Augustin Chaintreau


1214 Amsterdam Ave
Mail Code 0401 

Tel(212) 939-7082

Research Interests

Networked Algorithms, Stochastic Analysis, Privacy, Big Data, Computer System Measurement, Social Network

Research Areas

The promise of big data, initiated by a revolution in sensing, depends on efficient (and fair!) algorithms exploring that information sphere for our best interests. Mathematics, and scientifically reproducible datasets, are critical not only for algorithms to improve for all, but also to guide society on the ethical and economic consequences of differentiated treatments led by automatic decisions. Chaintreau joined Columbia after five years of research in industry; since then he works only with public and non-profit funds. Scientists and engineers in industry labs are however frequent collaborators in the joint release of data, tools, or publication of results, as well as investigative journalists.

Today’s big data is flawed, and the threats it poses are not theoretical: Chaintreau has proved with reproducible experiments that personalization algorithms in services used by millions pose moral hazards. His measurements have revealed that metrics of social endorsement misrepresent online attention. As our lives become more digital, Chaintreau has showed that the network dynamics facilitated by online interactions and sharing economies sometimes organically exacerbates various inequalities. Personal information collection and usage, however, ultimately bring benefits that we cannot forego, in various domains such as health, energy efficiency, and public policies. While at first glance, those problems appear embedded in the fabric of Big Data, Chaintreau's work has shown, on the contrary, that these trends can be reversed by addressing some of their common root causes.

What are those causes? First, today’s Big Data is opaque. That is, the algorithms run for personalization may be kept behind closed doors and are unaccountable. Second, it typically presents a fragmented view, where the data needed to answer a question may be large, difficult to collect, and spread across two or more disparate domains. Third, its impact may be felt differently among participants in complex network dynamics, where individual incentives can reinforce unfair outcomes. For each of those hard root causes analyzed by his research, Chaintreau found that mathematical analysis and novel measurement offer a foundation to build a solution that scales: reveal what caused an algorithm output among a growing set of signals, quickly reconnect identical users among the same datasets using a few location points, and mitigate the bad consequence of incentive interaction dynamics in growing networks.

Chaintreau received a BSc in pure and applied mathematics and computer science from École Normale Supérieure (ENS) in Paris in 2001 and a MSc in probability and applications in 2002 from Universit´e Pierre et Marie Curie Paris. He earned a PhD from ENS in 2006 in mathematics and computer science for his work conducted jointly with INRIA. A recipient of a NSF CAREER in 2013, and the ACM SIGMETRICS Rising Star Award that same year. In 2015, he was elected by ACM members of the Computer System Performance Evaluation research community to its board of directors following his service in 28 ACM conference program committees, including two as chair. He also played identical roles occasionally for AAAI and IEEE venues, and served as editor for four ACM or IEEE journals, including one currently as editor in chief. He joined the faculty of Columbia Engineering in 2010. 


  • Assistant professor of computer science, Columbia University, 2010–
  • Member of the technical staff, Thomson Technicolor Research Lab, 2005-2010


  • Association for Computing Machinery (ACM)
  • Société Mathématique de France (SMF)


  • NSF CAREER, 2013
  • ACM SIGMETRICS Rising Star Award, 2013


  • 2015-2019: co-PI for National Science Foundation Award CNS 1514437 “Scalable Web Transparency: New Scientific Building Blocks, Tools, and Measurements to Tame the Data - Driven Web”
  • 2013-2018: PI for National Science Foundation CAREER Award CNS 1254035 “Ba- nalytics: Behavioral Network Analytics for Data Transparency” 


  • M. Lecuyer, M. Tucker and A. Chaintreau, Improving the Transparency of the Sharing Economy in Proceedings of ACM WWW, Apr. 2017.
  • M. Gabielkov, A. Ramachandran, A. Chaintreau, and A. Legout, Social Clicks: What and Who Gets Read on Twitter? in Proceedings of ACM SIGMETRICS, June 2016.
  • Y. Zeng, A. Chaintreau, D. Towsley, and C. Xia, A Necessary and Sufficient Condition for Throughput Scalability of Fork and Join Networks with Blocking in Proceedings of ACM SIGMETRICS, June 2016.
  • C. Riederer, Y. Kim, A. Chaintreau, N. Korula and S. Lattanzi, Linking Users Across Domains with Location Data: Theory and Validation in Proceedings of ACM WWW, Apr. 2016.
  • C. Riederer, S. Zimmeck, C. Phanord, A. Chaintreau, and S. M. Bellovin, I don’t have a photograph, but you can have my footprints.: Revealing the Demographics of Location Data, in Proceedings of ACM COSN 2015, Nov. 2015.
  • A. Ramachandran and A. Chaintreau, Who Contributes to the Knowledge Sharing Economy? pp. 3748. in Proceedings of ACM COSN 2015, Nov. 2015.
  • M. Lecuyer, R. Spahn, Y. Spiliopolous, A. Chaintreau, R. Geambasu, and D. Hsu, Sunlight: Fine-grained Targeting Detection at Scale with Statistical Confidence, in Proceedings of ACM CCS 2015, Oct. 2015.
  • A. Ramachandran, A. Chaintreau. ”I knew they clicked when I saw them with their friends” In Proceedings of ACM COSN 2014, Oct. 2014.
  • M. Lécuyer, G. Ducoffe, F. Lan, A. Papancea, T. Petsios, R. Spahn, A. Chaintreau, and R. Geambasu XRay: Enhancing the Web’s Transparency with Differential Cor- relation In Proceedings of USENIX Security, Aug. 2014.
  • A. May, A. Chaintreau, N. Korula, S. Lattanzi. Filter & Follow: Do Social Media En- courage Efficient News Curation? In Proceedings of ACM SIGMETRICS, June 2014.