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  2. Faculty
  3. Malek Ben Sliman

Malek Ben Sliman

Lecturer in Discipline in the Marketing Division
Marketing Division
Malek Ben Sliman
Areas of Expertise
Artificial Intelligence (AI), Marketing, Marketplace Design, Operations & Supply Chain Management
Contact
Office: 775 Kravis
E-mail: [email protected]
Links
Personal Website
Curriculum Vitae
LinkedIn

Malek Ben Sliman is a Lecturer in Discipline in the Marketing Division at Columbia Business School. His work focuses on how organizations can use AI to learn from unconventional data, from paintings and auction records to factory sensors and text. At Columbia, he teaches courses on generative AI, marketing, and analytics. His teaching focuses on helping students think critically about what AI can and cannot do and getting them to build with it. Alongside the tools he develops for his own courses, he helps students design and ship AI projects of their own.

Before joining the faculty full-time, Malek was at Sotheby's, where he built models to price artworks and track the evolution of artists' reputations, and at Oden Technologies, an Industry 4.0 start-up, where he developed analytics to improve manufacturing processes. 

Malek holds an M.S. in Industrial Engineering from Ecole Centrale Paris, an M.S. in Operations Research from the IEOR Department at Columbia, and a Ph.D. in Marketing from Columbia Business School.

All Activities

  • Research
  • Teaching
  • Executive Education
  • Achievements
  • Press
  • Authored Works
  • CaseWorks
  • Journal articles
  • Working papers
  • Articles
  • Books
  • Chapters
Journal Article
Peng, Tianyi, George (Zhida) Gui, Daniel Merlau, Jiarui Fan, Malek Ben Sliman, Eric Johnson, Abdullah Althenayyan, Silvia Bellezza, Dante Donati, Hortense Fong, Elizabeth Friedman, Ariana Guevara, Hannah Li, Vicki Morwitz, Oded Netzer, Patryk Perkowski, and Olivier Toubia. “Digital Twins are Funhouse Mirrors: Five Systematic Distortions.”
ArXiV
(January 02, 2026).
Explore Further about Digital Twins are Funhouse Mirrors: Five Systematic Distortions
Journal Article
Jedidi, Kamel, Bernd Schmitt, Malek Ben Sliman, and Yanyan Li. “R2M Index 1.0: Assessing the Practical Relevance of Academic Marketing Articles.”
Journal of Marketing
vol. 85, no.
5
(June 10, 2021): 22-41. doi:
https://doi.org/10.1177/00222429211028145
Explore Further about R2M Index 1.0: Assessing the Practical Relevance of Academic Marketing Articles
  • Courses
  • Case Studies
Course
B7609: Generative AI for Business
View Course on Generative AI for Business
Course
B9651: MS Marketing Analytics
View Course on MS Marketing Analytics
Course
B8609: Generative AI for Business (Half-Term)
View Course on Generative AI for Business (Half-Term)
Course
B6101: Business Analytics and Artificial Intelligence
View Course on Business Analytics and Artificial Intelligence
Course
B9659: Statistical Modeling and Decision Making
View Course on Statistical Modeling and Decision Making
Course
B6601: Marketing
View Course on Marketing
Course
B8631: Generative AI for Business (Full-Term)
View Course on Generative AI for Business (Full-Term)
Course
B5601: Marketing Strategy
View Course on Marketing Strategy
  • Programs
This faculty member has no associated Execed courses.
  • Awards
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  • In the Media
  • Articles
In the Media

AI Project Raises Concerns Over Emerging Hacker Threats

CBS News/MoneyWatch
Article

Digital twins stumble on surveys but may pay off in the field: Here’s why

Read More about Digital twins stumble on surveys but may pay off in the field: Here’s why
Article

In the age of AI, the MBA must go ‘one level deeper’

Read More about In the age of AI, the MBA must go ‘one level deeper’
  • Articles
  • CaseWorks
Case ID
250505

Takasago and the Use of Generative Artificial Intelligence in Consumer Insights (A and B)

Could Jelena Le Breton, global vice president of consumer insights at fragrance manufacturer Takasago, use generative AI to reliably mimic human responses in large-scale surveys on consumer preference?

View Case on Takasago and the Use of Generative Artificial Intelligence in Consumer Insights (A and B)
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