About
I am a CBS-NTT Physics of Intelligence fellow in the Center for Brain Science at Harvard University. I am interested in various problems at the intersection of Neuroscience and Artificial Intelligence, and specifically how these two fields can benefit each other.
I received my PhD from the Johns Hopkins University, and previously was an Instructor in Medicine at the Harvard Medical School, and a Swartz Fellow in Theoretical Neuroscience at the Cold Spring Harbor Laboratory in New York. Prior to this, I conducted physics-inspired imaging and modeling research focused on understanding structure and dynamics in the heart. To learn more about some of past and current projects please see below, and my Google Scholar.
Selected Research
Neuroscience and Machine Learning
Data symmetries generate drifting similarity matrices in manifold-tiling neural codes Farhad Pashakhanloo, and Jacob Zavatone-Veth, Unifying Representations in Neural Models (UniReps) Workshop, NeurIPS 2025.
Contribution of task-irrelevant stimuli to drift of neural representations Farhad Pashakhanloo, Advances in Neural Information Processing Systems (NeurIPS) 39 (2025).
Convergent motifs of early olfactory processing are recapitulated by layer-wise efficient coding Juan Carlos Fernández del Castillo, Farhad Pashakhanloo, Venkatesh N. Murthy, Jacob A. Zavatone-Veth, biorRxiv, 2025
Perception and neural representation of intermittent odor stimuli in mice Luis Boero, Hao Wu, Joseph D. Zak, Paul Masset, Farhad Pashakhanloo, Siddharth Jayakumar, Bahareh Tolooshams, Demba Ba, Venkatesh N. Murthy, Biorxiv, 2025.
SGD-Induced Drift of Representation in a Two-Layer Neural Network Farhad Pashakhanloo, and Alexei Koulakov Proceedings of the 40th International Conference on Machine Learning, PMLR 202:27401-27419, 2023.
Imaging and Biophysical Modeling
Minimal Functional Clusters Predict the Probability of Reentry in Cardiac Fibrotic Tissue Farhad Pashakhanloo, and Alexander V. Panfilov, Physical Review Letters 127.9 (2021): 098101.
This work was featured on the cover of Physical Review Letters in Aug 2021.
Myofiber Architecture of the Human Atria as Revealed by Submillimeter Diffusion Tensor Imaging Farhad Pashakhanloo, Daniel A. Herzka, Hiroshi Ashikaga, Susumu Mori, Neville Gai, David A. Bluemke, Natalia A. Trayanova, and Elliot R. McVeigh Circulation: arrhythmia and electrophysiology 9.4 (2016): e004133.
Whole-heart Fiber Tractography (cover of Nature Reviews Cardiology) The picture shows detailed fibre tractography of the whole heart from a patient with atrial fibrillation. The image is reconstructed from in-vitro high-resolution diffusion tensor MRI obtained over 60 h of scan time. The tracts follow the local fibre orientation and reveal the myofibre architecture in both the atria and the ventricles.
Submillimeter diffusion tensor imaging and late gadolinium enhancement cardiovascular magnetic resonance of chronic myocardial infarction Farhad Pashakhanloo, Daniel A. Herzka, Susumu Mori, Muz Zviman, Henry Halperin, Neville Gai, David A. Bluemke, Natalia A. Trayanova, and Elliot R. McVeigh Journal of Cardiovascular Magnetic Resonance (2016).
Role of 3-Dimensional Architecture of Scar and Surviving Tissue in Ventricular Tachycardia: Insights From High-Resolution Ex Vivo Porcine Models Farhad Pashakhanloo, Daniel A. Herzka, Henry Halperin, Elliot R. McVeigh, and Natalia A. Trayanova Circulation: arrhythmia and electrophysiology 9.4 (2018).
Machine learning phenotyping of scarred myocardium from cine in hypertrophic cardiomyopathy Jennifer Mancio, Farhad Pashakhanloo, Hossam El-Rewaidy, Jihye Jang, Gargi Joshi, Ibolya Csecs, Long Ngo, Ethan Rowin, Warren Manning, Martin Maron, Reza Nezafat. European Heart Journal-Cardiovascular Imaging (2021).
Multi-domain convolutional neural network (MD-CNN) for radial reconstruction of dynamic cardiac MRI Hossam El-Rewaidy, Ahmed S. Fahmy, Farhad Pashakhanloo, Xiaoying Cai, Selcuk Kucukseymen, Ibolya Csecs, Ulf Neisius, Hassan Haji-Valizadeh, Bjoern Menze, Reza Nezafat. Magnetic Resonance in Medicine (2021)