I am a researcher driven by a fundamental curiosity about how biological systems work—both in principle and in practice. My research lies at the intersection of biology, physics, and computational science, focusing on uncovering the essential rules that underlie biological and behavioural systems. My approach is based on a firm belief: if I can simulate a phenomenon accurately, then I understand the rules that govern it. This philosophy not only guides how I frame scientific questions but also how I design experiments, analyse data, and derive solutions for real-world challenges.

My doctoral research focused on echolocation behaviour in bats—a field rich in complexity, dynamism, and biological elegance. Through a combination of systems analysis, experimental ethology, and in silico modelling, I explored how echolocating bats adapt their sonar emissions and auditory reception in real time to optimise their foraging and pursuit behaviours. This work was not merely descriptive; it was mechanistic. I modelled how sensory systems interact with motor processes, derived the limits of reaction time based on echo latency and neural processing, and identified critical parameters such as the relative velocity of pursuit that shape the structure and function of the sonar signal. I published all associated code and analyses, not only to advance transparency but to promote a reproducible and modular approach to sensory biology.

Throughout this work, I sought simplicity on the far side of complexity. Rather than leaning into overly engineered solutions, I emphasised fundamental models that emerge from first principles. For instance, I derived sonar beam dynamics not just from empirical data but from interference theory, exploring how the anatomical spacing between nostrils could generate frequency-specific gain and null bands and modulate geometric shape and size of the sonar beam. I developed simulation tools that could generalise across species and anatomical forms—offering a pathway to generative bioacoustics.

My experimental designs reflected the same ethos. I built a 45-channel real-time echo-acoustic environment that enabled interactive playback experiments with live bats. By simulating the echo environment dynamically, I could test behavioural responses to stimuli. I designed both lab-based and field-deployable versions of this system, allowing seamless transition from controlled experiments to naturalistic foraging environments. From the ground up, these systems were modular, open, and custom-built—including firmware, real-time DSP pipelines, 3D tracking, and automated data labelling.

The unifying idea across all these efforts was this: we can understand behaviour not just as a collection of responses but as a dynamic system driven by underlying mathematical and physical constraints. My models, from sonar beam geometry to target interception, focused on building computational representations that could be validated by data—and that, once validated, could offer insight into how similar strategies might be used in artificial systems.

This approach naturally extends to my current and future work. I am particularly interested in how the principles derived from biological systems can inform technologies in areas such as sensing, robotics, and intelligent systems. For example, my recent work on responsivity models—describing how agents modulate sensory sampling in response to environmental uncertainty—has direct applications in swarm robotics, where maintaining cohesion and collision-free navigation in real time requires the same principles of feedback-timed interaction. Similarly, my efforts to model temporal convergence in group behaviour extend insights from biological sensing to distributed autonomous systems. (ongoing project)

Going forward, my research will continue to centre on three core pillars:

  1. Biological Systems as Models of Computation Many biological systems perform optimally not by brute force, but by leveraging elegant heuristics evolved through selection. By studying these strategies—particularly in the realms of sensory-motor coordination and adaptive behaviour—I seek to reverse-engineer rules that can be applied to artificial systems.

  2. Simulation as Proof of Understanding I maintain a strong conviction that the ability to simulate a process—faithfully and generatively—is the ultimate test of understanding. Every simulation I build is not just a model, but a hypothesis made explicit. My simulations are not ends in themselves, but tools for prediction, analysis, and the generation of new experiments.

  3. Open, Applied Science I share all my code, models, and tools freely. I believe that openness accelerates scientific progress and that good science should be both rigorous and accessible. Moreover, my work is intentionally grounded in real-world problems. Whether I am developing acoustic tracking systems for bats, IMU-based control systems, or swarm coordination algorithms, the goal is always the same: to find fundamental solutions that work in practice, not just in theory.

I do not see a sharp divide between basic science and applied research—nor do I recognise strict boundaries between scientific disciplines themselves. To me, physics, biology, engineering, and computation are simply different dialects of the same language we use to understand the world. I believe real progress happens when insights from one domain illuminate questions in another, and when tools and models developed for a specific purpose find relevance far beyond their original scope. This integrative view guides my approach to both research and innovation. Whether I am studying echolocation in bats, developing wearable sensors, or building machine learning models, my goal is to distil complex systems into their fundamental components and to use that understanding to solve real-world problems.

Ultimately, I see my role as a systems thinker and builder—someone who abstracts from nature, models with clarity, and creates with intent.

My long-term goal is to foster scientific and technological progress that is unified across disciplines, openly accessible, and inclusive in purpose and practice.