Pavle Acimovic

Neuroscience major with a minor in Statistics & Machine Learning, with the concentration on creating systems for interpreting neural and biological signals—ranging from brain activity (EEG, fMRI) to molecular-level data (genomics, proteomics). My work emphasizes first-principles approaches to neural signal processing and machine learning, with applications in brain-computer interfaces, cognitive state decoding, and clinical neurotechnology.

I am an undergraduate researcher in the Krienen Lab at the Princeton Neuroscience Institute, where I apply computational tools to uncover circuit-level principles of brain organization. My recent work includes developing quantum-encoded models for EEG interpretation, and applying deep learning to multimodal neural data.

Former Princeton Heavyweight Rower with a background in endurance, collaboration, and composure under pressure.