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.



GIT: New pre-print: Nerve theorems for fixed points of neural networks

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GIT: External Faculty of the Vermont Complex Systems Center

    I am so excited to tell the world that I am an inaugural member of the Vermont Complex Systems Center External Faculty. I am humbled to be part of this inspiring group of researchers that I have admired for so long! I mean seriously! Look at the company I have: VCSC External Faculty .
    Some of them I have known for a while and others I have been adolizing from afar. They are all super-nice people and great scientists and I can't wait for the opportunity to spend some time talking science with them. The future is looking bright!

    And did you see their cute mascots?

GIT: I coded a thing, if you use GoogleScholar you might find it useful too

    I have always had trouble navigating the huge amount of new papers, pre-prints, and tweets that come out every day. I always felt like I was the last one to know about other people’s research until I went to a conference or stumbled upon it during the literature research for a new paper. I still haven’t figured out Twitter, but I did figure out how to have a list of new papers and pre-prints as they come out. It might not be the smartest way, but last year I coded a small script to compactify all the Google Scholar alerts I subscribe to.

GIT: Social Network Analysis for the network scientist

    Since I moved to IUNI as an Assistant Research Scientist, I have been struggling to communicate in a precise scientific language with network scientist in Sociology. The main problem being that the same terms I have learned from network scientists in Informatics do not always mean exactly the same thing in the two disciplines.
    So here I am going to try and put together a list of resources for Informatics/Physics trained network scientist to better navigate the long history of Social Network Analysis in Sociology.

GIT: Layered DAG drawing - list of useful reading

GIT: New paper in Network Neuroscience

    New work out, together with collaborators at King’s College London, Imperial College London and ISI Foundation.
    We applied the Mapper algorithm to the AHBA (Allen Human Brain Atlas). Our results suggest that topological network descriptions can be a powerful tool to explore the relationships between genetic pathways and their association with brain function and its perturbation due to illness and/or pharmacological challenge.



    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.



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: 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: 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.