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followers: 8.0 following: 6.0 repos: 31.0 gists: 0.0

Name: Jamal

Type: User

Location: Hannover, Germany

👋 Hi, I'm Jamal Bhatti

👨‍💻 I'm a passionate Computational Engineer with a Master's in Computational Methods in Engineering from Leibniz Universität Hannover. I specialize in blending advanced computational techniques with practical engineering solutions, focusing on machine learning, scientific computing, and computational mechanics.

🚀 About Me

🔍 I've dedicated my academic and professional journey to solving complex engineering challenges through innovative computational methods. My work ranges from developing sophisticated algorithms to enhancing the capabilities of existing simulation and analysis tools.

🌐 You can find me on LinkedIn: Jamal Bhatti

🛠 Skills

Programming Languages: Python 🐍, C++ 💻, MATLAB 🔢, C# 🔧
Technologies: Docker 🐳, Kubernetes 🎡, AWS ☁️, PySpark 🌟
Specialties: Finite Element Analysis (FEA), Computational Fluid Dynamics (CFD), Machine Learning 🧠, Phase-Field Modeling 📐

🎯 Projects

Convex Polygon Operations (2024): Boolean operations using the standard template library in C++ and Python. C++, Python
Plugin for Abaqus (2023): Developed a plugin to create periodic boundary conditions on RVE. Python
Material Model Parameter Calibration Tool (2023): Calibration tool for custom hyper-elastic material models. MATLAB
Cloud Native CI/CD Pipeline (2022): Integration and deployment pipeline using Docker, PostgreSQL, and Kubernetes. Docker, PostgreSQL, Kubernetes
Data Lakes with Spark (2022): ETL pipeline for data processing and storage in AWS S3. Python, PySpark, AWS
Physics-Informed Neural Network for Phase Field Problem (2022): Integrating physics-based constraints in machine learning models. Python, C++

📚 I'm currently learning

Advancing my understanding of quantum computing algorithms and their applications in engineering.
Exploring next-generation optimization techniques in aerospace design.

🤝 How to reach me:

Feel free to reach out for collaborations or just a chat:

📧 Email: [email protected]
📫 LinkedIn: Jamal Bhatti
🐱 GitHub: @jamal-dev

Thank you for visiting my profile! Let's connect and make something awesome together.

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Jamal's Projects

active-set-method-op

It's the Matlab code for the obstacle problem which is in the class of variational inequality methods

asymproj_edge_dnn_tensorflow2

Few modification of original implementation https://github.com/google/asymproj_edge_dnn

automate-data-pipeline-airflow

cloud-data-ware-house

convexpolygonops

Basic boolean operations on polygon.

cracks

pfm-cracks: A Finite Element code for crack propagation

data-modeling

dealii

The development repository for the deal.II finite element library.

desio_gui

$dynamic_fracture icon$ dynamic_fracture

fool_lime_shap

graphsage-pytorch

A PyTorch implementation of GraphSAGE. This package contains a PyTorch implementation of GraphSAGE.

hands-on-advanced-python-2703239

This repo is for the Linkedin Learning course: Hands-On Advanced Python

jamal-dev

Config files for my GitHub profile.

jamal-dev.github.io

material_parameters_calibaration

material_parameters_calibration

message-passing

nd064_course_1

pbc_linear

Periodic boundary condition implementation in Abaqus.

pbc_nonlinear

It implements periodic boundary condition for the general static analysis in Abaqus.

periodic-microstructure

pred2town

Relevant research has been standing out in the computing community aiming to develop computational models capable of predicting occurrence of crimes, analyzing contexts of crimes, extracting profiles of individuals linked to crimes, and analyzing crimes according to time. This, due to the social impact and also the complex origin of the data, thus showing itself as an interesting computational challenge. This research presents a computational model for the prediction of homicide crimes, based on tabular data of crimes registered in the city of Belém - Pará, Brazil. Statistical tests were performed with 8 different classification methods, both Random Forest, Logistic Regression, and Neural Network presented best results, AUC ~ 0.8. Results considered as a baseline for the proposed problem.