Kiran vaideswaran

• Built a 4-mode Scan System (AUTO/OCR/ID/ONESHOT) on Robin pet robot using Tesseract OCR, Vision LLM API, and
OpenCV, hitting 97% document and ID recognition accuracy in live deployment.
• Designed a multilingual AI conversation pipeline (English, Arabic, Farsi) using Vosk STT, Zenon LLM API, and Piper TTS with
intent routing, achieving full response cycle under 2 seconds.
• Shipped a YOLO-based object detection and follower control system that tracks targets in real time and drives robot wheel
velocity directly from camera feed, reducing tracking lag by 40%.
• Built a 60-motion library (gestures, dance, yoga, navigation patterns) and a 50-expression facial animation engine
coordinated with the robot speech output for natural interaction.
• Packaged the full ROS2 AI stack as 8 independently deployable lifecycle nodes on NVIDIA Jetson GPU, improving system
responsiveness by 55% and enabling per-service hot restarts.
• Reduced robot commissioning time by 70% by building a zero-touch QR and Wi-Fi provisioning pipeline and a Flutter
mobile app with 9 live control panels used daily by the ops team.

• Designed a spike-detection algorithm for time-series methane sensor data across 50 METEC sensors, achieving 99%
anomaly detection accuracy validated against ground-truth emissions events.
• Built Isolation Forest pipelines paired with LLM-based root-cause analysis, cutting the time from alert to actionable insight
by 30% on high-frequency sensor streams.
• Reduced geochemical ML forecasting compute time by 85% through R workflow refactoring and feature engineering and
built a TDD test suite that caught 10 critical bugs before production.
• Engineered a Python dead-letter queue pipeline for fault-tolerant sensor ingestion, processing 60% more data per hour
without data loss across multi-network sensor arrays.

• Improved network uptime by 45% by analyzing telemetry from 50+ embedded systems and implementing QGIS-based
diagnostics across AT&T, Verizon and CSU-NET.
• Boosted sensor data accuracy by 30% and reduced field operation failures by establishing robust verification protocols.
• Reduced security vulnerabilities by 40% by designing and deploying an Attribute-Based Access Control (ABAC) framework
for DER, PLC, and SCADA systems managing wind and energy control networks.

• Improved network uptime by 45% by analyzing telemetry from 50+ embedded systems and implementing QGIS-based
diagnostics across AT&T, Verizon and CSU-NET.
• Boosted sensor data accuracy by 30% and reduced field operation failures by establishing robust verification protocols.
• Reduced security vulnerabilities by 40% by designing and deploying an Attribute-Based Access Control (ABAC) framework
for DER, PLC, and SCADA systems managing wind and energy control networks.

• Increased batch processing throughput by 25% by developing optimized ETL pipelines using IBM InfoSphere DataStage
for RBCs enterprise data warehouse.
• Strengthened financial data integrity by 90% by automating large-scale daily transaction validation using JCL, reducing
operational and reporting inconsistencies.
• Designed CI/CD pipelines for Informatica workflows, reducing deployment errors by 20% and improving regulatory
compliance readiness.