Kiran vaideswaran

Built a cross-platform mobile app (iOS/Android) robotics control system integrated with ROS2, REST APIs, SQLite, and
Python backends, enabling real-time robot monitoring and control and improving operational efficiency by 60%.
• Developed AI vision systems (OCR, CNNs, OpenCV, TensorFlow) for Scan Mode and face-based user profiling, achieving
97% accuracy in identity verification and automating document + user recognition workflows.
• Designed scalable ROS2 robot architecture + NVIDIA Jetson GPU pipelines, enabling perception, decision-making, facial
animation, and autonomous behavior with 55% improved system responsiveness and modularity.
• Built automated robot onboarding system (QR + Wi-Fi + touchscreen setup) reducing deployment and commissioning
time by 70% across robotic devices.
• Enhanced enterprise HR platform using C#, SQL Server, JavaScript, adding Dark Mode, GPS tracking, NFC auth, Gantt
charts, and search optimization, improving usability and system performance.

• Accelerated methane emissions detection and analytics for Oil & Gas infrastructure (METEC facility) data processing by
60% by developing a Python-based DLQ pipeline, enabling scalable and high-accuracy sensor analytics.
• Reduced computation time by 85% by optimizing R-based forecasting workflows used for geochemical analysis.
• Achieved 99% anomaly detection accuracy by designing a scalable spike-detection algorithm for time-series data.
• Improved code reliability by 70% by creating a TDD unit-testing framework that identified 10+ critical bugs.
• Enhanced spatial data operations by improving deployment efficiency by 50% and shortening integration time by 30%
through a scalable spatial data infrastructure.
• Boosted anomaly-insight speed by 30% by building ML pipelines using Isolation Forest models and LLM-based
automation.

• 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.