Vasileios Milias

(Βασίλης Μηλιάς)

Hey! I am Vasilis Milias, I currently live in Rotterdam and I am a PhD candidate in the Urban Analytics Lab at TU Delft advised by Alessandro Bozzon and Achilleas Psyllidis. My work is supported by the Horizon 2020 project "Equal-Life".

In my research, I integrate my Computer Science knowledge with participatory approaches to inform the design of healthy and sustainable living environments. In particular, my interest lies in developing computational methods, tools, and indicators to assess the degree to which cities are accessible, inclusive, sustainable, safe, and attractive for all.


[Oct. 2023]
CTwalk Map is exhibited at the Dutch Design Week 2023. Read HERE.
[Sept. 2023]
Featured in a short news article in Delft Design Stories. Read HERE.
[Sept. 2023]
Lead the workshop on “What Factors Impact Access to Public Spaces and for Whom?“, realized in the City Hall of Helsinki (Finland) with Equal-Life's researchers and stakeholders.
[Aug. 2023]
CTwalk Map featured in #73: La Cultura Del Dato newsletter by Stefano Gatti. Read HERE.
[July 2023]
CTwalk Map featured in Rafagas Links "#2103: cities, biodiversity, minefield". Read HERE.
[June 2023]
Published and presented a paper in the AGILE2023 conference, in Delft, Netherlands. Read HERE.
[June 2023]
Published and presented a paper in the CUPUM2023 conference, in Montreal, Canada. Read HERE.
[June 2023]
CTwalk Map featured in #521: quantum of sollazzo newsletter by puntofisso. Read HERE.
[June 2023]
CTwalk Map featured in the beSpacific blog. Read HERE.
[May 2023]
Lead the workshop on “What Factors Impact Access to Public Spaces and for Whom?“, with the support of the American Planning Association. Read HERE.
[May 2023]
Presented CTwalk Map and how to map walking accessibility of different population groups at the National Program for Sustainable Digital Information Management Demo Day. Read HERE
[April 2023]
Exhibited posters at ICT Open 2023 conference. Download the poster HERE.
[March 2023]
Gave an invited talk at TU Delft’s CityAI Lab on computational methods for assessing accessibility.
[June 2022]
Published a journal article in Computers, Environment and Urban Systems. Read HERE.
[March 2021]
Published a journal article in Computers, Environment and Urban Systems. Read HERE.

Short CV

Work Experience

> 2020 - 2024: PhD Candidate - Urban Data Science (TU Delft, Netherlands)
> 2019 - 2020: Spatial Data Specialist (HERE Technologies, Netherlands)
> Sept.-Feb. 2018: R&D Intern (Philips Lighting, Netherlands)
> April-June 2015: Project assistant (MIT, U.S.)


> 2016-2018: MSc in Computer Science (TU Delft, Netherlands)
> 2010-2016: BSc & MSc in Electrical and Computer Engineering (NTUA, Greece)

My top picks

> Python
> PostgreSQL
> QGIs
> Javascript (only when needed)


(as a comic)

For more chapters feel free to contact me :)

Relevant Projects

Here are some of the projects I have thoroughly enjoyed working on during the past years.
CTstreets Map

Interactive Map
Focus: Walkability

CTstreets Map is an interactive web tool that seeks to highlight how walkable are the streets of Amsterdam. It uses granular population, location, and pedestrian network data from open sources to estimate how walkable each street is. It has been designed through a participatory approach involving urban experts who live and work in Amsterdam. It estimates the degree of walkability for the 5 and 15-minute walking areas from each housing block in Amsterdam, revealing neighborhoods that encourage walking and exposing disparities in walkability.

CTwalk Map

Interactive Web Map
Mapping Pedestrian (co)Accessibility

CTwalk Map is an interactive web tool that seeks to highlight the social cohesion potential of neighbourhoods while unmasking local access. Utilizing population, location, and pedestrian network data from open sources, it estimates the number of individuals from different age groups who can reach city destinations within a 5 or 15-minute walk, highlighting opportunities for social cohesion and access inequities.

Transparency is a key feature of CTwalk Map. It openly acknowledges limitations in the available data, encouraging further data collection and refinement. Users navigate the map through a user-friendly interface and explore different destinations. They are encouraged to identify anomalies in the map and scrutinize the completeness of the used data sources.

Furthermore, it serves an educational purpose by enhancing public understanding of the relationship between AI, urban design, accessibility, and social cohesion. It simplifies complex concepts and provides in-depth insights into how street networks shape accessibility, empowering citizens to actively participate in urban planning discussions.


Python Library
Focus: Accessibility &
Street Centrality Measures

A Python library for calculating various street network centrality and accessibility metrics, as well as the size and layout of surrounding pedestrian and bicucly infrastructure.


Python Library
Measuring pedestrian (co)accessibility

A Python library for calculating prospective social encounters between persons from different demographic groups


Python and Javascript toolkit
Capturing perceived qualities along city streets

A Python and Javascript toolkit for calculating perceived safety and attractiveness of city streets through crowd parcipation.

Social Distancing Dashboard

Interactive Web Map
How could our cities facilitate social distancing?

The Social Distancing Dashboard offers interactive city maps that show at street and neighborhood level whether people moving through public space can keep to the distance rules. It provides an overview of various aspects – such as the width of footpaths and the location of bus stops – that influence the extent to which we can actually comply with the distance rules. In this way, it can contribute to raising awareness of the limitations that the design of public space entails and to decision-making about COVID-19-related interventions in the city.

Equal Life

Horizon 2020 Project
Studying the exposome for a healthier future for all children

The EU-funded Equal-Life project will develop and utilise the exposome concept in an integrated study of the external exposome and its social aspects and of measurable internal physiological factors and link those to a child's development and life course mental health. This will be done using a novel approach combining exposure data to characterise, measure, model and understand influences at different developmental stages. The goal is to propose the best supportive environments for all children.

And here are some less relevant to my research but very fun projects I have also been a part of.

Making data trails tangible
A project led by Alejandra Gomez

As we navigate the physical and digital world, we unknowingly leave behind an immense trail of data. We are informed about this via short statements (e.g., cookie popups) or lengthy documents (e.g., privacy policies). However, even when we know that data is collected, we remain largely unaware of its nature. Dataslip is an interactive installation where the abstract notion of personal data is translated into a material and tangible representation in the form of a receipt. The receipt contains detailed information and illustrative examples of the data generated from our interactions with five different categories of products and services. Its length is proportional to the amount of data collected about us.

Kaggle's Competition

IEEE's Signal Processing Society
Camera Model Identification

Silver Medal - Top 8%

The goal of this competition was to identify from which camera models, images were taken. My team’s solution was based on deep learning techniques using Python (Keras library).

Sentimental Airbnb

Fun project during my MSc studies
TU Delft

Collected data from Airbnb and Twitter and made a visualization that shows color-coded clusters of areas with similar prices, the sentiment of the tweets that have been sent in these areas and a wordcloud containing the word-of-mouth as represented by these tweets.



> Urban Analytics - Lectures/Tutorials, Assignments (MSc, IDE - TU Delft)
> Machine Learning for Design - Lectures/Tutorials, Assignments (BSc, IDE - TU Delft)
> Urban Data Science, Lectures/Tutorials, Assignments (BSc, IDE - TU Delft)
> AI for Society - Guest Lecture (MSc, IDE - TU Delft)
> MOOC for H2020 project "Periscope"
> Contributed to Advanced Machine Learning for Design (MSc, IDE - TU Delft)
> Moderator in Software Based Products (BSc, IDE - TU Delft)

Supervised MSc Students


(* denotes equal contribution)

> Milias, V., Sharifi Noorian, S., Bozzon, A., & Psyllidis, A. (2023). Is it safe to be attractive? Disentangling the influence of streetscape features on the perceived safety and attractiveness of city streets. AGILE: GIScience Series, 4, 8.

> Van Asten, T.*, Milias, V.*, Bozzon, A., & Psyllidis, A. (2023). “Eyes on the Street”: Estimating Natural Surveillance Along Amsterdam’s City Streets Using Street-Level Imagery. In International Conference on Computers in Urban Planning and Urban Management (pp. 215-229). Cham: Springer Nature Switzerland.

> Milias, V., & Psyllidis, A. (2022). Measuring spatial age segregation through the lens of co-accessibility to urban activities. Computers, Environment and Urban Systems, 95, 101829.

> Milias, V., & Psyllidis, A. (2021). Assessing the influence of point-of-interest features on the classification of place categories. Computers, Environment and Urban Systems, 86, 101597.

For a full list of my publications check my Google Scholar page


> Email:

> LinkedIn: vmilias

> Github: MiliasV

> TU Delft Page: milias-v

If you like the comic-like portrait contact Tehzeeb