Stephen R. Becker
Ph.D. California Institute of Technology 2011
Facts
13 properties- Instance of
- human
- Occupation
- university teacher
- Field of work
- applied mathematics, electrical and computer engineering[2][3]
- Educated at
- California Institute of Technology[5]
- Official website
- amath.colorado.edu[13]
- Doctoral advisor
- Emmanuel Candès[6]
- Doctoral student
- David Kozak, Farhad Pourkamali-Anaraki, James Folberth[8][9][10]
🏷️ Also known as
en Stephen Becker
en Stephen R. Becker
🔖 Identifiers · 6
- ORCID iD
- 0000-0002-1932-8159
- OpenAlex ID
- A5086042756, A5128476750as of 2026-09-23
where from?
- Source
- OpenAlex
- Edition
- 20260923 · as of 2026-09-23
- Licence
- CC0 1.0 Universal
- Bronze
- not in the catalog
- Transform
- 4ort-mint · openalex:20260923 · version not recorded
- Google Scholar author ID
- fUDbcnYAAAAJ
- MR Author ID
- 924214
- Mathematics Genealogy Project ID
- 169307
- ZbMATH author ID
- becker.stephen-r
📜 Data licence
Source: OpenAlex (openalex.org), snapshot of 2026-09-23. CC0 1.0 Universal. CC0 1.0 Universal · OpenAlex
🔗 Connections
Author of 8
A stochastic subspace approach to gradient-free optimization in high dimensions,
Bi-fidelity variational auto-encoder for uncertainty quantification,
Efficient dictionary learning via very sparse random projections,
Guarantees for the Kronecker fast Johnson–Lindenstrauss transform using a coherence and sampling argument,
Low-Rank Tucker Decomposition of Large Tensors Using TensorSketch,
Modeling Massive Highly Multivariate Nonstationary Spatial Data with the Basis Graphical Lasso,
Preconditioned Data Sparsification for Big Data with Applications to PCA and K-means,
Randomized Clustered Nystrom for Large-Scale Kernel Machines
← Authored by 8
A stochastic subspace approach to gradient-free optimization in high dimensions,
Bi-fidelity variational auto-encoder for uncertainty quantification,
Efficient dictionary learning via very sparse random projections,
Guarantees for the Kronecker fast Johnson–Lindenstrauss transform using a coherence and sampling argument,
Low-Rank Tucker Decomposition of Large Tensors Using TensorSketch,
Modeling Massive Highly Multivariate Nonstationary Spatial Data with the Basis Graphical Lasso,
Preconditioned Data Sparsification for Big Data with Applications to PCA and K-means,
Randomized Clustered Nystrom for Large-Scale Kernel Machines
Affiliated with 2
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Summary
Stephen R. Becker worked as a university teacher[1].
Body
Career and Affiliations
Fields of work include applied mathematics[2], an academic discipline[17] and electrical and computer engineering[3], an academic discipline[18]. Doctoral students include David Kozak[8], a mathematician[19]; Farhad Pourkamali-Anaraki[9]; and James Folberth[10].
FAQs
What did Stephen R. Becker do for work?
Stephen R. Becker worked as university teacher[1].
Where did Stephen R. Becker go to school?
Stephen R. Becker was educated at California Institute of Technology[5].