Career roadmap
15+ years, sensor to system
From stereo-vision safety features to owning imaging-radar integration and leading Visual SLAM: 6 chapters across the ADAS/AV industry. Tap any role to expand the detail.
- Led algorithm team developing monocular Visual SLAM for autonomous parking: feature extraction, Bag-of-Words relocalization, EKF sensor fusion, and sparse 3D landmark-based pose estimation.
- Optimized the vision pipeline for real-time 30 FPS on embedded targets using SIMD vectorization, automated parameter tuning, and runtime-configurable debug/production separation.
- Integrated Lidar and GPS (OxTS) into the data-collection and validation infrastructure with time synchronization for ground-truth reference.
- Ensured ISO 26262 functional safety and ASPICE-compliant processes across the development and V&V lifecycle.
- Conducted code reviews and mentored engineers on vision and fusion development.
Visual SLAMEKF FusionEmbedded 30 FPSISO 26262ASPICE
- Led development of cost-effective imaging radars for autonomous vehicle platforms.
- Owned the end-to-end radar integration lifecycle: technical development, troubleshooting, acceptance testing, and production qualification.
- Drafted component-level radar requirements from vehicle requirements and established traceability.
- Led RFI/RFQ, eSOR, and SOW processes for supplier selection.
- Coordinated 360° vehicle integration with Perception, Systems, Manufacturing, and Quality: mounting, Automotive Ethernet/CAN interfaces, and FOV coverage.
- Defined environmental qualification (−40 °C to +85 °C, vibration, humidity, EMC/EMI) and benchmarked candidates in rain, fog, and snow.
Imaging Radar360° CoverageRFQ / SOWEnv. Qualification
- Drove sensor-sourcing strategy as sole owner of radar systems for the next-gen ADAS platform (Gravity), leading RFI/RFQ and setting component specifications.
- Defined sensor data-consumption and synchronization requirements, shaping the high-performance compute architecture for real-time radar/camera/lidar integration.
- Executed BOM and cost-optimization initiatives with Tier-1 suppliers, reducing module size, power, and thermal footprint.
- Achieved savings of approximately $600,000 by designing an innovative calibration process and altering manufacturing-line procedures.
- Directed environmental qualification (thermal cycling, vibration, IP rating) and vehicle-level validation for production launch (Lucid Air).
Radar Sourcing$600K SavingsCalibrationCompute Architecture
- Led the camera-based Driver Monitoring System (DMS) project with OEMs and suppliers; successfully demoed to OEMs.
- Directed suppliers on algorithm design and deliverables: HW testing, integration, diagnostics, vehicle-level tuning, and validation.
- Developed radar algorithm chains for NCOD/ICM: signal processing, DOA estimation, point-cloud processing, and tracking on custom 77/60 GHz radars in C++/MATLAB.
- Built radar-based NCOD and ICM prototypes, requirements, and feasibility test plans.
Driver Monitoring77/60 GHz RadarDOA EstimationC++ / MATLAB
- Designed and developed a radar + camera fusion system for occupant detection and vital-signs sensing.
- Served as technical point of contact coordinating a 20+ engineer cross-functional effort on radar/camera fusion algorithms.
- Successful demo at the Veoneer North America Ride & Drive Event, 2019, for multiple customers.
- Developed model-based applications (Rear Cross Traffic Alert, Lane Change Assist) for narrowband and 77 GHz radars.
- Recognized and awarded for optimizing development/validation timelines through tooling automation. Cost savings ~$200,000.
Sensor FusionRCTA / LCACross-functional Lead$200K Savings
- Implemented camera-based safety applications (FCW, AEB, primary target selection for Adaptive Cruise Control) and contributed to camera calibration.
- Drafted camera and feature requirements with traceability via PTC Integrity under V-model / ASPICE principles.
- Designed a tree-model structure using navigation and map databases for ADAS features.
Stereo VisionFCW / AEBMap MatchingV-Model