GIT: Page Not Found
Page not found. Your pixels are in another canvas.
Sitemap
A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
GIT: About me
About me
Posts
code
S.A.S.S.A.F.R.A.S.
A small script that looks for unread Google Scholar Alerts emails in your Gmail account and saves each paper in a Google Spreadsheet as:
Title/ Authors - Journal/ Google Scholar link/ Date/ number of Alerts that contained the paper
TDA summary for gene-expression: Mapper Algorithm in 2D
Code for reproducing results in the paper "Topological gene-expression networks recapitulate brain anatomy and function".
We present a pipeline based on the Mapper algorithm, a topological simplification tool, to produce and analyze genes co-expression data.
The code contains an implementation of the Mapper algorithm with 2 filters and to compute the agreement edge density matrices for the optimal parameters.
pre-prints
publications
GIT: P-persistent homology of finite topological spaces
We show that for any P-persistent object X in the category of finite topological spaces, there is a P− weighted graph whose clique complex has the same P-persistent homology as X.
GIT: Complex politics: A quantitative semantic and topological analysis of uk house of commons debates.
An exploratory attempt to use quantitative semantics techniques and topological analysis to analyze systemic patterns arising in a complex political system.
GIT: Construction of and efficient sampling from the simplicial configuration model
We propose the simplicial configuration model and use it to build null models to investigate the topology of real systems.
GIT: Navigating features: a topologically informed chart of electromyographic features space.
The success of biological signal pattern recognition depends crucially on theselection of relevant features. Across signal and imaging modalities, a largenumber of features have been proposed, leading to feature redundancy and the need for optimal feature set identification. A further complication is that,due to the inherent biological variability, even the same classification problemon different datasets can display variations in the respective optimal sets,casting doubts on the generalizability of relevant features. Here, we approachthis problem by leveraging topological tools to create charts of features spaces.These charts highlight feature sub-groups that encode similar information(and their respective similarities) allowing for a principled and interpretablechoice of features for classification and analysis. Using multiple electro-myographic (EMG) datasets as a case study, we use this feature chartto identify functional groups among 58 state-of-the-art EMG features, and toshow that they generalize across three different forearm EMG datasets obtained from able-bodied subjects during hand and finger contractions.We find that these groups describe meaningful non-redundant information,succinctly recapitulating information about different regions of featurespace. We then recommend representative features from each group basedon maximum class separability, robustness and minimum complexity.
GIT: Topological analysis of data
An editorial where we briefly present the TDA paradigm and some applications, in order to highlight its relevance to the data science community.
GIT: The shape of collaborations.
A study on the structure of scientific collaborations using simplicial descriptions of publications. We extend the concept of triadic closure to simplicial complexes and introduce a new way of dealing with large simplex sizes when computing homology.
GIT: Topological gene-expression networks recapitulate brain anatomy and function.
We present a pipeline based on Mapper to analyze genes co-expression data. We find that co-expression networks produced by Mapper returned a structure that matches the well-known anatomy of the dopaminergic pathway.
GIT: Topological analysis of differential effects of ketamine and propofol anaesthesia on brain dynamics
Topological data analysis reveals distinct effects of ketamine and propofol on brain network dynamics during anesthesia.
GIT: Topological learning for motion data via mixed coordinates
Novel topological approach for analyzing and learning from motion data using mixed coordinate representations.
GIT: Nerve theorems for fixed points of neural networks
Application of nerve theorems from algebraic topology to analyze fixed points and dynamics in neural network systems.
GIT: XGI: A Python package for higher-order interaction networks
Comprehensive Python library for analyzing higher-order interaction networks including hypergraphs and simplicial complexes.
GIT: Exact and rapid linear clustering of networks with dynamic programming
Efficient dynamic programming approach for exact network clustering with guaranteed optimality in linear time.
GIT: Integrative Analysis of Amyloid Imaging and Genetics Reveals Subtypes of Alzheimer Progression in Early Stage
Identification of Alzheimer’s disease subtypes through integrative analysis of imaging and genetic markers.
GIT: Cluster Synchronization via Graph Laplacian Eigenvectors
Novel approach to cluster synchronization using spectral graph theory and Laplacian eigenvectors.
GIT: The topology of synergy: linking topological and information-theoretic approaches to higher-order interactions in complex systems
Bridging topological and information-theoretic approaches to understand higher-order interactions and synergy in complex systems.
talks
teaching
GIT: Algebraic Topology - MATH 354
The course is an intrduction to basic Algebraic Topology concepts, like homotopy, homology and cohomology. We will look into application to Data Science at the end of the semester.
GIT: Topology - MATH 254
Introductory course on point set topology.
GIT: Data Science I - STAT 287
Combining data, computation, and inferential thinking, data science is redefining how people and organizations solve challenging problems and understand their world. Modeled on Berkely’s Data 100 course, this course introduces basic concepts in data science.
In this class, we explore key areas of data science including question formulation, data collection and cleaning, visualization, statistical inference, predictive modeling, and decision making. Through a strong emphasizes on data centric computing, quantitative critical thinking, and exploratory data analysis this class covers key principles and techniques of data science. These include languages for transforming, querying and analyzing data; algorithms for machine learning methods including regression, classification and clustering; principles behind creating informative data visualizations; statistical concepts of measurement error and prediction; and techniques for scalable data processing.