Carlos Martin
Carlos Martin is an Egleston Scholar studying computer science, applied physics, and applied mathematics at Columbia University. He is passionate about exploration and has performed research in computational physics, theoretical computer science, and machine learning, among other fields.
As a summer fellow at TRIUMF, Canada’s national laboratory for particle and nuclear physics, he worked on laser ion sources and resonance ionization spectroscopy. He also used computational techniques to model physical systems such as fluids, rigid bodies, electromagnetic fields, and chemical reactions.
At Wolfram, he studied different models of computation and published a paper on tag systems. More recently, his interest in artificial intelligence has led him to study the application of neural networks and other machine learning models to real-world problems. He is particularly interested in reinforcement learning and inductive programming.
Through his projects in industry and academia, he has worked with many programming languages and frameworks, including C++, Python, Java, JavaScript, and Mathematica. He is also interested in entrepreneurship and has been involved in multiple entrepreneurial projects.
For more information about Carlos, please visit carlosgmartin.com/