About me

Julien Cloarec | Ph.D. | M.Eng.

management Science

I am an expert in Privacy and Artificial Intelligence applied to Marketing, Information Systems and Innovation

computer science

My expertise benefits from my former experience as a Software Engineer in the industrial and service sectors


As an instructor, my goal is to make Data Science as accessible as possible to all


I am a seasoned trainer for all kinds of audience (beginner, advanced)

Julien Cloarec is an 𝗔𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝘁 𝗣𝗿𝗼𝗳𝗲𝘀𝘀𝗼𝗿 𝗼𝗳 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 at iaelyon School of Management, Université Jean Moulin Lyon 3. He holds an M.Eng. in Computer Science, as well as a Ph.D. in Management Science, for which he was awarded the 𝗕𝗲𝘀𝘁 𝗧𝗵𝗲𝘀𝗶𝘀 𝗣𝗿𝗶𝘇𝗲 by the French Foundation for Management Education and a 𝗦𝗽𝗲𝗰𝗶𝗮𝗹 𝗗𝗶𝘀𝘁𝗶𝗻𝗰𝘁𝗶𝗼𝗻 by the French Marketing Association. After defending his Ph.D. at Université Toulouse Capitole, he was a researcher at the Center for Research in Information Technology and Business at Université Laval, in Canada. As an academic, he seeks to offer insights pertaining to 𝗰𝗼𝗻𝘀𝘂𝗺𝗲𝗿 𝗽𝗿𝗶𝘃𝗮𝗰𝘆 𝗮𝗻𝗱 𝗻𝗲𝘄 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝗶𝗲𝘀. His research was 𝗽𝘂𝗯𝗹𝗶𝘀𝗵𝗲𝗱 𝗶𝗻 𝘀𝗲𝘃𝗲𝗿𝗮𝗹 𝗷𝗼𝘂𝗿𝗻𝗮𝗹𝘀, such as 𝘗𝘴𝘺𝘤𝘩𝘰𝘭𝘰𝘨𝘺 & 𝘔𝘢𝘳𝘬𝘦𝘵𝘪𝘯𝘨, 𝘛𝘦𝘤𝘩𝘯𝘰𝘷𝘢𝘵𝘪𝘰𝘯, and 𝘛𝘦𝘤𝘩𝘯𝘰𝘭𝘰𝘨𝘪𝘤𝘢𝘭 𝘍𝘰𝘳𝘦𝘤𝘢𝘴𝘵𝘪𝘯𝘨 𝘢𝘯𝘥 𝘚𝘰𝘤𝘪𝘢𝘭 𝘊𝘩𝘢𝘯𝘨𝘦. He 𝗰𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗲𝗱 𝘄𝗶𝘁𝗵 𝘀𝗲𝘃𝗲𝗿𝗮𝗹 𝗶𝗻𝘀𝘁𝗶𝘁𝘂𝘁𝗶𝗼𝗻𝘀 𝘄𝗼𝗿𝗹𝗱𝘄𝗶𝗱𝗲: Temple University (USA), Karlsruhe Institute of Technology (Germany), DHBW Stuttgart (Germany), Universidad de la Sabana (Colombia), Universidad de la Salle (Colombia), Business Science Institute (Luxemburg), Toulouse Business School (France), and Institut Mines-Télécom Business School (France). He also 𝗿𝗲𝗰𝗲𝗶𝘃𝗲𝗱 𝗳𝘂𝗻𝗱𝗶𝗻𝗴 𝗳𝗿𝗼𝗺 𝘀𝗲𝘃𝗲𝗿𝗮𝗹 𝗼𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝘀: Association for Consumer Research, European Cooperation in Science and Technology, and the French Foundation for Management Education.


As a researcher, I seek to offer insights pertaining to privacy and new technologies, as well as relevant implications for practice


Cloarec, J., Meyer-Waarden, L. and Munzel, A. (2021), The Personalization–Privacy Paradox at the Nexus of Social Exchange and Construal Level Theories, Psychology & Marketing, DOI: 10.1002/mar.21587

Cloarec, J. (2020), The Personalization-Privacy Paradox in the Attention Economy, Technological Forecasting and Social Change, 161, 120299, DOI: 10.1016/j.techfore.2020.120299

Artificial Intelligence

Meyer-Waarden, L. and Cloarec, J. (2021), “Baby, You Can Drive My Car”: Psychological Antecedents that Drive Consumers’ Adoption of AI-powered Autonomous Vehicles, Technovation, 120299, DOI: 10.1016/j.technovation.2021.102348


As an academic and trainer, I share my knowledge of Data Science, an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data


Statistics is the discipline that concerns the collection, organization, analysis, interpretation and presentation of data

Machine Learning

Machine learning is the study of computer algorithms that improve automatically through experience and by the use of data

natural language processing

Natural language processing is a subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language


Computer vision is an interdisciplinary scientific field that deals with how computers can gain high-level understanding from digital images or videos


Organizations that Trusted Me for Collaboration

Honors, Awards, and Grants

Can we invite R2 to be an author to make a truce so that they can at least try to write the paper that they think we should be writing even though it is not the paper we would like to write (especially knowing the data)?

As @j1berger says, there's a *HUGE* amount of language data out there (online reviews, social media posts, texts, customer service calls, open-ended survey questions, annual reports, ads, news articles, scripts, song lyrics, THIS TWEET!) ...super meta.

General dictionaries: you can build your own, but there's also LIWC, Diction, Roget, WordNet. Some dictionaries measure very specific things (e.g., sentiment, concreteness). You can also customize some of the pre-existing dictionaries. That's helpful!

LIWC is off-the-shelf, top-down (but customizable), well-validated and accepted, multi-lingual, and easy to use.

LIWC has 6,400 words, word stems, and even some emoticons. There are 93 output variables. It often tells you the % of words from a text that fall into a category.