I’m Director of Control Systems Engineering at SharkNinja, where I lead development of intelligent embedded systems for consumer products. I built and now manage the Control Systems Engineering team, combining hands-on engineering with strategic leadership—from Arduino/R-Pi prototyping to production firmware deployment across 20+ mass-market products.

My expertise spans model predictive control, system identification, robotics, mechatronics, signal processing, and real-time optimization. I work across the full product lifecycle: hardware integration, algorithm design, system testing, and manufacturing support. With a Ph.D. in Electrical Engineering (with a minor in Industrial & Manufacturing Engineering focusing on convex optimization) from The Pennsylvania State University (2015) and M.S. in Electrical and Computer Engineering from Northeastern University (2010), I bring both theoretical rigor and practical, product-driven execution to every project.

Academic and Research Background

My research journey has spanned multiple domains of control systems and robotics. I began with robotics and computer vision at Northeastern University, developing UAV control systems using computer vision algorithms and PID controllers for autonomous target tracking. During my Ph.D. at Penn State, I pioneered the application of robust model predictive control to behavioral health, designing intervention systems for smoking cessation studies and developing mathematical models for behavioral treatment optimization.

At Nanyang Technological University (Singapore), I worked on the Green Building Innovation Cluster (GBIC), where I developed real-time model identification algorithms and distributed MPC strategies for HVAC systems that achieved 12-13% energy savings in commercial buildings. This work combined IoT networks, real-time optimization, and adaptive control.

As Assistant Professor at SUNY Polytechnic Institute (2018-2021), I led research in mathematical modeling, MPC design for Type-2 diabetes artificial pancreas systems, IoT-based structural monitoring, and optimization-based forecasting methods. I also co-founded Ocean Dynamex, developing predictive workflow systems and novel forecasting algorithms for the maritime industry.

Key Accomplishments

Industry Impact:

• 20+ consumer products shipped with embedded control systems and intelligent algorithms
• 12+ patents (3 granted, 9 pending) in beverage systems, thermal control, and intelligent appliances
• 12-13% energy savings achieved in commercial building HVAC systems through distributed MPC

Academic Contributions:

• 20+ peer-reviewed publications in control theory, system identification, MPC, and predictive systems
• Assistant Professor at SUNY Polytechnic Institute, teaching and leading research projects
• Postdoctoral Researcher at Penn State, pioneering MPC applications in behavioral health

Technical Leadership:

• Built Control Systems Engineering team from ground up at SharkNinja
• Hands-on engineer: Prototype development with Python, C++, MATLAB, Arduino, R-Pi, from concept to mass production
• Co-founder, Ocean Dynamex - developing predictive systems and novel forecasting algorithms
• Developed UAV control systems, IoT monitoring platforms, and real-time optimization algorithms

Research Interests & Expertise

Control Systems: Model Predictive Control (MPC) • Distributed MPC • Robust Control • Adaptive Control • Real-time Optimization • Nonlinear Control

Robotics & Automation: Autonomous Systems • UAV Control • Computer Vision-based Control • Robotic Manipulation • Sensor Fusion

System Engineering: System Identification • Mathematical Modeling • Mechatronics • IoT in Control • Embedded Systems • Hardware-in-the-Loop Testing

Applications: Smart Buildings & HVAC • Intelligent Consumer Products • Behavioral Health Interventions • Building Automation • Predictive Analytics • Time-Series Forecasting

I am an active IEEE member, have published 20+ peer-reviewed articles in leading journals and conferences, and have served as associate editor and technical committee member for multiple IEEE events.