• Co-developed a model for predicting perceived Arabic letter features from demographic data, with 90% accuracy.

• Created RESTful backend (Node.js/Express) and data pipelines for linguistics-based model training.

• Conducted advanced statistical analysis using Python and R (AIC, log-likelihood, ordinal regression).

• Improved model prediction confidence and interpretability through hyperparameter tuning and validation.

• Integrated natural language processing algorithms to enhance data analysis capabilities, facilitating comprehensive linguistic research and expanding dataset usability.

• Built Python automation suite for system testing, reducing test cycle time by 45%.

• Developed real-time and asynchronous logging tools for large-scale SAN firmware diagnostics

• Created Ansible scripts to automate hybrid cloud server deployments across test environments.

• Aggregated and visualized SAN performance metrics from distributed systems using pandas and matplotlib.

• Configured advanced Layer 2/3 network features (VLANs, LACP, MLAG) on HPE and NVIDIA switches.

• Automated Medusa test data analysis for SAN (iSCSI/block storage), detecting read/write corruptions.

• Automated electrical test benches with Lua and Python, reducing manual testing time by 35%.

• Developed automated data analysis tools streamlining semiconductor device characterization process.

• Built SPICE model generation tools from raw lab data, enabling clients to simulate part behavior

• Developed Excel/VBA analytics dashboards for quality assurance, identifying product faults 2x faster.

• Worked with graduate TA’s to provide assistance to students in introductory physics labs during in person lab sessions.

• Assisted students in physics help room, to better understand concepts taught in lab, and answer questions about lab reports.

• Guided students through proper lab report formatting, data presentation, and error analysis.

• Helped to set up experiments for upcoming lab sessions, with assistance from graduate TA’s.