Martin Johnsen

Martin Johnsen

Copenhagen, Capital Region of Denmark, Denmark
2K followers 500+ connections

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About

Co-founder of Subsets. Subsets is the retention automation platform for subscription…

Activity

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Experience

Education

  • Y Combinator Graphic

    Y Combinator

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    Mathematics, machine learning, and signal processing.

    Publication during Master
    Title: Population synthesis for urban resident modeling using deep generative models
    Authors: Martin Johnsen, Oliver Brandt, Sergio Garrido, and Francisco C. Pereira
    Published in: Neural Computing and Applications (NCAA) - link attached

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    Thesis written at the department of Applied Mathematics and Computer Science. The objective was to identify fraudulent Bitcoin transactions using Bayesian machine learning.

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Licenses & Certifications

Volunteer Experience

  • DTU - Technical University of Denmark Graphic

    Co-researcher

    DTU - Technical University of Denmark

    - 1 year 4 months

    Assisting with research within Deep Neural Networks at DTU. The research was focused on population synthesis using deep generative models.

  • SupplyTech Insights Graphic

    Co-Founder & Managing Director

    SupplyTech Insights

    - 1 year 2 months

    Co-founded a conference that become the leading nordic CXO forum within Supply Chain Technology with a team of ten students. We generated 100k+ EUR in revenue within the first year and created profit.

Publications

  • Population synthesis for urban resident modeling using deep generative models

    Neural Computing and Applications (NCAA)

    Abstract:
    The impacts of new real estate developments are strongly associated to its population distribution (types and compositions of households, incomes, social demographics) conditioned on aspects such as dwelling typology, price, location, and floor level. This paper presents a Machine Learning based method to model the population distribution of upcoming developments of new buildings within larger neighborhood/condo settings.
    We use a real data set from Ecopark Township, a real…

    Abstract:
    The impacts of new real estate developments are strongly associated to its population distribution (types and compositions of households, incomes, social demographics) conditioned on aspects such as dwelling typology, price, location, and floor level. This paper presents a Machine Learning based method to model the population distribution of upcoming developments of new buildings within larger neighborhood/condo settings.
    We use a real data set from Ecopark Township, a real estate development project in Hanoi, Vietnam, where we study two machine learning algorithms from the deep generative models literature to create a population of synthetic agents: Conditional Variational Auto-Encoder (CVAE) and Conditional Generative Adversarial Networks (CGAN). A large experimental study was performed, showing that the CVAE outperforms both the empirical distribution, a non-trivial baseline model, and the CGAN in estimating the population distribution of new real estate development projects.

    See publication

Honors & Awards

  • Winner of Hiperfit Blockchain Competition

    Departement of Computer Science University of Copenhagen, IT University Copenhagen and Copenhagen Business School

    Best solution in the corporate category, case competition Blockchain Summer School 2017

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