What I do

I build physiometric data systems that help elite athletes and their coaches make precise and physiologically informed decisions. These precision performance systems are developed to detect early signs of internal instability, manage load with biological accuracy, and align recovery capacity with external demands on tour.

At the center of my work is the data modeling of HRV dynamics, hormonal phase transitions, training load reactivity, and cumulative fatigue patterns. These models are continuously updated using longitudinal input streams and calibrated to the athlete’s evolving physiology. Each one supports the coaching process with daily clarity and season-long resilience.

Using AI-supported physiodatalytics, I design AI engines that forecast readiness, simulate load scenarios, and recognize recurring physiological patterns. These include vagal tone recovery curves, sympathetic drift under travel, luteal-phase suppression risk, and surface-induced fatigue lag. Every metric is interpreted in the context of the athlete’s physiological fingerprint and strategic schedule.

The result is a system of real-time performance logic that operates quietly in the background, translating deep internal signals into timely and actionable recommendations. All systems are delivered as part of a service architecture built around the specific needs of each athlete.

I work exclusively with WTA players and their coaching teams to ensure that every output is scientifically grounded, contextually relevant, and immediately usable in a high-stakes environment.

My approach

My work is guided by a single principle: internal signals should shape external strategy.

Physiological state is not static. It is dynamic, adaptive, and highly sensitive to training, travel, and hormonal rhythm. My approach integrates real-time HRV readings, menstrual cycle phase mapping, sleep architecture, and environmental overlays into a single coherent model that evolves with the player across the season.

Each model begins with physiometric data. Through continuous surveillance of recovery slope, autonomic reactivity, and hormonal rhythm, I identify patterns that predict readiness drops, recovery constraints, and volatility windows. This allows for early intervention, biologically timed tapering, and stress adaptation before performance is compromised.

I build these systems with layered architecture. At the base are longitudinal physiodata streams. At the next level are forecasting models that simulate the effects of schedule, altitude, temperature, and surface. Finally, every output is delivered as a tactical cue, whether it be a training load recommendation, match entry readiness, or travel-linked fatigue offset plan.

This is physioperformance science applied in real time. Each signal is modeled. Each decision is timed. Each system is built from the athlete’s own data and refined through direct collaboration.

I do not believe in general templates. I believe in individual architecture.

The future of athletic performance lies in the intelligence of internal signals. That is what I build for.