Career and Leadership

My master's research at the Technical University of Nova Scotia used genetic algorithms to optimize active contours in computer vision. I treated the image-processing problem as constrained energy minimization, with an explicit system state and a search process able to escape local minima. My doctoral research at Waterloo moved that computational instinct into RF CMOS, where I had to understand oscillators, frequency conversion, device behaviour, and radio architectures together. Consulting during the same period added image-sensor circuits, VCOs, Bluetooth modelling, process variation, and practical control systems. The applications differed, but each demanded a model that could cross abstraction boundaries.

After I joined Carleton in 2002, I developed a broadband RF and optoelectronics program around semiconductor-laser models, analog optical links, predistortion, distributed amplification, oscillators, and broadband CMOS linearization. A CFI-supported optoelectronics laboratory made that work materially possible. In parallel, my research in nanowatt SAR converters, calibration, time-to-digital conversion, biomedical acquisition, and adaptive front ends established a second continuing line. My present work in microring drivers, short-haul links, SerDes, TIAs, equalization, and high-speed conversion is therefore a return to an established foundation under new bandwidth, process, and verification constraints.

The wearable period brought those circuit and sensing ideas into a complete product system. GestureLogic's LEO platform joined physiological acquisition, motion interpretation, embedded processing, power management, and product design. As principal inventor, co-founder, and CEO, I carried the technology through architecture, patents, partnerships, capital, recruiting, and strategy. That experience made robustness across users, battery life, safety-oriented control, and the translation of noisy measurements into useful decisions part of my research practice rather than afterthoughts.

I have used sabbaticals as deliberate periods of renewal. During my 2009–10 sabbatical, I concentrated work in low-power conversion, biomedical acquisition, embedded control, and capstone design into the wearable-sensing technology that preceded GestureLogic. My 2022–23 sabbatical added formal study in data science and machine intelligence at MIT, giving me contemporary neural methods and vocabulary for interests that began with my master's work in genetic algorithms. The same renewal period included the Kinàmàgawin Indigenous Learning Certificate, placing professional learning alongside technical retraining.

My intelligent-hardware program grew from that renewed computational foundation. Work in spiking, binarized, and ternary hardware, FPGA and ASIC implementation, and hardware-aware approximation now sits beside my research in AI-assisted verification, layout, and compilers, where I treat design effort itself as an engineering constraint. At the same time, my current photonic, wireline, and CERN-linked detector work reconnects learning, conversion, timing, and physical interfaces. The MALTA program, for example, joins active-pixel sensors, asynchronous readout, surrogate models, encoding, and layout-aware timing in one design problem.

From July 1, 2012, to June 30, 2016, I served as Associate Chair, Graduate. I was responsible for admissions, advising, recruitment, funding offers, teaching-assistant allocations, curriculum, enrolment management, and difficult academic or supervisory questions. That role made graduate education visible as a working system: a research program succeeds only when students, supervisors, courses, funding, and institutional decisions reinforce one another.

My department leadership extends the same systems view. As Chair of Electronics, I work across curriculum, hiring, research growth, faculty development, budgets, tools, facilities, and student pathways so that these parts can function as a coherent whole. The proposed Photonics and Optoelectronics stream, the FPGA Drop-In Center, and planning for an AI & HPC Exploratorium translate long-running technical programs into shared educational capacity. My career is broad because I have repeatedly expanded it around new constraints. It remains coherent because each expansion preserves accumulated circuit knowledge and asks how people, models, hardware, and evidence must change together.