The 7th International Conference on Machine Learning, Optimization, and Data Science – LOD 2021 – October 5-8, 2021 – Grasmere, Lake District, England – UK

LOD 2021, An Interdisciplinary Conference: Machine Learning, Optimization, Big Data & Artificial Intelligence without Borders

PAPERS SUBMISSION: 

All papers must be submitted using EasyChair:

Paper Submission deadline: Monday May 31, 2021 (Anywhere on Earth)

Any questions regarding the submission process can be sent to conference organizers: lod@icas.cc

PAPER FORMAT:

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 2021, authors are required to select one of the following four types of papers:

* long paper: original novel and unpublished work (max. 15 pages in Springer LNCS format);

* short paper: an extended abstract of novel work (max. 5 pages);

* 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.

It is also possible to present the talk virtually (Zoom).

KEYNOTE SPEAKERS:

* Ioannis Antonoglou, DeepMind, UK

  Topics:  AlphaGO, Model-Based Reinforcement Learning

  Title: TBA

* Paige Bailey, Microsoft, USA

  Topics: TensorFlow 2.0, Data Analysis, Machine Learning

  Title: Machine Learning with TF 2.x and JAX

* Roberto Cipolla, University of Cambridge, UK

  Topics: Computer Vision, Machine Learning

* Panos Pardalos, University of Florida, USA

  Topics: Optimization, Complex Networks & Data Science

  Title: TBA

* Verena Rieser, Heriot Watt University, UK

  Topics: Natural Language Processing, Conversational AI, Spoken Dialogue Systems, Dialog, Natural Language Generation

  Title: Advances and Challenges in Conversational AI

 

ACAIN 2021 Keynote Speakers

* Timothy Behrens, Nuffield Department of Clinical Neurosciences, University of Oxford, UK

  Topics: Computational Neuroscience, Behavioral Neuroscience, Decision Making, Learning Brain Connectivity

* Matthew Botvinick, DeepMind, UK

Topics: Artificial Intelligence, Neuroscience, Cognitive Psychology, Cognitive Science

* Claudia Clopath, Computational Neuroscience Lab, Dept of Bioengineering, Imperial College London, UK

  Topics: Computational Neuroscience

* Ila Fiete, MIT, USA 

  Topics: Theoretical neuroscience, Computational neuroscience, Neural coding

* Karl Friston, Institute of Neurology, University College London, UK & Wellcome Trust Centre for Neuroimaging

  Topics: Neuroscience

* Timothy Lillicrap, Google DeepMind & UCL, UK

  Topics: Computational Neuroscience

* Rosalyn Moran, Department of Neuroimaging, King’s College London, UK

  Topics: Computational Neuroscience 

* Maneesh Sahani, Gatsby Computational Neuroscience Unit, University College London, UK

  Topics: Theoretical Neuroscience,  Machine Learning

* Jane Wang, DeepMind, UK

  Topics: neural networks, cognitive neuroscience,  meta-learning, deep reinforcement learning

TUTORIAL SPEAKER(S):

* “Introduction to PyTorch” (4 hours), Thomas Viehmann, MathInf GmbH, Germany

More Tutorial Speakers Coming soon!

PAST LOD KEYNOTE SPEAKERS:

Pierre Baldi, University of California Irvine, USA

Yoshua Bengio, Head of the Montreal Institute for Learning Algorithms (MILA) & University of Montreal, Canada

Bettina Berendt, TU Berlin, Germany & KU Leuven, Belgium, and Weizenbaum Institute for the Networked Society, Germany

Jörg Bornschein, DeepMind, London, UK

Michael Bronstein, Imperial College London, UK

Nello Cristianini, University of Bristol, UK

Peter Flach, University of Bristol, UK, and EiC of the Machine Learning Journal

Marco Gori, University of Siena, Italy

Arthur Gretton, UCL, UK

Arthur Guez, Google DeepMind, Montreal, UK

Yi-Ke Guo, Imperial College London, UK

George Karypis, University of Minnesota, USA

Vipin Kumar, University of Minnesota, USA

Marta Kwiatkowska, University of Oxford, UK

Angelo Lucia, University of Rhode Island, USA

George Michailidis, University of Florida, USA

Kaisa Miettinen, University of Jyväskylä, Finland

Stephen Muggleton, Imperial College London, UK

Panos Pardalos, University of Florida, USA

Jan Peters, Technische Universitaet Darmstadt & Max-Planck Institute for Intelligent Systems, Germany

Tomaso Poggio, MIT, USA

Andrey Raygorodsky, Moscow Institute of Physics and Technology, Russia

Mauricio G. C. Resende, Amazon.com Research and University of Washington Seattle, Washington, USA

Raniero Romagnoli, CTO Almawave, Italy

Ruslan Salakhutdinov, Carnegie Mellon University, USA, and AI Research at Apple

Maria Schuld, Xanadu & University of KwaZulu-Natal, South Africa

My Thai, University of Florida, USA

Richard E. Turner, Department of Engineering, University of Cambridge, UK

Ruth Urner, York University, Toronto, Canada

Isabel Valera, Saarland University, Saarbrücken & Max Planck Institute for Intelligent Systems, Tübingen, Germany

SPECIAL SESSIONS:

*) Special Session on “Data Science for Sustainable Cities”

Chairs: Alberto Castellini, Alessandro Farinelli, Giuseppe Nicosia, Varun Ojha

Contact: alberto.castellini@univr.it

The amount of data generated nowadays by society, city infrastructures, and digital technologies around us is astonishing. The analysis, modeling and knowledge extraction of/from these data is a key asset for understanding urban environments and improving the efficiency of urban mobility,  air quality and other forms of sustainability. This special session provides a platform to share high-quality research ideas related to data science methods and technologies for urban environments, a topic of crucial importance for many Sustainable Development Goals (i.e., SDG 7 on Sustainable Energy and SDG 11 on Sustainable Cities and Communities). Another important goal is to establish a meeting point for researchers in academia and industry who develop methodologies and technologies for data science, machine learning and artificial intelligence with specific applications in smart and sustainable cities.

Analytics for smart growth and effective infrastructure

A selection of the best papers accepted for the presentation at the special session will be invited to submit an extended version for publication on Frontiers in Sustainable Cities (https://www.frontiersin.org/journals/sustainable-cities#)

*) Special Session on AI for Sustainability

We welcome  contributions on AI for Sustainable Development, AI for Sustainable Urban Mobility, AI for Food Security, AI to fight Deforestation, cutting-edge technology AI to create Inclusive and Sustainable development that leaves no one behind.

*) Special Session on AI to help to fight Climate Change

AI is a new tool to help us better manage the impacts of climate change and protect the planet. AI can be a “game-changer” for climate change and environmental issues.

AI refers to computer systems that “can sense their environment, think, learn, and act in response to what they sense and their programmed objectives,”

World Economic Forum report, Harnessing Artificial Intelligence for the Earth.

We accept papers/short papers/talks at the intersection of climate change, AI, machine learning and data science. AI, Machine Learning and Data Science  can be invaluable tools both in reducing greenhouse gas emissions and in helping society adapt to the effects of climate change.

We invite submissions  using AI, Machine Learning and/or Data Science to address problems in climate mitigation/adaptation including but not limited to the following topics:

* Industrial Session

Chairs: Giovanni Giuffrida – Neodata.

* Special Session on Explainable Artificial Intelligence

Explainability is essential for users to effectively understand, trust, and manage powerful artificial intelligence applications.

* Special Session on Multi-Objective Optimization (MOO) & Multi Criteria Decision Aiding (MCDA)

* The 7 Special Sessions on Machine Learning

Multi-Task Learning

Reinforcement Learning

Deep Learning

Generative Adversarial Networks

Deep Neuroevolution

Networks with Memory

Learning from Less Data and Building Smaller Models

* The 7 Special Session on Data Science and Artificial Intelligence

Simulation Environments to understand how AI Systems Learn

Chatbots and Conversational Agents

Data Science at Scale & Data in the Cloud

Urban Informatics & Data-Driven Modelling of Complex Systems

Data-centric Engineering

Data Security, Traceability of Information & GDPR

Economic Data Science

BEST PAPER AWARD:

Springer sponsors the LOD 2021 Best Paper Award with a cash prize of 1,000 Euro.

PROGRAM COMMITTEE:

500+ confirmed PC members! 

VENUE:

“ESCAPE THE HURRYING WORLD – The loveliest spot that man hath ever found…”

Escape to the Lake District, England – a UNESCO World Heritage site – and you’ll find it’s easy to share William Wordsworth’s delight in the area. 

The Wordsworth Hotel & Spa (****)

Address: Grasmere, Ambleside, Lake District, Cumbria, LA22 9SW, England, UK

Phone: +44-1539-435592

Email: enquiry@thewordsworthhotel.co.uk

Web: www.thewordsworthhotel.co.uk

ACCOMMODATION:

ACTIVITIES:

Best WALKS in the Lake District National Park:

LOD 2021 POSTER:

Submit your research work today!

See you in the beautiful Lake District – UK in October!

Best regards, 

  LOD 2021 Organizing Committee

LOD 2021 NEWS:

Past Editions

LOD 2020, The Sixth International Conference on Machine Learning, Optimization and Big Data

Certosa di Pontignano – Siena – Tuscany – Italy. Nature Springer – LNCS volumes 12565 and 12566.

LOD 2019, The Fifth International Conference on Machine Learning, Optimization and Big Data

Certosa di Pontignano – Siena – Tuscany – Italy.

Nature Springer – LNCS volume 11943.

LOD 2018, The Fourth International Conference on Machine Learning, Optimization and Big Data

Volterra – Tuscany – Italy. Nature Springer – LNCS volume 11331.

MOD 2017, The Third International Conference on Machine Learning, Optimization and Big Data

Volterra – Tuscany – Italy. Springer – LNCS volume 10710.

MOD 2016,  The Second International Workshop on Machine learning, Optimization and big Data

Volterra – Tuscany – Italy. Springer – LNCS volume 10122.

MOD 2015, International Workshop on Machine learning, Optimization and big Data

Taormina – Sicily – Italy. Springer – LNCS volume 9432.

Past Keynote Speakers: