The chart shows the distribution of publications by all Research.com ranked scientists in the field of Electronics and Electrical Engineering in 2026. The highlighted bar marks where Krisztian Flautner sits on this spectrum.
This scientist: 94 publications — 3rd percentile
3% of scientists in this discipline score the same or lower.
The last bar groups every scientist with 1,065 publications or more.
The chart shows the D-index (discipline H-index) distribution of Electronics and Electrical Engineering scientists ranked by Research.com in 2026. The highlighted bar marks where Krisztian Flautner sits on this spectrum.
This scientist: 39 D-Index — 33rd percentile
33% of scientists in this discipline score the same or lower.
The last bar groups every scientist with 111 D-Index or more.
Krisztian Flautner is affiliated with the University of Michigan-Ann Arbor in the United States. Their research activity centers primarily within the field of computer science, with significant contributions to artificial intelligence, computer networks and communications, information systems, and computer vision and pattern recognition.
Their scholarly output includes a range of publications, notably in venues such as arXiv (Cornell University), IEEE Computer Architecture Letters, and the Proceedings of the ACM on Programming Languages. These platforms have featured several of their recent works, including:
Their research covers a variety of topics, including but not limited to:
Co-authorship has played a notable role in their work, collaborating frequently with researchers such as Jason Mars, Lingjia Tang, Yiping Kang, Ashish Mahendra, and Savini Kashmira. These collaborations have contributed to multiple publications concentrated around core themes of large-scale system design, AI paradigms, and performance optimization.
Flautner's frequent publication venues include:
Their academic contributions predominantly advance the domains of artificial intelligence and computer science, engaging deeply with methods to improve system scalability, resource management, and performance reliability through innovative programming paradigms and AI-driven techniques.
Dan Ernst;Nam Sung Kim;Shidhartha Das;Sanjay Pant
N.S. Kim;T. Austin;D. Baauw;T. Mudge
Krisztián Flautner;Nam Sung Kim;Steve Martin;David Blaauw
Steven M. Martin;Krisztian Flautner;Trevor Mudge;David Blaauw
Krisztián Flautner;Steve Reinhardt;Trevor Mudge
S. Das;D. Roberts;Seokwoo Lee;S. Pant
Bo Zhai;David Blaauw;Dennis Sylvester;Krisztian Flautner
D. Ernst;S. Das;S. Lee;D. Blaauw
Yuan Lin;Hyunseok Lee;Mark Woh;Yoav Harel
Taeho Kgil;Shaun D'Souza;Ali Saidi;Nathan Binkert
Nam Sung Kim;K. Flautner;D. Blaauw;T. Mudge
Krisztián Flautner;Trevor Mudge
Nam Sung Kim;Krisztián Flautner;David Blaauw;Trevor Mudge
David Bull;Shidhartha Das;Karthik Shivashankar;Ganesh S. Dasika
T. Austin;D. Blaauw;T. Mudge;K. Flautner
Bo Zhai;D. Blaauw;D. Sylvester;K. Flautner
Y. Lin;H. Lee;M. Woh;Y. Harel
Mark Woh;Yuan Lin;Sangwon Seo;Scott Mahlke
Mark Woh;Sangwon Seo;Scott Mahlke;Trevor Mudge
S. Das;Sanjay Pant;D. Roberts;Seokwoo Lee
M. Woh;Sangwon Seo;S. Mahlke;T. Mudge
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