Marco Lattuada

459 total citations
40 papers, 258 citations indexed

About

Marco Lattuada is a scholar working on Computer Networks and Communications, Hardware and Architecture and Information Systems. According to data from OpenAlex, Marco Lattuada has authored 40 papers receiving a total of 258 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Computer Networks and Communications, 23 papers in Hardware and Architecture and 11 papers in Information Systems. Recurrent topics in Marco Lattuada's work include Parallel Computing and Optimization Techniques (22 papers), Embedded Systems Design Techniques (17 papers) and Interconnection Networks and Systems (11 papers). Marco Lattuada is often cited by papers focused on Parallel Computing and Optimization Techniques (22 papers), Embedded Systems Design Techniques (17 papers) and Interconnection Networks and Systems (11 papers). Marco Lattuada collaborates with scholars based in Italy, United States and Iran. Marco Lattuada's co-authors include Fabrizio Ferrandi, Danilo Ardagna, Antonino Tumeo, Marco Minutoli, Vito Giovanni Castellana, Christian Pilato, Li Zhang, Michele Ciavotta, Jussara M. Almeida and Fabrício Murai and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Computers and IEEE Micro.

In The Last Decade

Marco Lattuada

38 papers receiving 245 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Marco Lattuada Italy 10 133 116 75 54 54 40 258
Esha Choukse United States 10 147 1.1× 162 1.4× 57 0.8× 73 1.4× 63 1.2× 23 298
Milo Tomašević Serbia 10 253 1.9× 328 2.8× 84 1.1× 42 0.8× 52 1.0× 46 418
Vito Giovanni Castellana United States 10 178 1.3× 117 1.0× 26 0.3× 44 0.8× 75 1.4× 42 274
Puneet Kaur India 6 203 1.5× 223 1.9× 108 1.4× 57 1.1× 122 2.3× 15 373
Jelica Protić Serbia 6 120 0.9× 188 1.6× 75 1.0× 41 0.8× 29 0.5× 27 271
Jalil Boukhobza France 10 84 0.6× 204 1.8× 78 1.0× 28 0.5× 71 1.3× 46 288
Luís Nogueira Portugal 10 187 1.4× 175 1.5× 42 0.6× 23 0.4× 22 0.4× 40 277
Yuebin Bai China 9 74 0.6× 129 1.1× 73 1.0× 86 1.6× 70 1.3× 33 251
Gökçen Kestor United States 11 252 1.9× 252 2.2× 81 1.1× 68 1.3× 158 2.9× 42 407
David Greaves United Kingdom 9 131 1.0× 149 1.3× 32 0.4× 39 0.7× 56 1.0× 39 254

Countries citing papers authored by Marco Lattuada

Since Specialization
Citations

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

Fields of papers citing papers by Marco Lattuada

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Marco Lattuada

This figure shows the co-authorship network connecting the top 25 collaborators of Marco Lattuada. A scholar is included among the top collaborators of Marco Lattuada 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 Marco Lattuada. Marco Lattuada 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.
Lattuada, Marco, et al.. (2023). aMLLibrary: An AutoML Approach For Performance Prediction. 215–221. 2 indexed citations
2.
Vita, Fabrizio De, et al.. (2023). µ-FF: On-Device Forward-Forward Training Algorithm for Microcontrollers. 49–56. 7 indexed citations
3.
Lattuada, Marco, et al.. (2022). Performance prediction of deep learning applications training in GPU as a service systems. Cluster Computing. 25(2). 1279–1302. 16 indexed citations
4.
Lattuada, Marco, et al.. (2022). A Path Relinking Method for the Joint Online Scheduling and Capacity Allocation of DL Training Workloads in GPU as a Service Systems. IEEE Transactions on Services Computing. 16(3). 1630–1646. 3 indexed citations
5.
Pau, Danilo, et al.. (2021). Synthetic image dataset of shaft junctions inside wind turbines in presence or absence of oil leaks. SHILAP Revista de lepidopterología. 39. 107538–107538. 3 indexed citations
6.
Minutoli, Marco, et al.. (2021). Svelto: High-Level Synthesis of Multi-Threaded Accelerators for Graph Analytics. IEEE Transactions on Computers. 71(3). 520–533. 13 indexed citations
7.
Ferrandi, Fabrizio, Vito Giovanni Castellana, Marco Lattuada, et al.. (2021). Invited: Bambu: an Open-Source Research Framework for the High-Level Synthesis of Complex Applications. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 1327–1330. 41 indexed citations
8.
Ardagna, Danilo, et al.. (2021). ANDREAS: Artificial intelligence traiNing scheDuler for accElerAted resource clusterS. BOA (University of Milano-Bicocca). 7 indexed citations
9.
Lattuada, Marco, et al.. (2021). Performance Prediction of Deep~Learning Applications Training in GPU as a Service Systems. Zenodo (CERN European Organization for Nuclear Research). 1 indexed citations
10.
Pau, Danilo, et al.. (2021). Online Learning of Oil Leak Anomalies in Wind Turbines with Block-Based Binary Reservoir. Electronics. 10(22). 2836–2836. 9 indexed citations
11.
Ciavotta, Michele, et al.. (2020). Architectural Design of Cloud Applications: A Performance-Aware Cost Minimization Approach. IEEE Transactions on Cloud Computing. 10(3). 1571–1591. 9 indexed citations
12.
Lattuada, Marco, et al.. (2020). Optimal Resource Allocation of Cloud-Based Spark Applications. IEEE Transactions on Cloud Computing. 10(2). 1301–1316. 11 indexed citations
13.
Lattuada, Marco, et al.. (2020). Hierarchical Scheduling in on-demand GPU-as-a-Service Systems. BOA (University of Milano-Bicocca). 125–132. 2 indexed citations
14.
Lattuada, Marco, et al.. (2019). Optimizing on-demand GPUs in the Cloud for Deep Learning Applications Training. BOA (University of Milano-Bicocca). 5 indexed citations
15.
Murai, Fabrício, et al.. (2019). Machine Learning for Performance Prediction of Spark Cloud Applications. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 99–106. 25 indexed citations
16.
Lattuada, Marco, et al.. (2019). Gray-Box Models for Performance Assessment of Spark Applications. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 609–618. 1 indexed citations
17.
Minutoli, Marco, et al.. (2016). Efficient synthesis of graph methods: A dynamically scheduled architecture. IEEE Conference Proceedings. 2016. 1–8. 1 indexed citations
18.
Lattuada, Marco & Fabrizio Ferrandi. (2015). Code Transformations Based on Speculative SDC Scheduling. International Conference on Computer Aided Design. 71–77. 7 indexed citations
19.
Lattuada, Marco & Fabrizio Ferrandi. (2013). Modeling pipelined application with Synchronous Data Flow graphs. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 21. 49–55. 3 indexed citations
20.
Lattuada, Marco & Fabrizio Ferrandi. (2010). Combining Target-independent Analysis with Dynamic Profiling to Build the Performance Model of a DSP. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 1895–1901. 1 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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2026