Lorenzo Stella

1.6k total citations · 2 hit papers
11 papers, 678 citations indexed

About

Lorenzo Stella is a scholar working on Computational Mechanics, Numerical Analysis and Computational Theory and Mathematics. According to data from OpenAlex, Lorenzo Stella has authored 11 papers receiving a total of 678 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Computational Mechanics, 5 papers in Numerical Analysis and 5 papers in Computational Theory and Mathematics. Recurrent topics in Lorenzo Stella's work include Sparse and Compressive Sensing Techniques (7 papers), Optimization and Variational Analysis (5 papers) and Advanced Optimization Algorithms Research (5 papers). Lorenzo Stella is often cited by papers focused on Sparse and Compressive Sensing Techniques (7 papers), Optimization and Variational Analysis (5 papers) and Advanced Optimization Algorithms Research (5 papers). Lorenzo Stella collaborates with scholars based in Belgium, Italy and Germany. Lorenzo Stella's co-authors include Tim Januschowski, Syama Sundar Rangapuram, Jan Gasthaus, Panagiotis Patrinos, Yuyang Wang, Andreas Themelis, Matthias Seeger, Konstantinos Benidis, Valentín Flunkert and Danielle C. Maddix and has published in prestigious journals such as ACM Computing Surveys, Journal of Machine Learning Research and SIAM Journal on Optimization.

In The Last Decade

Lorenzo Stella

11 papers receiving 645 citations

Hit 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.

Deep State Space Models for Time Series Forecasting

2018 247 citations Syama Sundar Rangapuram, Matthias Seeger et al. Neural Information Processing Systems profile →
Deep Learning for Time Series Forecasting: Tutorial and Literature Survey

2022 150 citations Konstantinos Benidis, Syama Sundar Rangapuram et al. ACM Computing Surveys profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Lorenzo Stella Belgium	8	204	196	189	126	125	11	678
M. Pasadas Spain	13	38 0.2×	144 0.7×	139 0.7×	116 0.9×	272 2.2×	71	796
Jakub Mareček Czechia	10	36 0.2×	156 0.8×	241 1.3×	119 0.9×	23 0.2×	45	693
Francisco J. Prieto Spain	16	49 0.2×	90 0.5×	155 0.8×	345 2.7×	30 0.2×	27	889
Pascal Bondon France	14	146 0.7×	31 0.2×	132 0.7×	137 1.1×	79 0.6×	59	672
Yu Bai United States	16	49 0.2×	87 0.4×	209 1.1×	211 1.7×	81 0.6×	69	916
Musa Mammadov Australia	18	35 0.2×	115 0.6×	263 1.4×	64 0.5×	33 0.3×	77	909
Jiarong Shi China	12	27 0.1×	129 0.7×	215 1.1×	273 2.2×	48 0.4×	37	638
Hajime Nobuhara Japan	15	58 0.3×	145 0.7×	282 1.5×	33 0.3×	16 0.1×	112	752
K.-T. Fang	4	30 0.1×	414 2.1×	246 1.3×	97 0.8×	45 0.4×	4	1.4k

Countries citing papers authored by Lorenzo Stella

Since Specialization

Citations

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

Fields of papers citing papers by Lorenzo Stella

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Lorenzo Stella

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

All Works

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11 of 11 papers shown

1.

Benidis, Konstantinos, Syama Sundar Rangapuram, Valentín Flunkert, et al.. (2022). Deep Learning for Time Series Forecasting: Tutorial and Literature Survey. ACM Computing Surveys. 55(6). 1–36. 150 indexed citations breakdown →

2.

Themelis, Andreas, Lorenzo Stella, & Panagiotis Patrinos. (2022). Douglas–Rachford splitting and ADMM for nonconvex optimization: accelerated and Newton-type linesearch algorithms. Computational Optimization and Applications. 82(2). 395–440. 6 indexed citations

3.

Alexandrov, A., Konstantinos Benidis, Michael Bohlke‐Schneider, et al.. (2020). GluonTS: Probabilistic and Neural Time Series Modeling in Python. Journal of Machine Learning Research. 21(116). 1–6. 71 indexed citations

4.

Bézenac, Emmanuel de, Syama Sundar Rangapuram, Konstantinos Benidis, et al.. (2020). Normalizing Kalman Filters for Multivariate Time Series Analysis. Neural Information Processing Systems. 33. 2995–3007. 44 indexed citations

5.

Rangapuram, Syama Sundar, Matthias Seeger, Jan Gasthaus, et al.. (2018). Deep State Space Models for Time Series Forecasting. Neural Information Processing Systems. 31. 7785–7794. 247 indexed citations breakdown →

6.

Themelis, Andreas, Lorenzo Stella, & Panagiotis Patrinos. (2018). Forward-Backward Envelope for the Sum of Two Nonconvex Functions: Further Properties and Nonmonotone Linesearch Algorithms. SIAM Journal on Optimization. 28(3). 2274–2303. 53 indexed citations

7.

Themelis, Andreas, Lorenzo Stella, & Panagiotis Patrinos. (2017). Douglas-Rachford splitting and ADMM for nonconvex optimization: new convergence results and accelerated versions. 2 indexed citations

8.

Stella, Lorenzo, Andreas Themelis, & Panagiotis Patrinos. (2017). Forward–backward quasi-Newton methods for nonsmooth optimization problems. Computational Optimization and Applications. 67(3). 443–487. 61 indexed citations

9.

Stella, Lorenzo, et al.. (2016). New primal-dual proximal algorithms for distributed optimization. Lirias (KU Leuven). 10 indexed citations

10.

Stella, Lorenzo, et al.. (2014). Forward-backward truncated Newton methods for large-scale convex composite optimization. arXiv (Cornell University). 3 indexed citations

11.

Patrinos, Panagiotis, Lorenzo Stella, & Alberto Bemporad. (2014). Douglas-rachford splitting: Complexity estimates and accelerated variants. Lirias (KU Leuven). 4234–4239. 31 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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