LOD 2022: 8th International Conference on machine Learning, Optimization & Data science - LOD 2022 Certosa di Pontignano Castelnuovo Berardenga (Siena), Italy, September 19-22, 2022 |
Conference website | https://lod2022.icas.cc |
Submission link | https://easychair.org/conferences/?conf=lod2022 |
Abstract registration deadline | May 3, 2022 |
Submission deadline | May 3, 2022 |
Since 2015, the LOD Conference brings academics, researchers and industrial researchers together in a unique multidisciplinary community to discuss state of the art and latest advances in the integration of machine learning, optimization and data science to provide and support the scientific and technological foundations for interpretable, explainable and trustworthy AI. Since 2017, LOD adopted the Asilomar AI Principles.
The 8th International Conference on Machine Learning, Optimization, and Data Science (LOD) has established itself as a premier multidisciplinary conference in machine learning, computational optimization, knowledge discovery and data science. It provides an international forum for the presentation of original multidisciplinary research results, and the exchange and dissemination of innovative and practical development experiences.
LOD 2022 will be held in Certosa di Pontignano – Castelnuovo Berardenga – Italy on September 19 – 22, 2022. The conference will consist of four days of conference sessions. We invite submissions of papers on all topics related to Machine learning, Optimization, Knowledge Discovery and Data Science, including real-world applications for the Conference Post-Proceedings by Springer Nature – Lecture Notes in Computer Science (LNCS).
Submission Guidelines
Please prepare your paper in English using the Springer Nature – Lecture Notes in Computer Science (LNCS) template, which is available here. Papers must be submitted in PDF.
Types of Submissions
When submitting a paper to LOD 2022, authors are required to select one of the following four types of papers:
- Long Paper: original novel and unpublished work (max. 15 pages, including References, in Springer LNCS format);
- Short Paper: an extended abstract of novel work (max. 5 pages, including References, in Springer LNCS format);
- Work for Oral Presentation only (no page restriction; any format). For example, work already published elsewhere, which is relevant and which may solicit fruitful discussion at the conference;
- Abstract for Poster Presentation only (max 2 pages; any format). The poster format for the presentation is A0 (118.9 cm high and 84.1 cm wide, respectively 46.8 x 33.1 inch). For research work which is relevant and which may solicit fruitful discussion at the conference.
Each paper submitted will be rigorously evaluated. The evaluation will ensure the high interest and expertise of reviewers. Following the tradition of LOD, we expect high-quality papers in terms of their scientific contribution, rigor, correctness, novelty, clarity, quality of presentation and reproducibility of experiments.Accepted papers must contain significant novel results. Results can be either theoretical or empirical. Results will be judged on the degree to which they have been objectively established and/or their potential for scientific and technological impact.
Obviously, it is also possible to present the talk virtually.
List of Topics
The last ten-year period has seen an impressive revolution in the theory and application of machine learning and big data. Topics of interest include, but are not limited to:
- Foundations, algorithms, models and theory of data science, including big data mining;
- Machine learning and statistical methods for big data;
- Machine Learning algorithms and models. Neural Networks and Learning Systems. Convolutional neural networks;
- Unsupervised, semi-supervised, and supervised learning;
- Knowledge Discovery. Learning Representations. Representation learning for planning and reinforcement learning;
- Metric learning and kernel learning. Sparse coding and dimensionality expansion. Hierarchical models. Learning representations of outputs or states;
- Multi-objective optimization. Optimization and Game Theory. Surrogate-assisted Optimization. Derivative-free Optimization;
- Big data Mining from heterogeneous data sources, including text, semi-structured, spatio-temporal, streaming, graph, web, and multimedia data;
- Big Data mining systems and platforms, and their efficiency, scalability, security and privacy;
- Computational optimization. Optimization for representation learning. Optimization under Uncertainty;
- Optimization algorithms for Real-World Applications. Optimization for Big Data. Optimization and Machine Learning;
- Implementation issues, parallelization, software platforms, hardware;
- Big Data mining for modeling, visualization, personalization, and recommendation;
- Big Data mining for cyber-physical systems and complex, time-evolving networks;
- Applications in social sciences, physical sciences, engineering, life sciences, web, marketing, finance, precision medicine, health informatics, medicine and other domains.
We particularly encourage submissions in emerging topics of high importance such as data quality, advanced deep learning, time-evolving networks, large multi-objective optimization, quantum discrete optimization, learning representations, big data mining and analytics, cyber-physical systems, heterogeneous data integration and mining, autonomous decision and adaptive control.
Committees
Program Committee
https://lod2022.icas.cc/program-committee/
Organizing committee
https://lod2022.icas.cc/committee/
Invited Speaker(s)
- Pierre Baldi, University of California Irvine, USA
More Keynote Speakers TBA
https://lod2022.icas.cc/keynote/
Past Keynote Speakers: https://lod2022.icas.cc/past-keynote-speakers/
Publication
LOD 2022 proceedings will be published in Nature - Springer LNCS.
Past editions:
- LOD 2021 The Seventh International Conference on Machine Learning, Optimization and Big DataGrasmere – Lake District – England, UK.Nature Springer – LNCS volumes 13163 and 13164.
- LOD 2020 The Sixth International Conference on Machine Learning, Optimization and Big DataCastelnuovo Berardenga – Tuscany – Italy.Nature Springer – LNCS volumes 12565 and 12566.
- LOD 2019 The Fifth International Conference on Machine Learning, Optimization and Big DataCastelnuovo Berardenga – Tuscany – Italy.Nature Springer – LNCS volume 11943.
- LOD 2018 The Fourth International Conference on Machine Learning, Optimization and Big DataVolterra – Tuscany – Italy.Nature Springer – LNCS volume 11331.
- MOD 2017 The Third International Conference on Machine Learning, Optimization and Big DataVolterra – Tuscany – Italy.Springer – LNCS volume 10710.
- MOD 2016 The Second International Workshop on Machine learning, Optimization and big DataVolterra – Tuscany – Italy.Springer – LNCS volume 10122.
- MOD 2015 International Workshop on Machine learning, Optimization and big DataTaormina – Sicily – Italy.Springer – LNCS volume 9432.
Venue
The conference will be held at the Certosa di Pontignano
- Loc. Pontignano, 5 – 53019, Castelnuovo Berardenga (Siena) – Tuscany, Italy
- phone: +39-0577-1521104
- fax: +39-0577-1521098
- email: info@lacertosadipontignano.com
- email: lorenzopasquinuzzi@lacertosadipontignano.com
- web: https://www.lacertosadipontignano.com
https://lod2022.icas.cc/venue/
Contact
All questions about submissions should be emailed to lod@icas.cc