Dr. Joni Salminen

Postdoctoral researcher
Social Computing

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Research Focus at QCRI

Dr. Salminen works as a postdoctoral researcher at Qatar Computing Research Institute, and Turku School of Economics. His dissertation focused on strategic challenges of platform startups, and is freely available at http://www.doria.fi/handle/10024/99349. Dr. Salminen is currently working on automatic persona generation (APG) from social media data (YouTube, Facebook, Google Analytics), online advertising algorithms, and the societal impact of machine decision-making.
He is also interested in startups and digital marketing platforms.

Previous Experience

Professional Experience

2017-present: Postdoctoral Researcher, QCRI
2015-2017: Postdoctoral Researcher, Turku School of Economics

Professional Associations and Awards


DSc. (Econ.& Bus. Adm.)
Turku School of Economics, Turku (Finland), 2014

Selected Research

Salminen, J.
Startup dilemmas – Strategic problems of early-stage platforms on the Internet. Dissertation, Publications of Turku School of Economics, Series A-12:2014. ISBN: 978-952-249-371-2. 2014

Salminen, J. Kaartemo, V., & Mustak, M.
Network Perspective on the Creation of an Online Platform – A Case Study. IMP Conference, September, 2014, Bordeaux.

Teixeira, J., & Salminen, J.
Open-Source Software Entrepreneurial Business Modelling. In L. Corral, A. Sillitti, G. Succi, J. Vlasenko, & A. I. Wasserman (Eds.), Open Source Software: Mobile Open Source Technologies (pp. 80–82). Springer Berlin Heidelberg. 2014

Salminen, J., Teixeira, J.
Fool’s gold? Developer dilemmas in a closed mobile application market platform. ICEC 2013 The 15th International Conference on Electronic Commerce, August 13–15, 2013, Turku.

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In the Media

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Can AI Put An End To Fake News? Don't Be So Sure


Fake news was the Collin’s word of the year for 2017 with good reason. In a year where politics-as-usual was torn apart at the seams, high-profile scandals rocked our faith in humanity and the ...

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MIT/QCRI system uses machine learning to build road maps


Map apps may have changed our world, but they still haven’t mapped all of it yet. Specifically, mapping roads can be difficult and tedious: even after taking aerial images, companies still have to ...

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Improving disaster response efforts through data


Extreme weather events put the most vulnerable communities at high risk. How can data analytics strengthen early warning systems and and support relief efforts for communities in need? The size and ...

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Upcoming Events

Past Events


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QCRI Summer Internship Program

Download ICS File 06/05/2018  - 05/07/2018 , Hamad Bin Khalifa Research Complex

Each year, Qatar Computing Research Institute organizes a summer internship program for undergraduate students studying computer science, computer engineering and other disciplines. The internship is unpaid, and QCRI does not provide any visa support.

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Public Talk by Prof. Regina Barzilay "Artificial Intelligence for Oncology: Learning to Cure Cancer from Images and Text"

Download ICS File 27/03/2018 ,

Artificial Intelligence for Oncology: Learning to Cure Cancer from Images and Text A talk by Professor Regina Barzilay, MIT CSAIL Winner of 2017 MacArthur ‘genius grant’ At Education City Student ...

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QCRI & MIT-CSAIL Annual Project Review 2018

Download ICS File 27/03/2018 ,

Executive Overview Sessions Open to public Date:    Tuesday, March 27, 2018 Time:    9:00AM – 3:00PM Venue:  HBKU Research Complex Multipurpose Room To view full agenda, please click here . To RSVP, ...

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News Releases

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QCRI to offer kids’ computing activities at this year’s Darb Al Saai


Tech fun and robotics computing activities will be available to children attending the annual family celebration from December 12 to 20.

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Global experts in artificial intelligence for transportation to visit Qatar for TASMU-QCAI workshop


Urban computing experts from Europe, the US and Qatar are to discuss state-of-the-art advances in artificial intelligence for transportation with local stakeholders.

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QCAI to Conduct Joint Machine Learning School with BigML


Two-day crash course to provide hands-on introduction to machine learning for industry practitioners, developers, graduate students and undergraduates.

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