Daniel Alexander van Skye is a Performance Bioinformatics Scientist whose work combines quantitative physiology, computational modeling, and applied human performance science. He leads the development of the Digital BiodataTwin™, an adaptive algorithmic data model that integrates longitudinal autonomic, endocrine, metabolic, respiratory, thermal, and workload data to generate reproducible estimates of readiness, recovery, and resilience.

His methodological expertise includes mechanistic signal extraction, longitudinal model calibration, out-of-sample validation, and the formulation of decision rules that link model outputs to periodization and clinical protocols.

In professional tennis, his advisory work has supported top-ten ATP and WTA players, with contributions such as cycle-aware planning frameworks for WTA athletes and endocrine-sensitive tapering strategies, as well as androgen-profile-based load calibration for ATP athletes. His technical competencies span autonomic profiling, metabolic efficiency modeling, recovery slope quantification, thermal and hydration strain analysis, and multimodal biodata fusion.

Prior roles include senior military intelligence officer, specializing in cognitive warfare and cyber-enabled information operations, and Managing Director of CENTURYUM Cyber Operations Group, providing advanced cyber capabilities to defense organizations.

He is the founder of BIOALGO and 10NIS AI, organizations dedicated to bioinformatics algorithms engineering and applied biodata modeling for elite sport and dual-use applications. Daniel holds a Doctor of Business Administration and MBA from the University of Southampton, and a BBA from the University of Hamburg, alongside certifications in neurobiology, sport psychology, biomechanics, workload management, and applied data analytics. His research objectives focus on expanding the ceiling of tolerable load, widening the resilience window under volatility and environmental stress, and reducing the energetic and cognitive cost per unit of performance through secure, athlete-specific computational models.