Zico Kolter: h-index, Total Citations, and Citation Map
Zico Kolter's h-index is 94 (246 i10-index, 71,255+ total citations across 387+ publications) according to Google Scholar as of August 2026. Zico Kolter is affiliated with Carnegie Mellon University.
Zico Kolter is a researcher affiliated with Carnegie Mellon University, specializing in machine learning, optimization, application in energy systems. Their work has been cited 71,255 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Zico Kolter's Citation Metrics
Bibliometric impact based on 387 indexed publications.
- H-Index
- 94
- i10-Index
- 246
- Total Citations
- 71,255
- Citing Countries
- 69
As of August 2026.
Zico Kolter has an h-index of 94 and 71,255 total citations across 387 publications, with research cited by institutions in 69 countries.
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Global Impact Map
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An empirical evaluation of generic convolutional and recurrent networks for sequence modeling
201811,374
Top Citing Countries
Top Citing Institutions
Visa Evidence Package
Views and exports tuned for EB-1A, O-1A, and EB-2 NIW petitions. Sustained acclaim, geographic reach, and independent-citation filtering are the strongest evidence categories immigration adjudicators look for.
Significant Contributions
Auto-detected research lines — a seminal paper and the follow-up work building on it. Review and edit before using in a petition. Each Free PDF opens in a new tab — EB-1A organises this into the structure USCIS applies to Criterion 5 of 8 CFR § 204.5(h)(3)(v); EB-1B re-frames it under § 204.5(i)(3) (outstanding researcher); NIW presents it under prong 2 of Matter of Dhanasar.
15 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher advanced the field by developing scalable provable adversarial defenses and subsequently analyzing overfitting phenomena in robust deep learning models.
The researcher provided a foundational empirical evaluation comparing generic convolutional and recurrent networks for sequence modeling, establishing a critical benchmark for architectural performance.
The researcher pioneered methods for detecting malicious executables in real-world environments, establishing a foundational framework for automated malware analysis that has been widely adopted by the independent cybersecurity community.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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About Zico Kolter's research
Zico Kolter is a researcher in machine learning, optimization and application in energy systems at Carnegie Mellon University. Their work has been cited 71,255 times across 387 publications (h-index 94), according to Google Scholar.
Their most-cited work, “An empirical evaluation of generic convolutional and recurrent networks for sequence modeling” (2018), has accumulated 11,374 citations. Other influential works include “Universal and transferable adversarial attacks on aligned language models” (2023) with 4,072 citations and “Certified adversarial robustness via randomized smoothing” (2019) with 3,424 citations.
Citations of Zico Kolter's research come primarily from United States, China and United Kingdom, reflecting international research impact across 5+ countries. The interactive citation map above shows the full geographic distribution of the institutions citing this work.
