Ayesha Imam

Data analyst with a BSc in Data Science and professional experience at the Department of National Defence. Works in SQL, Python, Power BI and Excel to clean data, evaluate models, and turn results into findings a non-technical audience can act on. Co-author of a peer-reviewed IEEE journal paper on machine learning for power grid security.

Permanent resident of Canada and the United States — no sponsorship required in either country Security clearance: Reliability status (Government of Canada), active

Programmer / Software Developer at Department of National Defence, Software Engineering Team (2026-03 – Present)

• Designed and ran the evaluation of a locally hosted LLM prototype used as the reasoning layer for an NPC in a Godot training application, measuring response latency, answer accuracy against source documentation, hallucination rate, and CPU and memory use across two hardware configurations.
• Built that prototype end to end, routing player input through a local API to a DeepSeek 7B model on Ollama, and reported findings, failure cases and recommendations to the team.
• Develop and maintain training software and internal tools across C#, C++ and JavaScript codebases: feature implementation, debugging and technical documentation.
• Work in an agile team using Azure DevOps, Visual Studio,.NET, Unity and Unreal; identify performance bottlenecks and propose solutions in sprint planning.

Research Intern, Department of Energy funded project at Fisk University (2025-06 – 2025-08)

• Preprocessed cyber-physical power system data and trained XGBoost, LSTM and Transformer models in Python and PyTorch to detect false data injection attacks on the grid.
• Produced the model comparison showing all three detectors held a global AUC of 0.94–0.96 under a blockchain-secured federated learning setup, against a 13–40% increase in training time from block confirmation delays.
• Applied NVFlare federated learning, Ethereum smart contracts and homomorphic encryption so models could be trained across sites without sharing raw data.

Teaching Assistant at Fisk University (2024-08 – 2025-12)

• Supported more than 100 students in data science and computer science coursework (Python, SQL, statistics); co-designed a tutor training curriculum with faculty and ran weekly study sessions.

Bachelor in Data Science, Minor in Business Administration – Fisk University (2025-12)