Machine Learning
Applied ML systems, from model architecture to production deployment.
Placeholder detail copy for Machine Learning — swap in a real case study, methodology, or outcomes once content is ready.
what we work on
Applied ML systems, from model architecture to production deployment.
Placeholder detail copy for Machine Learning — swap in a real case study, methodology, or outcomes once content is ready.
Quantitative modeling and data-driven tooling for financial decision making.
Placeholder detail copy for Finance — swap in a real case study, methodology, or outcomes once content is ready.
Technology that adapts to how people actually learn.
Placeholder detail copy for Education — swap in a real case study, methodology, or outcomes once content is ready.
Human-centered systems focused on health and wellbeing outcomes.
Placeholder detail copy for Wellness — swap in a real case study, methodology, or outcomes once content is ready.
Robust control systems for industrial and automation environments.
Placeholder detail copy for Industrial Control — swap in a real case study, methodology, or outcomes once content is ready.
who are we
Cofounder
Bachelor in theoretical physics. Former data specialist at a building-management-system company, being solely responsible for research, modeling and infrastructure building. Currently doing a MSc, researching continuously monitored medical health data for event prediction.
Cofounder
A mathematical physicist with a strong interest in complexity science, machine learning, statistical mechanics and high-performance computing. Formerly a research scientist who worked in experimental astrophysics where he worked the design, simulations, and implementation of optical control systems for use in large scale interferometry. Outside of his activities with Monkeybrains, is finishing his PhD in theoretical physics with a focus on developing theory for understanding bias and overfitting in machine learning models for applications in medicine.
Cofounder
Bachelor in computer science and physics, former full-stack software engineer. Masters in physics with research conducted on the properties of RL agents in tackling varying environment topologies. Conducted research in time-series prediction of medical data and reinforcement learning. Co-founder of MagiQware, an AI company applying reinforcement learning techniques to areas of quantum error correction.
Cofounder
Trained with a BSc in Applied Physics and a MSc in Neurophysics, then worked as a computational neuroscientist at a world-renowned institute for understanding neural dynamics during freely moving behavior in mice, and now researching computational methods for neurotechnological applications. Interested in understanding and improving the world and industrial processes through theory and computation.
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