Afdal Tawassul

The Challenge

The best humanitarian projects help people in a way that is empowering for the assisted community. Those development projects have long-lasting effect, and their target group takes over the management and ownership of the project. Often, immediate and long-term feedback is gathered only through officialized channels like surveys and group discussions organised by NGOs and international organisations. Those might miss who are the worst affected by crises, in what precise ways, and how future response can better target the most vulnerable groups.


The goal of this team is to deepdive in the possibilities of technology and data science to reach a large sample of the population and collect the entire scope of meaningful information for the improvement of interventions. One way of exploring is predictive analytics, which can be used to improve feedback mechanisms, and on a broader humanitarian scope also to predict crises, and perhaps even the evolution of crises.

The possibilities include development of software, collaboration with existing and widely used platforms or use of open-source data. Those have the potential of enabling the development of better, more effective data collection systems, for example immediately following a crisis or a humanitarian project, enabling affected people to communicate their needs more precisely and more effectively, and humanitarian organisations to use the data to create a better, more targeted response.

Problem Statement

The Afdal Tawassul team will create an automated feedback mechanism primarily for Internally displaced people in Yemen that are either currently receiving assistance from UNHCR and their partners including World Food Programme, or those currently outside of the UN data base, but still requiring assistance. The system we propose will rely on 2G mobile network coverage. It will primarily be in Arabic and SMS based since smartphones are a luxury in some areas. Although time permitting an audio extension may be investigated. We build a free or very cheap system and user friendly, since the coexistence of different dialects, as well as illiteracy could be major challenges for its users. The future system could ideally receive feedback from people outside of the UN data base.

Solution Statement

The team has been exploring software solutions for an automated SMS based survey system. We have decided to base the solution on the Django web framework. Twilio will be used as the communication interface to allow automated sending and receiving SMS surveys. Survey responses will be stored in a database, and the data will be made accessible through an API or simple user interface. Survey questions will be designed in Arabic with a view to making the responses structured and suitable for analysis. Time permitting, data visualisation and analysis options may be explored. To get a working product this solution will initially be set up for Switzerland, but the team will explore the costs and logistics of extending this to Yemen and other countries not supported by Twilio.


Project Team

Alex Howes

Alex is a data scientist at ACAPS where she analyses data to provide insight on humanitarian crises around the world, and develops software to make data more accessible and impactful in the humanitarian industry. Prior to joining ACAPS she worked on a number of data science projects including for Twitter, Fermilab, CERN, in agricultural technology, and in digital marketing. She holds a Master’s degree in Physics from the University of Cambridge.

Anca Muntenau

I am a researcher from France (Grenoble). I finished my PhD in 2019 at the Grenoble University, Department of Law. Since then, I have been working for the CNRS in Paris. My research focuses on North Africa’s regime change after 2011: religious movements’ integration, civil society building, gender and human rights. I am currently looking for a more dynamic and applied environment, so I am aspiring to join an NGO or an international organization.

Ben Krikler

Ben is a particle physicist working on the CMS (CERN) and LUX-ZEPLIN (SURF, USA) experiments to search for Dark Matter. Ben graduated in Physics from Imperial College London and (after a brief stint learning to motorcycle in Asia) started his PhD in Particle Physics on the COMET experiment in Japan. Ben is an expert in large-scale distributed simulations, data analysis, machine learning, and general software development as a Fellow of the UK’s Software Sustainability Institute.

Christopher Alagna

TEAM COACH - Christopher started his career as an RF engineer in Australia before becoming a humanitarian and joining the World Food Program in 2011. Since then Christopher has worked in emergency telecommunications coordination, with missions to Pakistan, Afghanistan, Yemen, Syria, Libya, Tunisia, Guinea, Uganda, South Sudan, Cambodia, and Haiti. Chris is now supporting Medecins Sans Frontières (MSF) as a program manager, upgrading the IT field infrastructure.

Eszter Badinova

Ibrahim Sekayi

Mechanical engineer with interest in renewable energy, self-motivated, eloquent with excellent communication, analytical, professionalism, risk management, presentation skills, well-honed problem solving skills based on logical methodologies, result oriented with deadline and quality focus, ability to work and interact with all levels of humanity, well developed capacity in team work, well developed intelligence, and has initiated projects right from conception to successful completion.

Ilaria Luise

Ilaria is a Research Associate at the State University of New York at Stony Brook (US). She works at CERN as a High Energy Physicist for the ATLAS experiment. In particular, she is involved since many years in searches for the Higgs boson decay into complex states containing fermions. She completed her PhD in High Energy physics in 2019 at Sorbonne Université in Paris, and she spent most of her sabbatic period as volunteer for a NGO in Colombia.

Ryan Li Hong Liew

Li Hong graduated from University of Cambridge, with a Master degree in Information and Computer Engineering, was selected to the CERN openlab Summer Student Programme 2020. He is passionate about data analytics, machine learning and scalable technology, particularly about their application to the healthcare, education or agriculture industry. He is actively seeking experience in solving social challenges using technology. He loves to bring people together and has led multiple student organisations and worked in 4 countries. In his free time, he enjoys exercising, reading and adventuring in the nature.

Valerio Rossetti

Dr. Valerio Rossetti worked for 8 years on the ATLAS experiment at CERN, where he contributed to the search for the possible production of dark matter in particle collision data. After a year as quantitative consultant in EY, he co-founded the startup SamurAI, based in Geneva. He develops solutions using artificial intelligence, automations and data analysis, for a variety of industries. He also organises training programs for professionals who want to boost their quantitative skills.


Afdal Tawassul