Bahareh Mayvan

Bahareh Bafandeh Mayvan, a Ph.D. holder in software engineering, specializes in deep learning and machine vision. She possesses a solid grasp of metrics, statistical modeling, predictive analysis, and various machine learning and deep learning algorithms. Her expertise includes machine learning and deep learning models like CNN, Linear Regression, Logistic Regression, PCA, and SVM. Additionally, she is well-versed in Graph Theory covering areas such as Graph Mining, Semantic Graphs, and Network Vulnerability Parameters.

In the realm of machine vision and image processing, Bahareh excels in Polymotion Analysis, 2D Pose Estimation, Tracking, Segmentation, Shape Representation, Texture Analysis, 3D Sensing, Medical Image Processing, Imaging Geometry, and Mathematical Morphology. She is also proficient in Software Quality Engineering, encompassing tasks like Design Pattern Detection, Code Smell Mining, Software Refactoring, and Benchmarking.

Her programming language skills include C++, Python, and MATLAB, and she is experienced in utilizing various software and libraries such as TensorFlow, Keras, Pandas, Scikit-Learn, NumPy, OpenCV, as well as tools like Git, Jira, Linux, and Docker. Bahareh's technical knowledge extends to Agile and Scrum methodologies.

Bahareh's educational background showcases her commitment to academia, having completed a Ph.D. in Software Systems, a Master's Degree in Algorithms and Computation, and a Bachelor's Degree in Computer Software Engineering. She has held key roles in organizations like Hinge Health, wrnch, Software Quality Lab (SQL), and Machine Vision Laboratory (MVLab). Furthermore, she has experience in academia as a University Lecturer, Lab Instructor, and Teaching Assistant at Ferdowsi University of Mashhad.