Gamaleldin Elsayed Personal Website
Gamaleldin Fathy Elsayed

Gamaleldin Fathy Elsayed.

I am a Ph.D. candidate in the Department of Neuroscience at Columbia University in the lab of John P. Cunningham, where I work on problems in computational neuroscience and machine learning. I am generally interested in identifying and validating structures from complex signals such as those recorded from neural systems. In my research, I develop machine learning and statistical methods and apply them on data recorded from different neural systems.
I completed my B.S. from the American University in Cairo (AUC) with a major in Electronics Engineering and a minor in Computer Science. After my B.S., I joined the founding class of King Abdullah University of Science and Technology (KAUST), where I pursued my M.S. in Electrical Engineering under the supervision of Jürgen Kosel and studied EKG signals to extract respiratory-related information. In 2011, I moved to the US to pursue my PhD in Electrical Engineering at Washington University in Saint Louis, where I started to develop my research interest for computational neuroscience. In 2013, I transferred to Columbia University PhD program, where I joined the Center for Theoretical Neuroscience.

Contact: gfa2109 at cumc dot columbia dot edu
Office: Kolb 718

Learn about what I do

Research

My main research interest is to elucidate the computational mechanism of neural systems by enhancing methods in the following areas: population data analysis, statistical testing and modeling.
I am interested in developing novel machine learning methods to explore neural population structures. One important aspect of data analysis is to explore population responses searching for possibly hidden structures that may be consistent with a proposed hypothesis about a neural mechanism. Traditionally, methods that are not necessarily guided by hypotheses were used, leading to suboptimal solutions or missing important structures. Thus, I am interested in developing hypotheses-driven methods for identifying structures in population data, which yields optimal projections of population data that is consistent with proposed hypotheses.
With big datasets, a main challenge is that it becomes harder to judge whether an observed structure is fundamental or epiphenomenon because it becomes easier to find structures by chance. For example, one may find neural dimensions with structures supporting mostly any hypothesis from random data in space with high enough dimensions. Thus, as datasets get larger, the question becomes whether an observed population structure profound rather than can we find a specific structure in data. Thus, I am interested in designing statistical methods to validate population structures.
Given that a population structure is profound, then it will give insight regarding the mechanism underlying a neural computation in a certain brain area. Since the ultimate goal in systems neuroscience is to identify neural mechanisms, I am interested in modeling potential neural mechanisms that are able to produce realistic single neuron responses while preserving all known fundamental population structures. In particular, I am interested in dynamical systems models including recurrent network architectures.
Beyond my interest in neuroscience problems, I have also interests in topics from machine learning including inference and deep learning.

Project 1

Single neuron responses are highly complex yet are able to flexibly perform different computations underlying different behaviors through their collective activity. Here I study structure of preparatory and movement computations in motor cortex. I demonstrate that collective activity patterns of motor cortical neurons are orthogonal during successive task epochs yet are linearly linked, indicating a degree of flexibility that was not displayed or predicted by existing cortical models. These results reveal a population-level strategy for performing separate but linked computations.

Project 2

Collective patterns of neural responses are increasingly studied from various neural systems. These population structures give concise pictures of the activity of populations beyond the complexity of single-neurons, and are relied upon to support various hypotheses about neural mechanisms. However, the increasing use of population structures without proper validation pose serious problems that could undermine the conclusions drawn from population data. In particular, we should be genuinely concerned that population findings may be outcomes of a powerful data analysis method rather than a fundamental structure as any identified population structure may be a trivial consequence of primary features we already know exist in data. In this work, I use ideas from Fisher randomization, optimization and maximum entropy to design novel methods that to generate random surrogate data while preserving the data primary features, thus overcoming the shortcomings of previous validation methods. With this method, one can perform statistical tests, and be confident that epiphenomenal structures can be identified or ruled out, providing a general-purpose platform for validating population structures for the whole scientific community.

Other projects

I am also involved in other research problems in machine learning, motor control, and decision making.

Publications

Journal Papers

Conference Papers and Abstracts

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