I am a research scientist at Columbia University’s Zuckerman Institute. My work sits at the intersection of neuroscience and artificial intelligence, using deep learning, probabilistic modeling, and large-scale data analysis to understand brain function and build biologically inspired AI models.

My research focuses on understanding how the brain recognizes faces and its underlying computational mechanisms. At the Visual Inference Lab, I use artificial neural networks (ANNs) to simulate diverse face recognition tasks and apply a distinct method—Artiphysiology—to manipulate these models and study how invariant face recognition emerges at single-cell and population levels. By working closely with experimentalists, I test model predictions against neural data to uncover the principles that drive face perception.

Previously, I developed scalable machine learning pipelines for neural and image data in the Coen-Cagli Lab at Albert Einstein College of Medicine, and designed deep learning models to study visual processing in the Laboratory of Neural Systems at Rockefeller University. I hold a PhD in computational neuroscience, with research spanning both modeling and experimental studies of the primate visual system.

Selected publications

Relating natural image statistics to patterns of response covariability in macaque V1

A. Farzmahdi , A. Kohn , R. Coen-Cagli — Nature Communications (2025)

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Determining how the brain encodes sensory information requires understanding the structure of cortical activity, including how its variability is shared among neurons. The role of this covariability in cortical representations of natural visual inputs is unclear. Here we adopt the neural sampling hypothesis and extend a well-established generative model of image statistics, to explain pairwise activity as representing joint probabilistic inferences about latent features of images. According to the theory, variability reflects uncertainty about those latent features. In natural images, some sources of uncertainty are shared between features and lead to covariability between neurons, whereas other independent sources contribute to private variability. Our analysis shows that spatial context in images reduces shared uncertainty for overlapping features, whereas it reduces independent uncertainty for non-overlapping features. As a result, the model predicts that increasing the size of an image reduces correlations for pairs with overlapping receptive fields and increases correlations for pairs with offset receptive fields. This prediction was confirmed by recordings from male macaque primary visual cortex. Our study establishes a precise connection between V1 correlations and natural scene statistics, suggesting patterns of covariability are a feature of probabilistic representations of scenes.
Emergence of brain-like mirror-symmetric viewpoint tuning in convolutional neural networks

A. Farzmahdi , W. Zarco , W. A Freiwald , N. Kriegeskorte , T. Golan — eLife (2024)

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Primates can recognize objects despite 3D geometric variations such as in-depth rotations. The computational mechanisms that give rise to such invariances are yet to be fully understood. A curious case of partial invariance occurs in the macaque face-patch AL and in fully connected layers of deep convolutional networks in which neurons respond similarly to mirror-symmetric views (e.g. left and right profiles). Why does this tuning develop? Here, we propose a simple learning-driven explanation for mirror-symmetric viewpoint tuning. We show that mirror-symmetric viewpoint tuning for faces emerges in the fully connected layers of convolutional deep neural networks trained on object recognition tasks, even when the training dataset does not include faces. First, using 3D objects rendered from multiple views as test stimuli, we demonstrate that mirror-symmetric viewpoint tuning in convolutional neural network models is not unique to faces: it emerges for multiple object categories with bilateral symmetry. Second, we show why this invariance emerges in the models. Learning to discriminate among bilaterally symmetric object categories induces reflection-equivariant intermediate representations. AL-like mirror-symmetric tuning is achieved when such equivariant responses are spatially pooled by downstream units with sufficiently large receptive fields. These results explain how mirror-symmetric viewpoint tuning can emerge in neural networks, providing a theory of how they might emerge in the primate brain. Our theory predicts that mirror-symmetric viewpoint tuning can emerge as a consequence of exposure to bilaterally symmetric objects beyond the category of faces, and that it can generalize beyond previously experienced object categories.
A specialized face-processing model inspired by the organization of monkey face patches
explains several face-specific phenomena observed in humans

A. Farzmahdi , K. Rajaei , M. Ghodrati , R. Ebrahimpour , S.M. Khaligh-Razavi — Scientific Reports (2016)

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Converging reports indicate that face images are processed through specialized neural networks in the brain –i.e. face patches in monkeys and the fusiform face area (FFA) in humans. These studies were designed to find out how faces are processed in visual system compared to other objects. Yet, the underlying mechanism of face processing is not completely revealed. Here, we show that a hierarchical computational model, inspired by electrophysiological evidence on face processing in primates, is able to generate representational properties similar to those observed in monkey face patches (posterior, middle and anterior patches). Since the most important goal of sensory neuroscience is linking the neural responses with behavioral outputs, we test whether the proposed model, which is designed to account for neural responses in monkey face patches, is also able to predict well-documented behavioral face phenomena observed in humans. We show that the proposed model satisfies several cognitive face effects such as: composite face effect and the idea of canonical face views. Our model provides insights about the underlying computations that transfer visual information from posterior to anterior face patches.