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.
Do no harm: a roadmap for responsible machine learning for health care
2019506 citationsMarzyeh Ghassemi, Finale Doshi‐Velez et al.profile →
Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing Their Input Gradients
2018282 citationsFinale Doshi‐Velez et al.profile →
The myth of generalisability in clinical research and machine learning in health care
2020252 citationsJoseph Futoma, Finale Doshi‐Velez et al.profile →
Ethical and regulatory challenges of large language models in medicine
2024105 citationsFinale Doshi‐Velez et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
Countries citing papers authored by Finale Doshi‐Velez
Since
Specialization
Citations
This map shows the geographic impact of Finale Doshi‐Velez'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 Finale Doshi‐Velez with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Finale Doshi‐Velez more than expected).
Fields of papers citing papers by Finale Doshi‐Velez
This network shows the impact of papers produced by Finale Doshi‐Velez. 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 Finale Doshi‐Velez. The network helps show where Finale Doshi‐Velez may publish in the future.
Co-authorship network of co-authors of Finale Doshi‐Velez
This figure shows the co-authorship network connecting the top 25 collaborators of Finale Doshi‐Velez.
A scholar is included among the top collaborators of Finale Doshi‐Velez 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 Finale Doshi‐Velez. Finale Doshi‐Velez is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Wang, Kai, et al.. (2021). Learning MDPs from Features: Predict-Then-Optimize for Sequential Decision Making by Reinforcement Learning. arXiv (Cornell University). 34.8 indexed citations
7.
Graule, Moritz A., et al.. (2020). Incorporating Interpretable Output Constraints in Bayesian Neural Networks. Neural Information Processing Systems. 33. 12721–12731.1 indexed citations
8.
Lu, Mingyu, et al.. (2020). Is Deep Reinforcement Learning Ready for Practical Applications in Healthcare? A Sensitivity Analysis of Duel-DDQN for Sepsis Treatment. arXiv (Cornell University).1 indexed citations
9.
Futoma, Joseph, et al.. (2020). Model-based Reinforcement Learning for Semi-Markov Decision Processes with Neural ODEs. Neural Information Processing Systems. 33. 19805–19816.1 indexed citations
Hughes, Michael C., et al.. (2018). Semi-Supervised Prediction-Constrained Topic Models. International Conference on Artificial Intelligence and Statistics. 1067–1076.6 indexed citations
13.
Doshi‐Velez, Finale & Been Kim. (2017). A Roadmap for a Rigorous Science of Interpretability.. arXiv (Cornell University).55 indexed citations
14.
Fan, Angela, Finale Doshi‐Velez, & Luke Miratrix. (2017). Promoting Domain-Specific Terms in Topic Models with Informative Priors.. arXiv (Cornell University).1 indexed citations
15.
Kim, Been, Julie Shah, & Finale Doshi‐Velez. (2015). Mind the Gap: a generative approach to interpretable feature selection and extraction. Neural Information Processing Systems. 28. 2260–2268.53 indexed citations
16.
Doshi‐Velez, Finale, David Pfau, Frank Wood, & Nicholas Roy. (2015). Bayesian Nonparametric Methods for Partially-Observable Reinforcement Learning. DSpace@MIT (Massachusetts Institute of Technology).6 indexed citations
17.
Ghassemi, Marzyeh, Finale Doshi‐Velez, Rohit Joshi, et al.. (2014). Unfolding physiological state: mortality modelling in intensive care units. DSpace@MIT (Massachusetts Institute of Technology).32 indexed citations
18.
Doshi‐Velez, Finale & George Konidaris. (2012). Transfer Learning by Discovering Latent Task Parametrizations. Neural Information Processing Systems.1 indexed citations
19.
Doshi‐Velez, Finale, David Wingate, Nicholas Roy, & Joshua B. Tenenbaum. (2010). Nonparametric Bayesian Policy Priors for Reinforcement Learning. DSpace@MIT (Massachusetts Institute of Technology). 23. 532–540.18 indexed citations
20.
Doshi‐Velez, Finale. (2009). The Infinite Partially Observable Markov Decision Process. Neural Information Processing Systems. 22. 477–485.44 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.