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SADIO – Ultimas vacantes para el Curso Introducción a la Ciencia de Datos
October 18th, 2024 
Daniela Lopez de Luise 
DeepLearn 2025: early registration October 28
October 17th, 2024 Daniela Lopez de Luise 12th INTERNATIONAL SCHOOL ON DEEP LEARNING
(with a special focus on Large Language Models, Foundation Models and Generative AI)
DeepLearn 2025
Porto – Maia, Portugal
July 21-25, 2025
https://deeplearn.irdta.eu/2025/
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Co-organized by:
University of Maia
Institute for Research Development, Training and Advice – IRDTA
Brussels/London
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Early registration: October 28, 2024
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SCOPE:
DeepLearn 2025 will be a research training event with a global scope aiming at updating participants on the most recent advances in the critical and fast developing area of deep learning. Previous events were held in Bilbao, Genova, Warsaw, Las Palmas de Gran Canaria, Guimarães, Las Palmas de Gran Canaria, Luleå, Bournemouth, Bari, Las Palmas de Gran Canaria and Porto.
Deep learning is a branch of artificial intelligence covering a spectrum of current frontier research and industrial innovation that provides more efficient algorithms to deal with large-scale data in a huge variety of environments: computer vision, neurosciences, speech recognition, language processing, human-computer interaction, drug discovery, biomedicine and health informatics, medical image analysis, recommender systems, advertising, fraud detection, robotics, games, business and finance, biotechnology, physics experiments, biometrics, communications, climate sciences, geographic information systems, signal processing, genomics, materials design, video technology, social systems, earth and sustainability, etc. etc.
The field is also raising a number of relevant questions about robustness of the algorithms, explainability, transparency, interpretability, as well as important ethical concerns at the frontier of current knowledge that deserve careful multidisciplinary discussion.
Most deep learning subareas will be displayed, and main challenges identified through 18 four-hour and a half courses, 2 keynote lectures, 1 round table and a hackathon competition among participants. Renowned academics and industry pioneers will lecture and share their views with the audience. The organizers are convinced that outstanding speakers will attract the brightest and most motivated students. Face to face interaction and networking will be main ingredients of the event. It will be also possible to fully participate in vivo remotely.
DeepLearn 2025 will place special emphasis on large language models, foundation models and generative artificial intelligence.
ADDRESSED TO:
Graduate students, postgraduate students and industry practitioners will be typical profiles of participants. However, there are no formal pre-requisites for attendance in terms of academic degrees, so people less or more advanced in their career will be welcome as well.
Since there will be a variety of levels, specific knowledge background may be assumed for some of the courses.
Overall, DeepLearn 2025 is addressed to students, researchers and practitioners who want to keep themselves updated about recent developments and future trends. All will surely find it fruitful to listen to and discuss with major researchers, industry leaders and innovators.
VENUE:
DeepLearn 2025 will take place in Porto, the second largest city in Portugal, recognized by UNESCO in 1996 as a World Heritage Site. The venue will be:
University of Maia
Avenida Carlos de Oliveira Campos – Castêlo da Maia
4475-690 Maia
Porto, Portugal
STRUCTURE:
3 courses will run in parallel during the whole event. Participants will be able to freely choose the courses they wish to attend as well as to move from one to another.
All lectures will be videorecorded. Participants will be able to watch them again for 45 days after the event.
An open session will give participants the opportunity to present their own work in progress in 5 minutes. Also companies will be able to present their technical developments for 10 minutes.
The school will include a hackathon, where participants will be able to work in teams to tackle several machine learning challenges.
Full live online participation will be possible. The organizers highlight, however, the importance of face to face interaction and networking in this kind of research training event.
KEYNOTE SPEAKERS:
Yonina Eldar (Weizmann institute of Science), Model Based AI and Applications
Manuela Veloso (JPMorganChase), AI, Humans, and Robots for Task Solving
PROFESSORS AND COURSES: (to be completed)
Pierre Baldi (University of California Irvine), [intermediate/advanced] From Deep Learning and Transformers to AI Risks and Safety
Sean Benson (Amsterdam University Medical Center), [intermediate] Digital Twins and Generative AI for Personalised Medicine
Mark Derdzinski (Dexcom), [introductory] From Prototype to Production: Evaluation Strategies for Agentic Applications
Samira Ebrahimi Kahou (University of Calgary), [intermediate/advanced] Explainability in Machine Learning
Elena Giusarma (Michigan Technological University), [introductory/intermediate] Machine Learning at the Frontier of Astrophysics: Simulating the Universe
Xia “Ben” Hu (Rice University), [introductory/advanced] Efficient LLM Serving: Algorithms, Systems and Applications
Jayashree Kalpathy-Cramer (University of Colorado), [introductory/intermediate] Multimodal AI for Healthcare
Yingbin Liang (Ohio State University), [intermediate/advanced] Theory on Training Dynamics of Transformers
Chen Change Loy (Nanyang Technological University), [intermediate/advanced] Harnessing Prior for Content Enhancement and Creation
Preslav Nakov (Mohamed bin Zayed University of Artificial Intelligence), tba
Evan Shelhamer (DeepMind), [intermediate] Test-Time Adaptation for Updating Models on New and Different Data
Atlas Wang (University of Texas Austin), [intermediate] Low Rank Strikes Back in the Era of Large Language Models
Xiang Wang (University of Science and Technology of China), [advanced] Large Language Models for User Behavior Modeling: Cross-Modal Interpretation, Preference Optimization, and Agentic Simulation
Rex Ying (Yale University), [intermediate/advanced] Multimodal Foundation Models for Graph-Structured Data: Framework and Scientific Applications
OPEN SESSION:
An open session will collect 5-minute voluntary oral presentations of work in progress by participants.
They should submit a half-page abstract containing the title, authors, and summary of the research to david@irdta.eu by July 13, 2025.
INDUSTRIAL SESSION:
A session will be devoted to 10-minute demonstrations of practical applications of deep learning in industry.
Companies interested in contributing are welcome to submit a 1-page abstract containing the program of the demonstration and the logistics needed. People in charge of the demonstration must register for the event.
Expressions of interest have to be submitted to david@irdta.eu by July 13, 2025.
HACKATHON:
A hackathon will take place, where participants can work in teams to tackle several machine learning challenges. They will be coordinated by Professor Sergei V. Gleyzer (University of Alabama). The challenges will be released 2 weeks before the beginning of the school. A jury will judge the submissions and the winners of each challenge will be announced by August 25, 2025. The winning teams will receive a modest monetary prize and the runners-up will get a certificate.
SPONSORS:
Companies/institutions/organizations willing to be sponsors of the event can download the sponsorship leaflet from
https://deeplearn.irdta.eu/2025/sponsoring/
ORGANIZING COMMITTEE:
Sergei V. Gleyzer (Tuscaloosa, hackathon chair)
José Paulo Marques dos Santos (Maia, local chair)
Carlos Martín-Vide (Tarragona, program chair)
Sara Morales (Brussels)
José Luís Reis (Maia)
Luís Paulo Reis (Porto)
David Silva (London, organization chair)
REGISTRATION:
It has to be done at
https://deeplearn.irdta.eu/2025/registration/
The selection of 6 courses requested in the registration template is only tentative and non-binding. For logistical reasons, it will be helpful to have an estimation of the respective demand for each course.
Since the capacity of the venue is limited, registration requests will be processed on a first come first served basis. The registration period will be closed and the on-line registration tool disabled when the capacity of the venue will have got exhausted. It is highly recommended to register prior to the event.
FEES:
Fees comprise access to all program activities and lunches.
There are several early registration deadlines. Fees depend on the registration deadline.
The fees for on site and for online participation are the same.
ACCOMMODATION:
Accommodation suggestions will be available in due time at
https://deeplearn.irdta.eu/2025/accommodation/
CERTIFICATE:
A certificate of successful participation in the event will be delivered indicating the number of hours of academic activities. This should be sufficient for those participants who plan to request ECTS recognition from their home university.
QUESTIONS AND FURTHER INFORMATION:
ACKNOWLEDGMENTS:
Universidade da Maia
Universidade do Porto
Universitat Rovira i Virgili
Institute for Research Development, Training and Advice – IRDTA, Brussels/London
CFP OLA’2025 Int. Conf. Optimization & Learning @Dubai
October 17th, 2024 Daniela Lopez de Luise OLA'2025
International Conference on Optimization and Learning
23-25 April 2025
Dubai
http://ola2025.sciencesconf.org/
SCOPUS Springer Proceedings
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OLA is a conference focusing on the future challenges of optimization
and/or machine learning methods and their applications. The conference
OLA'2025 will provide an opportunity to the international research
community in optimization and learning to discuss recent research
results and to develop new ideas and collaborations in a friendly and
relaxed atmosphere.
OLA'2025 welcomes presentations that cover any aspects of optimization
and/or machine learning research such as big optimization and learning,
optimization for learning, learning for optimization, optimization and
learning under uncertainty, deep learning, new high-impact applications,
parameter tuning, 4th industrial revolution, computer vision,
hybridization issues, optimization-simulation, meta-modeling,
high-performance computing, parallel and distributed optimization and
learning, surrogate modeling, multi-objective optimization …
Submission papers: We will accept two different types of submissions:
– S1: Extended abstracts of work-in-progress and position papers
of a maximum of 3 pages
– S2: Original research contributions of a maximum of 12 pages
Important dates:
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Paper submission deadline Dec 20, 2024
Notification of acceptance Feb 07, 2025
Proceedings: Accepted papers in categories S1 and S2 will be published
in the proceedings. A SCOPUS and DBLP indexed Springer book will be
published for accepted long papers. Proceedings will be available at the
conference.
1st International Workshop on Security and Truth in the Era of AI-Generated Media Manipulation (STAMM-2025) – 39th AINA-2025 Conference
October 17th, 2024 Daniela Lopez de Luise Special Issue Approved!!!
The authors of chosen papers presented at STAMM-2025 have the opportunity to submit an extended version of their contributions to the Special Issue “Disrupting Truth: Information Disorder in the Age of Generative AI” on Computers and Electrical Engineering (Elsevier). The link to the call for papers will soon be available on the workshop website.
Image and Vision Computing, Special Issue on “Advancing Transparency and Privacy: Explainable AI and Synthetic Data in Biometrics and Computer Vision”
October 17th, 2024 Daniela Lopez de Luise *PAPER SUBMISSION DEADLINE: Feb 7, 2025*
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The rapid advancement of deep learning presents significant challenges, particularly in the realm of ethical data collection and usage. As regulations like the General Data Protection Regulation (GDPR) gain prominence, the need for rigorous ethical standards across various fields becomes increasingly apparent. In domains such as biometrics, data has frequently been collected without appropriate consent or ethical consideration. The medical field, in particular, grapples with the dual challenges of safeguarding privacy and the inherent difficulties of data acquisition.
Ensuring continued progress in deep learning requires that data collection and generation practices adhere to strict ethical guidelines. Greater control over data is essential to enhancing transparency and fairness within deep learning methodologies. Attention must also be given to the explainability of systems trained on generated data, as well as the fairness implications of using synthetic datasets. Addressing these challenges is critical to fostering more ethical and responsible advancements in deep learning.
The Special Issue aims to promote research on the use of Explainable AI and Synthetic Data to enhance transparency and privacy in Biometrics and Computer Vision. Topics include, but are certainly not limited to:
- Responsible image synthesis
- Generative models
- Assessing and comparing AI explanations
- Researching causal learning and inference
- Synthetic medical data with clinical information
- Innovative synthesis of biometric data
- Generating natural language for explanations
- AI transparency and fairness
- Learning from synthetic data
- Interpreting biometric models and vulnerabilities
- Fairness enhancement with synthetic data
= IMPORTANT DATES =
Submission dates:
Final Manuscript Submission Deadline: Feb 7, 2025
Lucia Cascone – University of Salerno, Italy (lcascone@unisa.it)
Zilong Huang – TikTok, Singapore (zilonghuang2020@gmail.com)
= SUBMISSION AND REVIEW DETAILS =
Manuscript submission information:
The Journal's submission system (Editorial Manager) will be open for submissions to our Special Issue from Oct 1st, 2024. Please refer to the Guide for Authors to prepare your manuscript and select the article type of “VSI: XAISynData” when submitting your manuscript online. Both the Guide for Authors and the submission portal could be found on the Journal Homepage: Guide for authors – Image and Vision Computing – ISSN 0262-8856 (elsevier.com).