Stefano Ermon

25.6k citations
148 papers · 7.2k · 11 hit papers · h-index 31

Impact in

Papers in

    • Bayesian Modeling and Causal Inference 15
    • Domain Adaptation and Few-Shot Learning 14
    • Machine Learning and Algorithms 12
    • Machine Learning and Data Classification 12
    • Adversarial Robustness in Machine Learning 11
    • Explainable Artificial Intelligence (XAI) 11
    • Anomaly Detection Techniques and Applications 10
    • Generative Adversarial Networks and Image Synthesis 28

Stefano Ermon

141 papers receiving 7.0k citations

Stefano Ermon's Hit Papers

Sequence modeling and design from molecular to genome scale with Evo 2024 · 177 citations
1770+3+6Years since publication2505007501000

Peers

Stefano Ermon
Comparison fields: 5 of 181
  • Biophysics 434
  • Automotive Engineering 895
  • Media Technology 624
  • Transportation 451
  • Computer Vision and Pattern Recognition 1.2k
Replace Paolo Frasconi with:
Paolo Frasconi Italy
Dirk P. Kroese Australia
Tin Kam Ho United States
Haifeng Li China
Michel Verleysen Belgium
Taifeng Wang China
P. M. Durai Raj Vincent India
Damien Ernst Belgium
Hengshu Zhu China
Thomas Finley United States
Stefano Ermon relative to Paolo Frasconi Italy Paolo Frasconi's profile →
Citations per field
00.5×4.4×
Paolo Frasconi · 1×
Citations per year

Countries citing papers authored by Stefano Ermon

Since Specialization
Citations

This map shows the geographic impact of Stefano Ermon's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Stefano Ermon with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Stefano Ermon more than expected).

Fields of papers citing papers by Stefano Ermon

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Stefano Ermon. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Stefano Ermon. The network helps show where Stefano Ermon may publish in the future.

Co-authors

The 25 scholars most cited alongside Stefano Ermon, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Stefano Ermon Line = papers co-authored together Stefano Ermon links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 148 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Combining satellite imagery and machine learning to predict poverty
Hit paper breakdown →
20161034
2
Closed-loop optimization of fast-charging protocols for batteries with machine learning
Hit paper breakdown →
2020791
3
Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning
Hit paper breakdown →
2019623
4
A Survey on Behavior Recognition Using WiFi Channel State Information
Hit paper breakdown →
2017404
5
Deep Gaussian Process for Crop Yield Prediction Based on Remote Sensing Data
Hit paper breakdown →
2017360
6
High‐Voltage Charging‐Induced Strain, Heterogeneity, and Micro‐Cracks in Secondary Particles of a Nickel‐Rich Layered Cathode Material
Hit paper breakdown →
2019295
7
Using satellite imagery to understand and promote sustainable development
Hit paper breakdown →
2021274
8 2016274
9
Using publicly available satellite imagery and deep learning to understand economic well-being in Africa
Hit paper breakdown →
2020253
10
Transfer learning in environmental remote sensing
Hit paper breakdown →
2023212
11
Sequence modeling and design from molecular to genome scale with Evo
Hit paper breakdown →
2024177
12 2018176
13 2017153
14 2018150
15
On Distillation of Guided Diffusion Models
Hit paper breakdown →
2023129
16 2019120
17 202195
18 201994
19 202086
20
A DIRT-T Approach to Unsupervised Domain Adaptation
201884

About Stefano Ermon

Stefano Ermon is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing, Electrical and Electronic Engineering and Global and Planetary Change, having authored 148 papers that have together received 7.2k indexed citations. Recurring topics across this work include Generative Adversarial Networks and Image Synthesis (28 papers), Bayesian Modeling and Causal Inference (15 papers), Domain Adaptation and Few-Shot Learning (14 papers), Machine Learning and Algorithms (12 papers), Machine Learning and Data Classification (12 papers), Adversarial Robustness in Machine Learning (11 papers), Explainable Artificial Intelligence (XAI) (11 papers) and Anomaly Detection Techniques and Applications (10 papers). The work is most often cited by research in Biophysics (434 citations), Automotive Engineering (895 citations), Media Technology (624 citations), Transportation (451 citations) and Computer Vision and Pattern Recognition (1.2k citations). Stefano Ermon has collaborated with scholars based in United States, China and Canada. Frequent co-authors include David B. Lobell, Marshall Burke, Neal Jean, Sang Michael Xie, Aditya Grover, Anne Driscoll, Russell J. Stewart, Sankalp Dayal, Shahrokh Valaee and Siamak Yousefi. Their work appears in journals such as Science, Remote Sensing of Environment, Nature Communications, IEEE Transactions on Pattern Analysis and Machine Intelligence and International Journal of Multiphase Flow.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

Explore authors with similar magnitude of impact