Joseph Makin

Joseph Makin is an Assistant Professor of Electrical and Computer Engineering at Purdue University, affiliated with the Elmore Family School of Electrical and Computer Engineering. His research focuses on mathematical neuroscience, neural computation, and brain-machine interfaces (BMIs), with a strong emphasis on machine learning applications in neuroscience. He leads the Makin Lab, which develops algorithms for BMI/BCI systems, investigates theoretical aspects of neural computation, and explores pure machine learning principles inspired by biological systems.

• Education: Not explicitly listed in provided texts.

His work bridges computational neuroscience and engineering, particularly in decoding speech and movement from neural activity using ECoG arrays. Notable projects include real-time speech decoding in paralyzed individuals and developing encoder-decoder frameworks for cortical activity translation. The lab also focuses on advancing recurrent neural network architectures and probabilistic graphical models, as evidenced by their textbook An Introduction to Modern Statistical Learning.

Recent research trends emphasize self-supervised learning for speech decoding, hybrid system modeling of biological processes, and translational applications in neuroprosthetics. His team collaborates with neurosurgeons and biomedical engineers to translate theoretical insights into clinical tools.

• Scientific Awards: No explicit awards listed, though his BCI project reached finalist status in a major competition.

Makin advises graduate students in machine learning and BMI projects, prioritizing strong backgrounds in neural networks and statistical methods. The Makin Lab is part of Purdue's broader efforts in computational neuroscience and biomedical engineering.