Navid Tavakoulnia
Aliso Viejo,CA
Summary
PhD Mechanical Engineer with a 4.0 GPA and dual certifications in Data Science and ML/AI from UC Irvine and UC San Diego. Expertise in computational modeling and machine learning applied to fluid dynamics and biomedical challenges. Proven ability to develop innovative solutions in AI and engineering fields. Seeking opportunities to leverage technical skills for impactful projects.
Overview
9
9
years of professional experience
1
1
Certification
Work History
Graduate Student Researcher
University of Alabama
08.2022 - Current
- Machine Learning for Modeling Microplastic Fate and Transport in Porous Media
- Developed a physics informed neural network (PINN) framework to model the dynamics of fibers in porous media, acting as a replacement for computationally intensive CFD simulations.
- Source Code : https://github.com/ntavakoulnia/Machine-Learning-Simulation-of-Microplastic-Fiber-Dynamics-in-PorousMedia.git
- Computational Fluid Dynamics (CFD) and Fluid Structure Interaction Modeling
- Performed numerical simulations of fiber transport at the pore scale using the Immersed Boundary Method (IBM) and High Performance Computing (HPC) to resolve detailed fluid-solid interactions.
- Developed a probabilistic upscaled model for continuum-scale transport of microplastic particles in subsurface systems.
Research and Development Engineer
VentriMend Inc
Lake Forest, USA
10.2021 - 12.2022
- Designed and built structural heart device prototypes, led R&D in CAD modeling.
- Optimized geometries using immersed boundary CFD simulations for hemodynamic performance.
- Integrated ML/AI for defect detection, and enhancing quality control.
- Conducted product testing, documenting improvements through compliance analysis.
- Collaborated on SOP and DOP documentations, adding safety advancements.
Graduate Student Researcher
California State University Fullerton
Fullerton, USA
08.2019 - 05.2021
- Optimized stent designs for congenital heart disease using computational fluid dynamics (CFD) (SimVascular/ANSYS), improving hemodynamic efficiency in patient specific models.
- Developed ML flow prediction models using (Python/TensorFlow) to accelerate design iteration, reducing simulation time by 30% while maintaining high accuracy.
- Executed CFD simulations in an AWS cloud environment, enabling analysis of stent performance under robust boundary conditions.
- Prototyped designs using Solidworks to ensure compliance with clinical constraints.
Undergraduate Student Researcher
University of California, Riverside
Riverside, USA
09.2016 - 06.2018
- Designed a drone delivery system with SolidWorks, improving package efficiency.
- Collaborated on Arduino integration, enhancing accuracy for delivery - github.com/ntava001/drone-delivery-device
Education
Ph.D. - Mechanical Engineering
University of Alabama
Tuscaloosa, AL05.2025
Masters - Mechanical Engineering
California State University, Fullerton
Fullerton, CA06.2021
Bachelors - Mechanical Engineering
University of California, Riverside
Riverside, CA08.2018
Skills
- MATLAB and Python
- SQL and data analysis
- Machine learning frameworks
- Cloud computing (AWS)
- Data visualization (Power BI)
- Web development (Flask)
- Statistical modeling (R)
- Engineering simulation (ANSYS, COMSOL)
- Data science techniques
- Deep learning (TensorFlow, PyTorch)
- Ensemble methods (Random Forest, XGBoost, LightGBM)
- Data manipulation (Pandas, Numpy)
- Data visualization libraries (Matplotlib, Seaborn)
- Natural language processing (NLP)
- Fluid dynamics and CFD
- Hydrological modeling
Certification
- Engineer in Training, NCEES, 05/19
- Certified SolidWorks Professional (CSWP) - Mechanical Design, Dassault Systemes, 01/22
- Python for Data Science, Web, and Core Programming, UCI, 07/24
- Principles of Machine Learning Engineering Bootcamp, UCSD, 05/25
Websites
Publications And Projects
- Transport and localization of microfibers around periodically and randomly placed circular obstacles, Tavakoulnia, N., Momeni, K., & Rasoulzadeh, M., 2025, Physics of Fluids, 37, 6, 10.1063/5.0267020
- Optimal Stent Placement for Patients with Congenital Heart Defects, Tavakoulnia, N., 2021, 10.5281/zenodo.5108841
- Telecom Customer Churn Analysis and Prediction : Source Code : https://github.com/ntavakoulnia/Customer-Churn-Prediction-.git
References
References available upon request.
Timeline
Graduate Student Researcher
University of Alabama
08.2022 - Current
Research and Development Engineer
VentriMend Inc
10.2021 - 12.2022
Graduate Student Researcher
California State University Fullerton
08.2019 - 05.2021
Undergraduate Student Researcher
University of California, Riverside
09.2016 - 06.2018
Ph.D. - Mechanical Engineering
University of Alabama
Masters - Mechanical Engineering
California State University, Fullerton
Bachelors - Mechanical Engineering
University of California, Riverside
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