guidance of Prof. Akimov, started working on interfacing Libra with Libint code - which can compute the overlap integrals in Gaussian type orbital basis. This new tool enabled studying hot carrier dynamics in large scale nanomaterials and periodic solids - with thousands of atoms using extended tight-binding (xTB) approach. Mohammad will start his Ph.D. in Fall 2022 under the guidance of Prof. Alexey V. Akimov at The State University of New York at Buffalo. - His future research will focus on providing efficient tools for studying nonadiabatic dynamics in different structures, such as performing nonadiabatic dynamics in - momentum space, and interfacing Libra with new codes such as OpenMolcas and ORCA. + worked on a project studying the effect of nanomaterials on concrete properties in Prof. Reza Rahgozar group. In 2018, he started his MS in SBUK in Nanomaterials Engineering which is a branch of Materials Science Engineering. During his MS, he collaborated with Prof. Gholam Reza Khayati, Prof. Ahmad Irannejad, and Prof. Shahriar Sharafi investigating the properties of CdSe and CdS quantum dots using density functional theory and predicting the size of nano-hydroxyapatite using machine learning techniques. In May 2020, Mohammad started working in Prof. Alexey V. Akimov's group remotely from Iran. During this time, he collaborated with Prof. Akimov's group interfacing the Libra code with different quantum chemistry codes such as CP2K, Gaussian, and DFTB+ leading to publication of two papers studying the role of many-body effects in hot carrier cooling dynamics in nanomaterials. In May 2021, Mohammad started working on interfacing Libra with Libint code which can compute the overlap integrals in Gaussian type orbital basis. This new tool enabled studying hot carrier dynamics in large scale nanomaterials and periodic solids with thousands of atoms using extended tight-binding (xTB) approach. In Fall 2022, he started his Ph.D. in the group of Prof. Alexey V. Akimov at The State University of New York at Buffalo. Since Fall 2022 as a Ph.D. student, Mohammad published 4 first author and 1 second author research papers in the field of nonadiabatic molecular dynamics. Mohammad entered the field of machine-learning for nonadiabatic dynamics in Fall 2023 by proposing a new approach for nonadiabatic dynamics called “Machine-learned Kohn-Sham Hamiltonian mapping”. Mohammad has several experiences as helper and coorganizer of multiple workshops held at University at Buffalo on nonadiabatic dynamics and machine-learning. Currently, Mohammad aims to extend machine learning methodologies in nonadiabatic dynamics for large scale systems and to assess the performance of multiple nonadiabatic dynamics methodologies.