Ekdeep Singh Lubana

I am a PhD candidate in EECS Department at the University of Michigan, advised by Robert Dick. I am also affiliated with Harvard Center for Brain Science, where I am mentored by Hidenori Tanaka.

I am generally interested in designing theoretically motivated, grounded algorithms for practical applications of DNNs. I am also very interested in better understanding training dynamics of neural networks, especially via a statistical physics perspective.

I graduated with a Bachelor's degree in ECE from Indian Institute of Technology (IIT), Roorkee in 2019. My research in undergraduate was primarily focused on embedded systems, such as energy-efficient machine vision systems.

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We show models that rely on entirely different mechanisms for making their predictions can exhibit mode connectivity, but generally the ones that are mechanistically similar are linearly connected.

We present a highly detailed analysis of the landscape of several self-supervised learning objectives to clarify the role of representational collapse.

We propose a theoretical framework that demonstrates limitations of popular graph augmentation strategies for self-supervised learning.

We propose an unsupervised learning method that exploits client heterogeneity to enable privacy preserving, SOTA performance unsupervised federated learning.

We develop a general theory to understand the role of normalization layers in improving training dynamics of a neural network at initialization.

This work demonstrates how quadratic regularization methods for preventing catastrophic forgetting in deep networks rely on a simple heuristic under-the-hood: Interpolation.

A unified, theoretically-grounded framework for network pruning that helps justify often used heuristics in the field.

An image signal processing pipeline that allows use of out-of-the-box deep neural networks on RAW images directly retrieved from image sensors.

An energy-efficient machine vision framework inspired by the concept of Fovea in biological vision. Also see follow-up work presented at CVPR workshop, 2020.

An efficient imaging system that accurately calculates chlorophyll content in leaves by using RAW images.

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