Meenakshi Khosla

Meenakshi Khosla

PhD student in Electrical and Computer Engineering

Cornell University

Hey there!

I am a PhD candidate in ECE at Cornell University working with Mert Sabuncu and Amy Kuceyeski. I use techniques from artificial intelligence and functional imaging to understand how the brain works. To that end, I’m interested in the development of computational models that can accurately predict how the brain responds under naturalistic stimulation. By modelling the computations underlying sensory processing ‘in the wild’, a critical focus of my research is to understand the function of different brain areas and how they collectively support complex human behavior. I also have more general interests across machine learning and neuroimaging, particularly in the use of predictive models to understand the distinctive characteristics of the brains of people affected with different mental disorders. The ultimate goal of this work is to develop novel diagnostics of neuropsychiatric diseases, and to harness the improved understanding of the brain and mental disorders to inform treatment and design personalized therapeutics. [CV][Research statement]


  • Neuroimaging
  • Artificial Intelligence
  • Computational Neuroscience


  • PhD in Electrical and Computer Engineering, 2017-Present

    Cornell University

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

    Indian Institute of Technology, Kanpur, India

Recent news

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.