Nishan Parvez

Email: nishanparvez@gmail.com
LinkedIn: linkedin.com/in/nishanparvez

Summary

Detail-oriented mechanical engineer with 5+ years of research experience in computational solid mechanics, linear and nonlinear finite element analysis (FEA), and material modeling. Expert in Abaqus and simulation of large deformation, damage, and failure of soft tissue and elastomers. Proficient in scripting, custom material modeling, and data pre/post-processing for virtual product development.

Education and Training

Children’s Hospital at Philadelphia, Philadelphia, PA
Postdoctoral Research Fellow, 2025 - Present

Rensselaer Polytechnic Institute (RPI), Troy, NY
PhD in Mechanical Engineering, 2020 – 2025

Miami University, Oxford, OH
MSc in Mechanical Engineering, 2018–2020

Bangladesh University of Engineering and Technology (BUET), Dhaka, Bangladesh
BSc in Mechanical Engineering, 2013–2017

Core Skills

• FEA & Computational Mechanics: Nonlinear material modeling, viscoelasticity, contact, damage, and large deformation with tools such as ABAQUS and FEBio.
• Programming & Automation: Python, MATLAB, R, CPP; Custom subroutines (ABAQUS), Plug-in development in FEBio; Scripting for FEA pre/post-processing.
• Machine Leanring and Data Science: PyTorch, scikit-learn, Pandas
• Software & Tools: ParaView, SolidWorks, AutoCAD, Version Control, Linux & High Performance Computing environments

Research Experience

PhD Researcher | RPI, Troy, NY (2020 – 2025)

• Led simulation studies of fibrous and soft network materials under complex loading conditions.
• Developed neural network based material (constitutive) models for nonlinear elastomeric behavior, integrated with FEA software.
• Simulated damage and fracture using advanced FEA techniques, leveraging scripting and batch processing.
• Conducted parameter sensitivity and model convergence studies for FEA models.

Graduate Research Assistant | Miami University (2018 – 2020)

• Simulated colloidal and composite structures to optimize their performance.
• Applied data-driven modeling for identifying dynamic cluster formation in physical simulation data using Python-based machine learning workflows.

Selected Publications

• “Nonlinear elastic-plastic behavior of stochastic athermal fiber networks” – Soft Matter, 2025, Physical Review Letter E, 2023, Soft Matter 2023.
• “Damage Mechanism in soft fibrous materials” – IJSS, 2025
• “Physics-preserving neural networks for constitutive modeling” – Engineering with Computers, 2025
• Up-to-date list at google scholar.

Presentations

• “Mechanisms of fracture of cellulose products” – MRS Annual Fall Meeting, 2024
• “Colloidal assembly pathways via manifold learning” – APS March Meeting, 2021
• “Notch insensitivity in network materials” – Society of Engineering Science, 2023

Additional Highlights

• Developed internal tools for batch simulation and pre-processing.
• Developed automated workflows for post-processing and data analysis of large-scale FEA simulation of discrete models of soft materials in large deformation and damage.
• Strong foundation in technical writing and presentation.

… last updated: Nov, 25.