2026 D. Sculley: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Computer Science	33	12378	12016	5015	4788	57	10096

Overview

D. Sculley is affiliated with Google in the United States and has contributed extensively to research at the intersection of computer science and medicine. Their work spans multiple fields of study, primarily focusing on artificial intelligence, molecular biology, and computational theory and mathematics.

The scientist's research covers diverse subfields, including:

Artificial Intelligence
Molecular Biology
Computational Theory and Mathematics
Ophthalmology
Spectroscopy

Their main topics of work address several specialized areas such as:

Genomics and Phylogenetic Studies
Machine Learning in Bioinformatics
Advanced Proteomics Techniques and Applications
Metaheuristic Optimization Algorithms Research
Evolutionary Algorithms and Applications
Advanced Multi-Objective Optimization Algorithms
DNA and Biological Computing

D. Sculley has been published in several venues, with a significant number of publications appearing on arXiv (Cornell University). Frequent publication venues include:

arXiv (Cornell University)
American Journal of Transplantation
Nature Biotechnology
The American Journal of Human Genetics

Notable recent papers authored or co-authored by D. Sculley include:

"Underspecification Presents Challenges for Credibility in Modern Machine Learning", 2020, arXiv (Cornell University)
"Using deep learning to annotate the protein universe", 2022, Nature Biotechnology
"Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift", 2020, arXiv (Cornell University)
"Population-Based Black-Box Optimization for Biological Sequence Design", 2020, arXiv (Cornell University)
"Best-Effort Lazy Evaluation for Python Software Built on APIs", 2021, arXiv (Cornell University)

D. Sculley frequently collaborates with several researchers, including:

Zachary Nado
Lucy J. Colwell
Dan Moldovan
Babak Alipanahi
Farhad Hormozdiari

The combination of computer science and medicine in D. Sculley's work is reflected by their contributions to both advanced algorithmic approaches and biological applications. This multidisciplinary involvement includes research on machine learning methods tailored to bioinformatics challenges, proteomics, and optimization algorithms applied to biological sequence design.

Best Publications

Web-scale k-means clustering

D. Sculley

1349
Citations
Hidden technical debt in Machine learning systems

D. Sculley;Gary Holt;Daniel Golovin;Eugene Davydov

1077
Citations
Ad click prediction: a view from the trenches

H. Brendan McMahan;Gary Holt;D. Sculley;Michael Young

1021
Citations
Can you trust your model's uncertainty? Evaluating predictive uncertainty under dataset shift

Yaniv Ovadia;Emily Fertig;Jie Ren;Zachary Nado

999
Citations
Google Vizier: A Service for Black-Box Optimization

Daniel Golovin;Benjamin Solnik;Subhodeep Moitra;Greg Kochanski

672
Citations
Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift

Yaniv Ovadia;Emily Fertig;Jie Ren;Zachary Nado

560
Citations
Underspecification Presents Challenges for Credibility in Modern Machine Learning

Alexander D'Amour;Katherine A. Heller;Dan Moldovan;Ben Adlam

480
Citations
Relaxed online SVMs for spam filtering

D. Sculley;Gabriel M. Wachman

359
Citations
Machine Learning: The High Interest Credit Card of Technical Debt

D. Sculley;Gary Holt;Daniel Golovin;Eugene Davydov

295
Citations
The ML test score: A rubric for ML production readiness and technical debt reduction

Eric Breck;Shanqing Cai;Eric Nielsen;Michael Salib

239
Citations
No Classification without Representation: Assessing Geodiversity Issues in Open Data Sets for the Developing World

Shreya Shankar;Yoni Halpern;Eric Breck;James Atwood

225
Citations
Combined regression and ranking

D. Sculley

190
Citations
Rapid Prediction of Electron–Ionization Mass Spectrometry Using Neural Networks

Jennifer N. Wei;Jennifer N. Wei;David Belanger;Ryan P. Adams;D. Sculley

175
Citations
Fairness is not static: deeper understanding of long term fairness via simulation studies

Alexander D'Amour;Hansa Srinivasan;James Atwood;Pallavi Baljekar

163
Citations
TensorFlow.js: Machine Learning for the Web and Beyond

Daniel Smilkov;Nikhil Thorat;Yannick Assogba;Ann Yuan

152
Citations
Predicting bounce rates in sponsored search advertisements

D. Sculley;Robert G. Malkin;Sugato Basu;Roberto J. Bayardo

141
Citations
Online Active Learning Methods for Fast Label-Efficient Spam Filtering.

D. Sculley

140
Citations
Winner's Curse? On Pace, Progress, and Empirical Rigor.

D. Sculley;Jasper Snoek;Alexander B. Wiltschko;Ali Rahimi

138
Citations
Compression and machine learning: a new perspective on feature space vectors

D. Sculley;C.E. Brodley

137
Citations
TensorFlow.js: Machine Learning for the Web and Beyond

Daniel Smilkov;Nikhil Thorat;Yannick Assogba;Ann Yuan

137
Citations
Direct-Manipulation Visualization of Deep Networks

Daniel Smilkov;Shan Carter;D. Sculley;Fernanda B. Viégas

111
Citations
Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift

Zachary Nado;Shreyas Padhy;D. Sculley;Alexander D'Amour

98
Citations
Using Deep Learning to Annotate the Protein Universe

Maxwell L Bileschi;David Belanger;Drew H Bryant;Theo Sanderson

60
Citations

Frequent Co-Authors

Jasper Snoek Google (United States)

Balaji Lakshminarayanan Google (United States)

Carla E. Brodley Northeastern University

Martin Wattenberg Harvard University

Tiark Rompf Purdue University West Lafayette

H. Brendan McMahan Google (United States)

Ryan P. Adams Princeton University

Fernanda B. Viégas Harvard University

Sebastian Nowozin Microsoft (United States)

Mark A. DePristo BigHat Biosciences

External Links

Personal website of D. Sculley

If you think any of the details on this page are incorrect, let us know.

Related Online Degrees & Career Pathways

Exploring Computer Science in the USA opens up a variety of related online degrees and flexible career options. Many students look for programs that combine technical knowledge with practical skills. For instance, choosing a data science degree online can help you gain expertise in analytics and artificial intelligence, often with affordable tuition rates.

Those with an interest in engineering might consider enrolling at one of the top online electrical engineering schools. These programs provide robust training in modern hardware and software systems, preparing graduates for diverse technology careers.

For students who want to boost employability in less time, obtaining certifications for jobs is a smart step. These short-term credentials can quickly validate your skills and lead to high-demand, high-paying tech positions.

Additionally, professionals aiming to fast-track their advancement often seek the fastest masters degree online programs. These allow you to gain advanced knowledge and leadership skills in as little as one year, so you can stay ahead in an ever-evolving industry.