Advancing Computer Science research and innovation through Machine Learning and Artificial Intelligence.
I am currently pursuing advanced studies in Computer Science as an international student in Germany. I hold a B.Sc. in Computer Science and Engineering (CSE) from United International University (UIU), Bangladesh.
My expertise lies in Machine Learning Theory and Algorithms, Bioinformatics, and Computer Systems. I am deeply passionate about developing efficient and interpretable algorithms that expand the frontiers of modern AI.
With 7+ years of experience as a Machine Learning Engineer and Research Assistant, I specialize in designing and implementing advanced models that drive data-driven decision-making across academic and industrial domains.
| Machine Learning Engineer (Part-Time) | Siemens AG, Berlin, Germany |
| Applied AI for predictive maintenance and industrial automation — focused on model reliability, multimodal data integration, and scalable deployment. | |
| Machine Learning Engineer | Againsoft, Bangladesh |
| Developed and deployed scalable ML pipelines for financial analytics and automation, enhancing prediction accuracy and model efficiency. | |
| Research Assistant | Physikalisch-Technische Bundesanstalt (PTB), Germany |
| Contributed to AI-based sensor fusion, precision measurement, and time-series signal modeling for metrological applications. | |
| Research Assistant | Hochschule für Wirtschaft und Recht Berlin (HWR Berlin) |
| Focused on Explainable AI (XAI), deep learning interpretability, and multimodal model optimization for research projects. | |
| Research Assistant | Technische Universität Berlin (TU Berlin) |
| Worked on Transformer-based architectures for vision-language integration and multimodal learning systems. | |
| Research Assistant | Fraunhofer Institute, Germany |
| Contributed to industrial AI systems research, focusing on cloud-based deployment, model compression, and real-time inference. | |
Core Domains: Machine Learning Theory, Deep Learning, Multimodal AI, Bioinformatics, Explainable AI (XAI)
Key Architectures: CNN, RNN, Transformer, ViT, GAN, GPT, LLaMA3, Gemma
Frameworks & Tools: PyTorch, TensorFlow, Scikit-learn, MLFlow, DVC, Docker, FastAPI, Flask
Cloud & MLOps: AWS, Azure, Docker, Model Versioning, CI/CD Pipelines
Programming: Python, Java, SQL, Bash
- Designing efficient Transformer architectures for multimodal understanding
- Developing foundation models and vision-language systems from scratch using PyTorch
- Improving explainability and interpretability in modern AI models
- Applying AI in Bioinformatics and scientific data modeling
