Felipe Oviedo

2.1k total citations · 1 hit paper
35 papers, 1.4k citations indexed

About

Felipe Oviedo is a scholar working on Materials Chemistry, Electrical and Electronic Engineering and Molecular Biology. According to data from OpenAlex, Felipe Oviedo has authored 35 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Materials Chemistry, 13 papers in Electrical and Electronic Engineering and 8 papers in Molecular Biology. Recurrent topics in Felipe Oviedo's work include Machine Learning in Materials Science (11 papers), Perovskite Materials and Applications (8 papers) and Chalcogenide Semiconductor Thin Films (5 papers). Felipe Oviedo is often cited by papers focused on Machine Learning in Materials Science (11 papers), Perovskite Materials and Applications (8 papers) and Chalcogenide Semiconductor Thin Films (5 papers). Felipe Oviedo collaborates with scholars based in United States, Singapore and United Kingdom. Felipe Oviedo's co-authors include Tonio Buonassisi, Juan Lavista Ferres, Shijing Sun, Keith T. Butler, Zekun Ren, Noor Titan Putri Hartono, Siyu Tian, A. Gilad Kusne, Savitha Ramasamy and Brian DeCost and has published in prestigious journals such as Nature Communications, SHILAP Revista de lepidopterología and Chemical Communications.

In The Last Decade

Felipe Oviedo

33 papers receiving 1.4k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Interpretable and Explainable Machine Learning for Materials Science and Chemistry

2022 193 citations Felipe Oviedo, Juan Lavista Ferres et al. Accounts of Materials Research profile →

Peers

Felipe Oviedo

EEE

CTM

Siyu Tian China

Weike Ye United States

Mário R. G. Marques Germany

Aurelio Mollo United States

Paul Raccuglia United States

Anna M. Hiszpanski United States

Xiangyu Sun China

Katherine C. Elbert United States

Philip Adler United Kingdom

Siyu Tian China

Felipe Oviedo

686 ×0.8

397 ×0.8

EEE

101 ×0.5

CTM

94 ×0.6

128 ×1.0

Citations per year, relative to Felipe Oviedo Felipe Oviedo (= 1×) peers Siyu Tian

Countries citing papers authored by Felipe Oviedo

Since Specialization

Citations

This map shows the geographic impact of Felipe Oviedo'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 Felipe Oviedo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Felipe Oviedo more than expected).

Fields of papers citing papers by Felipe Oviedo

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Felipe Oviedo. 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 Felipe Oviedo. The network helps show where Felipe Oviedo may publish in the future.

Co-authorship network of co-authors of Felipe Oviedo

This figure shows the co-authorship network connecting the top 25 collaborators of Felipe Oviedo. A scholar is included among the top collaborators of Felipe Oviedo based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Felipe Oviedo. Felipe Oviedo is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Oviedo, Felipe, Yixi Xu, Robert A. Vandermeulen, et al.. (2025). Cancer Detection in Breast MRI Screening via Explainable AI Anomaly Detection. Radiology. 316(1). e241629–e241629. 2 indexed citations

Oviedo, Felipe, Florent Tixier, Satomi Kawamoto, et al.. (2025). Benchmarking robustness of automated CT pancreas segmentation: achieving human-level reliability through human-in-the-loop optimization. PubMed. 2(6). umaf040–umaf040. 1 indexed citations

Ferres, Juan Lavista, Felipe Oviedo, Caleb Robinson, et al.. (2024). Performance of explainable artificial intelligence in guiding the management of patients with a pancreatic cyst. Pancreatology. 24(7). 1182–1191. 3 indexed citations

Oviedo, Felipe, et al.. (2024). Synergistic Polymer Blending Informs Efficient Terpolymer Design and Machine Learning Discerns Performance Trends for pDNA Delivery. Bioconjugate Chemistry. 35(7). 897–911. 7 indexed citations

Tian, Siyu, Zekun Ren, Selvaraj Venkataraj, et al.. (2024). Correction: Tackling data scarcity with transfer learning: a case study of thickness characterization from optical spectra of perovskite thin films. Digital Discovery. 3(5). 1068–1068.

Ting, Jeffrey, et al.. (2024). Predictive design of multimonomeric polyelectrolytes enables lung-specific gene delivery. Polymer Chemistry. 15(26). 2627–2633. 3 indexed citations

Ting, Jeffrey, Teresa Tamayo-Mendoza, Shannon R. Petersen, et al.. (2023). Frontiers in nonviral delivery of small molecule and genetic drugs, driven by polymer chemistry and machine learning for materials informatics. Chemical Communications. 59(96). 14197–14209. 6 indexed citations

Tian, Siyu, Zekun Ren, Selvaraj Venkataraj, et al.. (2023). Tackling data scarcity with transfer learning: a case study of thickness characterization from optical spectra of perovskite thin films. Digital Discovery. 2(5). 1334–1346. 6 indexed citations

Meller, Artur, Michael D. Ward, Jonathan Borowsky, et al.. (2023). Predicting locations of cryptic pockets from single protein structures using the PocketMiner graph neural network. Nature Communications. 14(1). 1177–1177. 86 indexed citations

10.

Meller, Artur, Michael D. Ward, Jonathan Borowsky, et al.. (2023). Predicting the locations of cryptic pockets from single protein structures using the PocketMiner graph neural network. Biophysical Journal. 122(3). 445a–445a. 13 indexed citations

11.

Ortiz, Anthony, Joseph M. Kiesecker, Caleb Robinson, et al.. (2022). An Artificial Intelligence Dataset for Solar Energy Locations in India. Scientific Data. 9(1). 497–497. 28 indexed citations

12.

Oviedo, Felipe, Juan Lavista Ferres, Tonio Buonassisi, & Keith T. Butler. (2022). Interpretable and Explainable Machine Learning for Materials Science and Chemistry. Accounts of Materials Research. 3(6). 597–607. 193 indexed citations breakdown →

13.

Sun, Shijing, Armi Tiihonen, Felipe Oviedo, et al.. (2021). A data fusion approach to optimize compositional stability of halide perovskites. Matter. 4(4). 1305–1322. 111 indexed citations

14.

Ren, Zekun, Siyu Tian, Juhwan Noh, et al.. (2021). An Invertible Crystallographic Representation for <b>General</b> Inverse Design of Inorganic Crystals with Targeted Properties. SSRN Electronic Journal. 1 indexed citations

15.

Ren, Zekun, Felipe Oviedo, Siyu Tian, et al.. (2020). Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaics. npj Computational Materials. 6(1). 28 indexed citations

16.

Ren, Zekun, Felipe Oviedo, Siyu Tian, et al.. (2020). Author Correction: Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaics. npj Computational Materials. 6(1). 1 indexed citations

17.

Hartono, Noor Titan Putri, Janak Thapa, Armi Tiihonen, et al.. (2020). How machine learning can help select capping layers to suppress perovskite degradation. Nature Communications. 11(1). 4172–4172. 131 indexed citations

18.

Liu, Zhe, Felipe Oviedo, Emanuel M. Sachs, & Tonio Buonassisi. (2020). Detecting Microcracks in Photovoltaics Silicon Wafers using Varitional Autoencoder. 139–142. 3 indexed citations

19.

Oviedo, Felipe, Zekun Ren, Shijing Sun, et al.. (2018). Fast classification of small X-ray diffraction datasets using data augmentation and deep neural networks.. arXiv (Cornell University). 6 indexed citations

20.

Hoye, Robert L. Z., Kevin A. Bush, Felipe Oviedo, et al.. (2018). Developing a Robust Recombination Contact to Realize Monolithic Perovskite Tandems With Industrially Common p-Type Silicon Solar Cells. IEEE Journal of Photovoltaics. 8(4). 1023–1028. 30 indexed citations

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.

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