Experience

• Contributing to the architecture of a privacy-first urban sensing network CityOS, under Dr. Jorge Ortiz.
• Specifically engineering the Single-Locality Aggregation API, a system designed to ingest and process continuous data streams from fixed, localized environments.
• Developing a multi-modal sensing pipeline for pedestrian behavior analysis, integrating sensor data from cameras, IMUs to identify pedestrians and their activities.

• Architected a PostgreSQL schema and Python ETL workflow to streamline multimodal gait analysis, slashing data synchronization latency by 90% across a corpus of 300+ recordings.
• Developed a dual-stream gait recognition framework leveraging Transformer backbones to correlate video and accelerometer data, delivering a predictive performance of 0.98 AUC-ROC.

Delivered lectures, designed lesson plans, graded assignments, and provided personalized feedback for 50+ undergraduates.

• Deployed an end-to-end Vision Transformer (ViT) pipeline on PyTorch to directly parse multilingual academic records without traditional OCR dependencies.
• Optimized document throughput for an Italian fintech client, achieving 88% extraction accuracy and slashing processing latency by 90%.
• Part of the Pi School of AI Fall 2022 cohort.

• Engineered KQL logic to execute advanced threat hunting across client environments, neutralizing over 1,000 active network intrusions.
• Deployed ML-driven SIEM analytics to automate log ingestion and refine IDS monitoring, significantly accelerating incident detection and response velocity.

• Engineered high-fidelity ROS simulations to validate autonomous navigation, deploying SLAM for instantaneous mapping and localization.
• Implemented robust traffic perception systems using YOLO and R-CNN in TensorFlow, securing a 93% detection accuracy.