Ratan is an Assistant Professor in the Cognition and Brain Science Unit of the Department of Psychology, at Georgia Tech (with additional affiliations at Center for Computational Cognition, Neuro@GT, Quantitative Biosciences, Institute for Bioengineering and Bioscience, and IDEaS) and the Director of the Murty Lab.

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Before joining Georgia Tech, Ratan was a Postdoc and Research Scientist at the Center for Brains, Minds and Machines and the McGovern Institute of Brain Research at MIT with Nancy Kanwisher and Jim DiCarlo. He received his PhD in Neuroscience at the Vision Lab, Indian Institute of Science (IISc) with SP Arun.

Ruolin believes that bridging neuroscience and artificial intelligence will lead to interdisciplinary breakthroughs and inspire the development of innovative, inclusive technologies to address real-world challenges. As a graduate student at UCLA, she has worked closely with the blind community as an accessibility researcher and advocate. She has a deep interest in understanding the human brain, particularly the cognitive processes that integrate multiple modalities such as vision and language.

Nick is a second-year Machine Learning Ph.D. student in the School of Math. He is interested in the computational principles underlying visual perception, particularly the relationship between representations from neural networks and humans. Previously, Nick completed research at Carnegie Mellon University in computer vision and received his undergraduate degree in Computer Science at Ohio State University.

Mayukh is an incoming PhD student in the School of Psychology. As a computer scientist his philosophy has been to understand how things work by either building from scratch, or breaking them apart. Before moving to Georgia Tech, Mayukh was working with different start-ups (RunwayML, Eden, and Aleph-Alpha). He got interested in the human brain while working alongside Ratan while he was at the Kanwisher Lab at MIT. ​Unlike his previous endeavors, this time he seeks to understand the brain without the need to build or dismantle it. When not around computers, he likes to read books, particularly sci-fi.

Alish is interested in understanding the computational principles and learning algorithms leading to generalization in natural and artificial agents, especially their visual systems. Alish holds a B.E. in Computer Science from BITS Pilani, India followed by research experiences at CNRS, Baylor College of Medicine, National Brain Research Centre, Northeastern University, and MIT. ​Alish also contributes to the Neuromatch Academy as a TA, making education accessible to everyone. Outside of research, Alish is interested in teaching, mentoring, open-source development, cooking, traveling, and spending time with his cats.

Haider aims to develop biologically inspired artificial intelligent systems for object recognition. He uses a two-pronged approach: he studies how the brain solves object recognition to build more efficient artificial intelligence algorithms, and by advancing the algorithms, he intends to discover models that explain human brain activity and behaviour. Before joining the lab, Haider was an AI Resident at FAIR (Meta AI).

Ranjani is an undergraduate in Computer Science who's received the President's Undergraduate Research Award (PURA) to pursue her research in the lab. She is interested in using predictive deep learning models to understand how brain regions integrate visual information. In her spare time she enjoys racquetball and hiking.