Control Systems & Algorithm Team for Smart Products
Control systems, embedded software and algorithms for SharkNinja coffee, carbonation, cooking, frozen treat, blender and Shark beauty products.
Portfolio
AI-Assisted Engineering: Adoption and Internal Tooling
LLM-assisted workflows standardized across the controls team, Control Flow architecture diagrams generated instead of hand-drawn, and an internal telemetry diagnostic tool for non-engineers.
Ninja Luxe Cafe Project
Led development of barista assist technology, milk froth control algorithms, and temperature control systems for the Ninja Luxe Cafe series.
Nespresso & Coffee/Tea Projects
Designed and shipped controllers and embedded algorithms (cascade temperature control, flow control, failure detection, altitude calibration) into four Ninja coffee and tea products from prototype through mass production.
Ninja Thirsti: CO₂ Flavored Water & Soda System
Led controls and algorithm development for the Ninja Thirsti carbonated beverage platform (three product generations shipped). Three patents pending covering CO₂ estimation, liquid-temperature detection, and carbonation system architecture.
Real-Time R&D Data Collection Platform
Built the internal Python/Serial data-collection GUI and sensor profiler used across SharkNinja beverage and heated-product R&D. Real-time MCU communication, CSV logging, live plotting. Early SharkNinja project (Senior Systems Engineer, 2022); still used across product lines today.
IoT-Based Pavement Monitoring System
Externally-funded research (Assistant Professor, SUNY Poly): a low-cost IoT platform for wireless structural monitoring of asphalt pavement. Solar-powered edge nodes, LTE/4G uplink, AWS backend. Published at IEEE iThings 2021.
Green Building Innovation Cluster: IoT-Based Distributed MPC for Commercial HVAC
Distributed MPC and IoT platform for commercial-building HVAC, deployed on an 85-zone pilot at Nanyang Technological University. Delivered 12 to 13% measured energy savings in occupied space.
System Identification & Mathematical Modeling: PhD Research
PhD dissertation research at Penn State (2010–2015): sparse and randomized system identification algorithms for noisy, incomplete data, with applied deployments across behavioral health, radar tracking, thermal modeling, and forecasting.
Anomaly & Fault Detection via Linear System Theory: Research Group X∞
Cross-disciplinary research at SUNY Polytechnic Institute (Assistant Professor, 2018–2021), collaborating with CS colleagues in Research Group X∞ on anomaly and fault detection algorithms for cyber-physical systems and IoT.