D. Sculley

8.1k total citations · 5 hit papers
37 papers, 3.4k citations indexed

About

D. Sculley is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, D. Sculley has authored 37 papers receiving a total of 3.4k indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Artificial Intelligence, 15 papers in Information Systems and 4 papers in Computer Vision and Pattern Recognition. Recurrent topics in D. Sculley's work include Spam and Phishing Detection (11 papers), Machine Learning and Algorithms (7 papers) and Text and Document Classification Technologies (6 papers). D. Sculley is often cited by papers focused on Spam and Phishing Detection (11 papers), Machine Learning and Algorithms (7 papers) and Text and Document Classification Technologies (6 papers). D. Sculley collaborates with scholars based in United States, Canada and Iran. D. Sculley's co-authors include Daniel Golovin, Gabriel Wachman, Dietmar Ebner, Todd Phillips, Eugene Davydov, Gary D. Holt, Michael Young, Subhodeep Moitra, Greg Kochanski and John Karro and has published in prestigious journals such as Nature Biotechnology, ACS Central Science and Literary and Linguistic Computing.

In The Last Decade

D. Sculley

37 papers receiving 3.2k citations

Hit Papers

5 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.

Web-scale k-means clustering

2010 664 citations D. Sculley profile →
Ad click prediction

2013 509 citations Gary D. Holt, D. Sculley et al. profile →
Hidden technical debt in Machine learning systems

2015 450 citations D. Sculley, Gary D. Holt et al. Neural Information Processing Systems profile →
Google Vizier

2017 286 citations Daniel Golovin, D. Sculley et al. profile →
Using deep learning to annotate the protein universe

2022 164 citations Maxwell L. Bileschi, David Belanger et al. Nature Biotechnology profile →

Peers

Countries citing papers authored by D. Sculley

Since Specialization

Citations

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

Fields of papers citing papers by D. Sculley

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of D. Sculley

This figure shows the co-authorship network connecting the top 25 collaborators of D. Sculley. A scholar is included among the top collaborators of D. Sculley 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 D. Sculley. D. Sculley 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

#	Work	Indexed citations
1	Using deep learning to annotate the protein universe breakdown → 2022 ·Nature Biotechnology ·Maxwell L. Bileschi, David Belanger, Drew Bryant, Theo Sanderson, Brandon Carter, D. Sculley, Alex Bateman, Mark A. DePristo, Lucy J. Colwell	164
2	A Voice-Activated Switch for Persons with Motor and Speech Impairments: Isolated-Vowel Spotting Using Neural Networks 2021 ·Shanqing Cai, (unknown), (unknown), Jordan R. Green, Michael P. Brenner, Philip Nelson, D. Sculley	3
3	Fairness is not static 2020 ·Alexander D’Amour, Hansa Srinivasan, James Atwood, Pallavi Baljekar, D. Sculley, Yoni Halpern	85
4	TensorFlow.js: Machine Learning for the Web and Beyond 2019 ·arXiv (Cornell University) ·Daniel Smilkov, Nikhil Thorat, (unknown), Ann Yuan, (unknown), (unknown), (unknown), Shanqing Cai, (unknown), David Soergel, (unknown), Michael Terry, Charles Nicholson, Sandeep Gupta, (unknown), D. Sculley, Rajat Monga, Greg S. Corrado, Fernanda Viégas, Martin Wattenberg	5
5	Can you trust your model's uncertainty? Evaluating predictive uncertainty under dataset shift 2019 ·arXiv (Cornell University) ·Yaniv Ovadia, Emily Fertig, Jie Ren, Zachary Nado, D. Sculley, Sebastian Nowozin, Joshua V. Dillon, Balaji Lakshminarayanan, Jasper Snoek	92
6	Rapid Prediction of Electron–Ionization Mass Spectrometry Using Neural Networks 2019 ·ACS Central Science ·Jennifer N. Wei, David Belanger, Ryan P. Adams, D. Sculley	123
7	Winner's Curse? On Pace, Progress, and Empirical Rigor. 2018 ·International Conference on Learning Representations ·D. Sculley, Jasper Snoek, Alexander B. Wiltschko, A R Ostad Rahimi	55
8	TensorFlow Estimators: Managing Simplicity vs. Flexibility in High-Level Machine Learning Frameworks 2017 ·arXiv (Cornell University) ·Heng-Tze Cheng, (unknown), Lichan Hong, (unknown), (unknown), Illia Polosukhin, Georgios Roumpos, D. Sculley, Jamie Smith, David Soergel, Yuan Tang, (unknown), Martin Wicke, (unknown), (unknown)	7
9	TensorFlow Debugger: Debugging Dataflow Graphs for Machine Learning 2016 ·Shanqing Cai, Eric Breck, Eric J. Nielsen, (unknown), D. Sculley	6
10	Hidden technical debt in Machine learning systems breakdown → 2015 ·Neural Information Processing Systems ·D. Sculley, Gary D. Holt, Daniel Golovin, Eugene Davydov, Todd Phillips, Dietmar Ebner, (unknown), Michael Young, (unknown), (unknown)	450
11	Machine Learning: The High Interest Credit Card of Technical Debt 2014 ·D. Sculley, Gary D. Holt, Daniel Golovin, Eugene Davydov, Todd Phillips, Dietmar Ebner, (unknown), Michael Young	125
12	Large scale learning to rank 2009 ·D. Sculley, (unknown)	28
13	On Free Speech and Civil Discourse: Filtering Abuse in Blog Comments. 2008 ·D. Sculley	4
14	Advances in online learning-based spam filtering 2008 ·Carla E. Brodley, D. Sculley	6
15	Online Active Learning Methods for Fast Label-Efficient Spam Filtering. 2007 ·D. Sculley	64
16	Filtering Email Spam in the Presence of Noisy User Feedback 2007 ·D. Sculley, Gordon V. Cormack	19
17	Relaxed Online SVMs in the TREC Spam Filtering Track. 2007 ·Text REtrieval Conference ·D. Sculley, Gabriel Wachman	14
18	Rank Aggregation for Similar Items 2007 ·D. Sculley	62
19	Relaxed online SVMs for spam filtering 2007 ·D. Sculley, Gabriel Wachman	167
20	Spam Filtering Using Inexact String Matching in Explicit Feature Space with On-Line Linear Classifiers. 2006 ·Text REtrieval Conference ·D. Sculley, Gabriel Wachman, Carla E. Brodley	28

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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