Regina Barzilay: h-index, Total Citations, and Citation Map
Regina Barzilay's h-index is 109 (251 i10-index, 54,044+ total citations across 8+ publications) according to Google Scholar as of June 2026. Regina Barzilay is affiliated with Professor of EECS, MIT.
Regina Barzilay is a researcher affiliated with Professor of EECS, MIT, specializing in AI & Machine Learning. Their work has been cited 54,044 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Regina Barzilay's Citation Metrics
Bibliometric impact based on 8 indexed publications.
- H-Index
- 109
- i10-Index
- 251
- Total Citations
- 54,044
- Citing Countries
- 18
As of June 2026.
Regina Barzilay has an h-index of 109 and 54,044 total citations across 8 publications, with research cited by institutions in 18 countries.
Global Impact Map
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A Deep Learning Approach to Antibiotic Discovery
20203,139
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.
The researcher pioneered a deep learning framework for antibiotic discovery, establishing a foundational computational approach that has significantly influenced the field of antimicrobial drug development.
The researcher pioneered the application of lexical chains to text summarization, establishing a foundational method for identifying semantic coherence in automated summary generation.
The researcher developed an entity-based approach to modeling local coherence, establishing a foundational framework for discourse analysis that has been widely adopted by independent scholars.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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About Regina Barzilay's research
Regina Barzilay is a researcher in AI & Machine Learning at Professor of EECS, MIT. Their work has been cited 54,044 times across 8 publications (h-index 109), according to Google Scholar.
Their most-cited work, “A Deep Learning Approach to Antibiotic Discovery” (2020), has accumulated 3,139 citations. Other influential works include “Analyzing Learned Molecular Representations for Property Prediction” (2019) with 2,299 citations and “Junction Tree Variational Autoencoder for Molecular Graph Generation” (2018) with 2,280 citations.
Citations of Regina Barzilay's research come primarily from United States, Switzerland and China, reflecting international research impact across 5+ countries. The interactive citation map above shows the full geographic distribution of the institutions citing this work.
