Nesrine Wagaa

- Taught deep-learning and transformer courses with hands-on labs - Supervised projects in vision, NLP, and robotic kinematics - Mentored students in PyTorch/TensorFlow, data prep, training, and optimization - Contributed to a Q1 publication on analytical and deep-learning methods for high-DOF robotic IK

- Taught Python and C programming fundamentals for engineering students. - Supported robotics labs focusing on kinematics, dynamics, and mapping.

- Implemented CNN, LSTM, GRU, and BiLSTM architectures for inverse kinematics estimation and trajectory tracking. - Designed evaluation protocols under sensor noise and singularity constraints. - Resulted in a Q1 journal publication on analytical and deep learning approaches for solving IK of high-DOF arms.

- Designed data-efficient deep learning models for inverse kinematics, using transfer learning and synthetic augmentation to learn accurate motion mappings from limited data. - Built robust Arabic handwritten character classifiers despite small datasets by applying advanced augmentation and data-efficient pattern-recognition methods.