Meenakshi Khosla

Meenakshi Khosla

Postdoc at the McGovern Institute for Brain Research


Hey there!

I am a postdoc at the McGovern Institute for Brain Research at MIT, working with Nancy Kanwisher. I use techniques from artificial intelligence and functional imaging to understand how the brain processes the external world. I am most interested in challenges at the intersection of neuroscience and large-scale data analysis. My postdoctoral research focuses on developing interpretable machine learning tools to understand structured neural representations in the human visual cortex and deep neural networks. During my PhD, I also worked more broadly at the intersection of machine learning and neuroimaging, developing predictive models to understand the distinctive characteristics of the brains of people affected with different mental disorders.

[CV][Research statement]


  • Computational Neuroscience
  • Artificial Intelligence
  • Functional neuroimaging


  • PhD in Electrical and Computer Engineering, 2017-2021

    Cornell University

  • B. Tech - M. Tech dual degree in Electrical Engineering, 2011-2016

    Indian Institute of Technology, Kanpur, India

Recent news

  • [Oct'22] Pleasure to participate in a fun discussion on food-selective neural responses at the Quantum Photonics Clubhouse! [Listen here]!
  • [Oct'22] Talked about ‘Food on the brain’ at the Cambridge Science Festival, MIT Museum
  • [Sep'22] Paper on ‘Characterizing the Ventral Visual Stream with Response-Optimized Neural Encoding Models’ accepted at NeurIPS'22!
  • [Aug'22] ‘A highly selective response to food in human visual cortex revealed by hypothesis-free voxel decomposition’ accepted to Current Biology! paper. Featured at The Guardian!
  • [Aug'22] Presented our work on food-selectivity in the ventral visual cortex at CCN! [paper]
  • [May'22] Gave an oral presentation on our work entitled ‘Data-driven component modeling reveals the functional organization of high-level visual cortex’ at VSS!
  • [Mar'22] Presented our work entitled ‘Hypothesis-neutral models of higher-order visual cortex reveal strong semantic selectivity’ at Cosyne! [Abstract]
  • [Dec'21] Shared our work on `emergent semantic selectivity in hypothesis-neural response-optimized models of high-level visual cortex’ in an oral presentation at Neuromatch 4.0
  • [Sep'21] Started a postdoctoral position at the McGovern Institute for Brain Research, working with Nancy Kanwisher
  • [Jul'21] Defended my thesis!
  • [Apr'21] Gave a talk at the Biomedical Image Computing series, ETH Zurich on ‘Predicting cortical responses to naturalistic stimuli using deep learning’
  • [Mar'21] Work on cortical response prediction to multi-modal naturalistic stimuli accepted at Science Advances
  • [Mar'21] Presented my latest work at the Voxel Talk seminar series
  • [Dec'20] Work on neural encoding with visual attention accepted to NeurIPS 2020 as an oral. Here’s a video of the talk.
  • [Oct'20] Presented our work on shared neural encoding models for subject-specific response prediction at MICCAI 2020! Here’s a video of the talk.
  • [Jul'20] Co-led a breakout session on “Machine Learning for Neuroimaging” with Elvisha Dhamala and Carmen Khoo in the Women in Machine Learning un-workshop @ ICML'20.
  • [Jun'20] Presented our research poster on “holistic neural encoding with multi-modal naturalistic stimuli” at OHBM 2020. The poster and a quick video walkthrough is also available at

Short Projects

Machine learning methods for seizure detection [report]

This project was implemented as part of ECE5040 under the guidance of Prof. Mahsa Shoaran.
Here, we explored the utility of various feature sets extracted form intracranial EEG recordings (time-domain and frequency-domain) as well as different machine learning algorithms for automated seizure detection.

A graph-based approach to estimate mutual information[report]

This project was implemented as part of ECE6970 under the guidance of Prof. Ziv Goldfeld.
Here, we presented a novel approach to estimate mutual information between input data and internal representations of a neural network that relies solely on the neighborhood graph of these representations.

Prediction of longitudinal evolution of Alzheimer’s Disease [report]

This project was implemented as part of ECE5970 under the guidance of Prof. Mert Sabuncu.
Here, we implemented several algorithms to predict future disease states and clinical scores of patients from multi-modal imaging data, including functional principal component analysis, linear and non-linear mixed effect models and random forests.

Bayesian nonparametric extensions of Hidden Markov Models [report]

This project was implemented as part of ORIE6780 under the guidance of Prof. David Ruppert.
Here, we reviewed bayesian nonparametric models for time-series data and discussed the evolution of their inference algorithms.