Finale Doshi

450 total citations
11 papers, 281 citations indexed

About

Finale Doshi is a scholar working on Artificial Intelligence, Biomedical Engineering and Molecular Biology. According to data from OpenAlex, Finale Doshi has authored 11 papers receiving a total of 281 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Artificial Intelligence, 2 papers in Biomedical Engineering and 1 paper in Molecular Biology. Recurrent topics in Finale Doshi's work include Reinforcement Learning in Robotics (4 papers), Bayesian Modeling and Causal Inference (3 papers) and Multi-Agent Systems and Negotiation (3 papers). Finale Doshi is often cited by papers focused on Reinforcement Learning in Robotics (4 papers), Bayesian Modeling and Causal Inference (3 papers) and Multi-Agent Systems and Negotiation (3 papers). Finale Doshi collaborates with scholars based in United States, United Kingdom and Canada. Finale Doshi's co-authors include Nicholas Roy, Joëlle Pineau, Jurgen Van Gael, Yee Whye Teh, Nicholas Roy, Nicholas Roy, Joshua Redding, Alborz Geramifard, Jonathan P. How and Josh Tenenbaum and has published in prestigious journals such as Connection Science, PubMed and International Conference on Machine Learning.

In The Last Decade

Finale Doshi

11 papers receiving 262 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Finale Doshi United States	8	211	48	48	32	27	11	281
Jimmy Secretan United States	7	224 1.1×	11 0.2×	81 1.7×	14 0.4×	6 0.2×	21	347
Christoph Salge United Kingdom	10	140 0.7×	9 0.2×	25 0.5×	18 0.6×	5 0.2×	31	294
Kevin Ellis United States	13	209 1.0×	6 0.1×	58 1.2×	20 0.6×	7 0.3×	33	380
Wei Bi China	12	315 1.5×	8 0.2×	136 2.8×	16 0.5×	3 0.1×	42	469
Matthew C. Fontaine United States	8	84 0.4×	8 0.2×	43 0.9×	27 0.8×	3 0.1×	20	150
Peter Gorniak United States	10	263 1.2×	28 0.6×	107 2.2×	14 0.4×		17	376
Povilas Daniušis Lithuania	5	163 0.8×	3 0.1×	30 0.6×	16 0.5×	22 0.8×	11	277
José Luis Montaña Spain	9	99 0.5×	7 0.1×	36 0.8×	6 0.2×	6 0.2×	35	314
Paul McKevitt United States	10	110 0.5×	16 0.3×	174 3.6×	16 0.5×	2 0.1×	51	334
Daniel Hládek Slovakia	9	194 0.9×	13 0.3×	40 0.8×	51 1.6×		50	295

Countries citing papers authored by Finale Doshi

Since Specialization

Citations

This map shows the geographic impact of Finale Doshi'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 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 more than expected).

Fields of papers citing papers by Finale Doshi

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Finale Doshi

This figure shows the co-authorship network connecting the top 25 collaborators of Finale Doshi. A scholar is included among the top collaborators of Finale Doshi 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. Finale Doshi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

11 of 11 papers shown

1.

Geramifard, Alborz, Finale Doshi, Joshua Redding, Nicholas Roy, & Jonathan P. How. (2011). Online Discovery of Feature Dependencies. International Conference on Machine Learning. 881–888. 25 indexed citations

2.

Doshi, Finale, David Wingate, Josh Tenenbaum, & Nicholas Roy. (2011). Infinite Dynamic Bayesian Networks. International Conference on Machine Learning. 913–920. 17 indexed citations

3.

Doshi, Finale, et al.. (2009). Variational Inference for the Indian Buffet Process. UCL Discovery (University College London). 137–144. 63 indexed citations

4.

Doshi, Finale, Joëlle Pineau, & Nicholas Roy. (2008). Reinforcement Learning with Limited Reinforcement: Using Bayes Risk for Active Learning in POMDPs.. 6 indexed citations

5.

Doshi, Finale & Nicholas Roy. (2008). The permutable POMDP: fast solutions to POMDPs for preference elicitation. Adaptive Agents and Multi-Agents Systems. 493–500. 26 indexed citations

6.

Doshi, Finale, Joëlle Pineau, & Nicholas Roy. (2008). Reinforcement learning with limited reinforcement. 256–263. 14 indexed citations

7.

Doshi, Finale, Joëlle Pineau, & Nicholas Roy. (2008). Reinforcement Learning with Limited Reinforcement: Using Bayes Risk for Active Learning in POMDPs.. PubMed. 301. 256–263. 38 indexed citations

8.

Doshi, Finale & Nicholas Roy. (2008). Spoken language interaction with model uncertainty: an adaptive human–robot interaction system. Connection Science. 20(4). 299–318. 42 indexed citations

9.

Doshi, Finale, Emma Brunskill, Alexander Shkolnik, et al.. (2007). Collision Detection in Legged Locomotion using Supervised Learning. 317–322. 6 indexed citations

10.

Doshi, Finale & Nicholas Roy. (2007). Efficient model learning for dialog management. 65–72. 42 indexed citations

11.

Doshi, Finale, et al.. (2000). The Safe Distance Between Airplanes and the Complexity of an Airspace Sector. 2 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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