The goal was to create a seamless, automated digital solution that ensured high data integrity without compromising user experience.

I architected a robust Flutter application integrated with a Google Cloud Platform (GCP) & Firebase backend. The system replaces manual sign-ins with a secure, event-specific 6-digit code validation system.

• Eliminated proxy attendance and ensured user privacy by developing a Flutter application that executes Geofencing verification locally on the client-side, ensuring zero raw location data is transmitted to the server.

• Optimized user experience, achieving 35% faster check-in times by implementing A/B testing via Firebase Remote Config to dynamically toggle Geolocator settings based on real-time network conditions.

• Reduced false-negative validation errors by 12% by conducting rigorous Hypothesis Testing to calibrate the geofence radius, balancing security requirements with GPS hardware limitations.

• Neutralized GPS spoofing attacks by engineering Automated ETL Pipelines that feed into an Outlier Detection model, identifying spatial anomalies in "successful" check-ins that bypassed initial validation.

• Enabled scalable, zero-downtime event management by architecting a hybrid data model using GCP Firestore for real-time syncing and Cloud SQL (Firebase)for structured historical archiving.

• Streamlined institutional onboarding by integrating Federated Authentication with University SAML SSO, reducing login friction while maintaining strict access control standards.

• Eliminated manual data entry bottlenecks and reduced processing lag by 40%, achieved by engineering an automated ETL pipeline to synchronize high-volume key check-in/check-out datasets.

• Restored data integrity and recovered 18% of incomplete records by executing advanced data wrangling, utilizing temporal correlation algorithms to impute missing User IDs.

• Unlocked granular event analysis and user segmentation capabilities by transforming unstructured raw logs into structured, schema-enforced formats using Pandas DataFrames.

• Secured 95% data cleanliness and enabled reliable downstream predictive analytics by establishing a rigorous Data Quality Framework to filter noise and validate raw inputs.

In my role as a Graduate-IT Associate at Carnegie Mellon University's College of Engineering, I excelled in troubleshooting hardware and software issues, managing user accounts, and utilizing various IT tools to enhance operational efficiency.

• Demonstrated effective collaboration in team environments through leadership roles in academic projects and extracurricular activities.

• Leveraged strong analytical skills and passion for technology to deliver exceptional user support and contribute to streamlined IT operations.

Delivered a 10% increase in operational efficiency and minimized logistics costs for Linde Inc. by engineering a Deep Reinforcement Learning framework (GAT + RL) to optimize complex vehicle routing and load balancing tasks.

Outperformed Google OR-Tools and Deep Q-Networks (DQN) in computation speed while achieving 62% routing efficiency by refining the RL agent with Policy Gradient methods and Adaptive Sampling techniques.

Optimized spatial encoding and route prediction on high-dimensional data by implementing Multi-Head Attention mechanisms and Node Embeddings within a Graph Attention Network, utilizing Data Augmentation to improve model generalization.

Enhanced model robustness and prediction accuracy by deploying Ensemble Learning and Gradient Boosting strategies to handle dynamic constraints and varying route topologies.

• Mathematics Tutor at PLUS - Personalized Learning Squared, affiliated with Carnegie Mellon University's School of Computer Science.

• Developed personalized math tutoring techniques for students from diverse backgrounds to enhance skills and confidence.

• Utilized interactive tools to create engaging learning experiences and inspire interest in math and STEM fields.

• Provided tailored feedback and guidance to help students achieve academic goals.