Albert S. Berahas

569 total citations
20 papers, 210 citations indexed

About

Albert S. Berahas is a scholar working on Computational Mechanics, Artificial Intelligence and Numerical Analysis. According to data from OpenAlex, Albert S. Berahas has authored 20 papers receiving a total of 210 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Computational Mechanics, 10 papers in Artificial Intelligence and 9 papers in Numerical Analysis. Recurrent topics in Albert S. Berahas's work include Sparse and Compressive Sensing Techniques (10 papers), Advanced Optimization Algorithms Research (8 papers) and Stochastic Gradient Optimization Techniques (7 papers). Albert S. Berahas is often cited by papers focused on Sparse and Compressive Sensing Techniques (10 papers), Advanced Optimization Algorithms Research (8 papers) and Stochastic Gradient Optimization Techniques (7 papers). Albert S. Berahas collaborates with scholars based in United States, China and Mexico. Albert S. Berahas's co-authors include Ermin Wei, Nitish Shirish Keskar, Martin Takáč, Daniel P. Robinson, Frank E. Curtis, Katya Scheinberg, Matthew Brusstar, Ruonan Sun, André L. Boehman and Jiahao Shi and has published in prestigious journals such as IEEE Transactions on Automatic Control, ACS Applied Materials & Interfaces and Mathematical Programming.

In The Last Decade

Albert S. Berahas

18 papers receiving 203 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Albert S. Berahas United States 8 106 72 62 48 26 20 210
Maher Nouiehed United States 6 99 0.9× 28 0.4× 36 0.6× 12 0.3× 16 0.6× 10 180
Julie Nutini Canada 5 66 0.6× 45 0.6× 8 0.1× 47 1.0× 9 0.3× 7 225
Salar Fattahi United States 9 37 0.3× 33 0.5× 33 0.5× 25 0.5× 18 0.7× 26 208
Ky Vu France 4 34 0.3× 16 0.2× 8 0.1× 15 0.3× 23 0.9× 5 139
Maciej Smółka Poland 11 92 0.9× 31 0.4× 25 0.4× 3 0.1× 9 0.3× 37 217
Diego Feijer Uruguay 3 53 0.5× 47 0.7× 203 3.3× 43 0.9× 18 0.7× 3 335
Jiaping Yu China 10 30 0.3× 172 2.4× 35 0.6× 38 0.8× 2 0.1× 39 316
Thinh T. Doan United States 10 114 1.1× 36 0.5× 147 2.4× 3 0.1× 28 1.1× 40 263
Damien Scieur France 5 34 0.3× 26 0.4× 12 0.2× 29 0.6× 12 0.5× 11 179
H. Mukai United States 8 38 0.4× 16 0.2× 19 0.3× 23 0.5× 11 0.4× 24 329

Countries citing papers authored by Albert S. Berahas

Since Specialization
Citations

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

Fields of papers citing papers by Albert S. Berahas

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Albert S. Berahas. 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 Albert S. Berahas. The network helps show where Albert S. Berahas may publish in the future.

Co-authorship network of co-authors of Albert S. Berahas

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

All Works

20 of 20 papers shown
1.
Liu, Yang, et al.. (2025). Collaborative and Distributed Bayesian Optimization via Consensus. IEEE Transactions on Automation Science and Engineering. 22. 11343–11355. 2 indexed citations
2.
3.
Berahas, Albert S., et al.. (2024). Adaptive Consensus: A Network Pruning Approach for Decentralized Optimization. SIAM Journal on Optimization. 34(4). 3653–3680.
4.
Berahas, Albert S., et al.. (2024). Gradient Descent in the Absence of Global Lipschitz Continuity of the Gradients. SIAM Journal on Mathematics of Data Science. 6(3). 602–626. 1 indexed citations
5.
Berahas, Albert S., et al.. (2024). Balancing Communication and Computation in Gradient Tracking Algorithms for Decentralized Optimization. Journal of Optimization Theory and Applications. 203(3). 2954–2987. 3 indexed citations
6.
Zhang, Junru, Zhenghao Zhai, Ali Moammeri, et al.. (2024). Scalable Accelerated Materials Discovery of Sustainable Polysaccharide-Based Hydrogels by Autonomous Experimentation and Collaborative Learning. ACS Applied Materials & Interfaces. 16(51). 70310–70321. 1 indexed citations
7.
Berahas, Albert S., et al.. (2023). First- and second-order high probability complexity bounds for trust-region methods with noisy oracles. Mathematical Programming. 207(1-2). 55–106. 14 indexed citations
8.
Berahas, Albert S., et al.. (2023). Accelerating stochastic sequential quadratic programming for equality constrained optimization using predictive variance reduction. Computational Optimization and Applications. 86(1). 79–116. 9 indexed citations
9.
Berahas, Albert S., et al.. (2023). Multiblock Parameter Calibration in Computer Models. RePEc: Research Papers in Economics. 2(2). 116–137. 6 indexed citations
10.
Berahas, Albert S., Frank E. Curtis, Michael O’Neill, & Daniel P. Robinson. (2023). A Stochastic Sequential Quadratic Optimization Algorithm for Nonlinear-Equality-Constrained Optimization with Rank-Deficient Jacobians. Mathematics of Operations Research. 49(4). 2212–2248. 8 indexed citations
11.
Berahas, Albert S., et al.. (2022). Full-low evaluation methods for derivative-free optimization. Optimization methods & software. 38(2). 386–411. 3 indexed citations
12.
Berahas, Albert S., et al.. (2022). Modeling and Predicting Heavy-Duty Vehicle Engine-Out and Tailpipe Nitrogen Oxide (NOx) Emissions Using Deep Learning. Frontiers in Mechanical Engineering. 8. 18 indexed citations
13.
Berahas, Albert S., et al.. (2021). Sequential Quadratic Optimization for Nonlinear Equality Constrained Stochastic Optimization. SIAM Journal on Optimization. 31(2). 1352–1379. 27 indexed citations
14.
Berahas, Albert S., et al.. (2021). Limited-memory BFGS with displacement aggregation. Mathematical Programming. 194(1-2). 121–157. 2 indexed citations
15.
Berahas, Albert S., et al.. (2020). On the Convergence of Nested Decentralized Gradient Methods with Multiple Consensus and Gradient Steps. arXiv (Cornell University). 7 indexed citations
16.
Berahas, Albert S. & Martin Takáč. (2020). A robust multi-batch L-BFGS method for machine learning*. Figshare. 27 indexed citations
17.
Berahas, Albert S., et al.. (2020). Finite Difference Neural Networks: Fast Prediction of Partial Differential Equations. 130–135. 7 indexed citations
18.
Berahas, Albert S., et al.. (2018). Balancing Communication and Computation in Distributed Optimization. IEEE Transactions on Automatic Control. 64(8). 3141–3155. 70 indexed citations
19.
Iliadis, Michael, Leonidas Spinoulas, Albert S. Berahas, Haohong Wang, & Aggelos K. Katsaggelos. (2016). Multi-model robust error correction for face recognition. 24. 3229–3233. 2 indexed citations
20.
Iliadis, Michael, Leonidas Spinoulas, Albert S. Berahas, Haohong Wang, & Aggelos K. Katsaggelos. (2014). Sparse representation and least squares-based classification in face recognition. 526–530. 3 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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