Romina Arab

Interdisciplinary researcher with an M.Sc. in Cellular and Molecular Sciences – Genetics and an M.Sc. in Data Science, AI and Digital Business. Research experience spanning molecular genetics, biological data analysis, multi-omics integration, and machine learning. Practical laboratory experience in molecular biology, complemented by Python-based computational analysis of genomic, clinical, and biomedical datasets.

Strong interest in applying integrated experimental and computational approaches to investigate molecular mechanisms underlying disease.

Master's Thesis Research – Multimodal Multi-Omics Deep Learning at Gisma University (2024-01 – 2025-12)

Key Success: Integration of six heterogeneous biomedical data types into a reproducible computational research pipeline for multimodal predictive modeling.

• Integration and preprocessing of genomic, transcriptomic, and clinical datasets from TCGA and GEO.
• Development of Transformer-based models in PyTorch/TensorFlow for multimodal biomarker prediction and immunotherapy-resistance modeling.
• Application of feature engineering, data-quality procedures, and performance evaluation using AUROC, precision, recall, and F1-score.

Molecular Biology Research Assistant – Cancer Genetics at University of Medical Sciences, Mazandaran (2017-01 – 2018-12)

Key Success: Contribution to molecular genetics research investigating genetic polymorphisms associated with upper gastrointestinal cancer.

• Execution of DNA extraction, PCR, PCR-RFLP, gel electrophoresis, and molecular biology procedures for genetic polymorphism studies.
• Application of statistical analysis to genetic association data and interpretation of genotype–phenotype relationships.
• Maintenance of experimental records and structured documentation of laboratory procedures and results.

Data Scientist at That was easy AI (2025-03 – 2026-07)

Strategic Contribution: Transfer of computational and data-science methods to biomedical information analysis.

• Development and evaluation of NLP/LLM-based computational workflows for biomedical information extraction and data processing.
• Application of Python-based data processing and validation approaches to scientific information.

Machine Learning Engineer Intern at U-Glow GmbH (2025-01 – 2026-02)

• Development and evaluation of deep learning pipelines using Python, PyTorch, and TensorFlow for image-based anomaly detection.
• Implementation of data preprocessing, model evaluation, and reproducible development workflows using Git and Docker.

M.Sc. in Data Science, AI and Digital Business – Gisma University (2024-10 – 2025-09)

M.Sc. in Cellular and Molecular Sciences – Genetics – University of Sari (2016-09 – 2018-09)

B.Sc. in Biology – Animal Science – Golestan University (2011-09 – 2015-07)