Humanist Discussion Group, Vol. 39, No. 443.
Department of Digital Humanities, University of Cologne
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Submit to: humanist@dhhumanist.org
Date: 2026-05-02 07:55:08+00:00
From: David Silva - IRDTA <david@irdta.eu>
Subject: DeepLearn 2026: early registration May 22
13th INTERNATIONAL SCHOOL ON DEEP LEARNING
DeepLearn 2026
Orléans, France
July 20-24, 2026
https://deeplearn.irdta.eu/2026/
Co-organized by:
University of Orléans
Centre Val de Loire Doctoral College
Institute for Research Development, Training and Advice – IRDTA
Luxembourg/London
Early registration: May 22, 2026
SCOPE:
DeepLearn 2026 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, Luleå, Bournemouth, Bari, 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 healthcare, medical image
analysis, recommender systems, advertising, fraud detection, robotics, games,
business and finance, biotechnology, physics and astrophysics, biometrics,
communications, climate sciences, geographic information systems, signal
processing, genomics, materials design, video technology, social systems, earth
and sustainability, mathematical proofs, etc. etc.
The field is also raising a number of relevant questions about efficiency and
robustness of the algorithms, explainability, transparency, interpretability,
risks and safety, 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 16 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.
ADDRESSED TO:
Graduates, postgraduates and industry practitioners will be typical profiles of
participants. However, there are no formal pre-requisites for attendance in
terms of academic degrees, hence 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 2026 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 2026 will take place in Orléans, located in the heart of the Loire
Valley, which was declared by UNESCO a World Heritage Site in 2000. The venue
will be:
University of Orléans
Faculty of Law, Economics and Management
11 rue de Blois
45100 Orléans, France
https://www.univ-orleans.fr/en
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
industrial 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:
Yingbin Liang (Ohio State University), Convergence Theory: How Fast Do Discrete
Diffusion Models Generate?
Le Song (Mohamed bin Zayed University of Artificial Intelligence), Towards AI-
Driven Digital Organism: A System of Multiscale Foundation Models for Biology
PROFESSORS AND COURSES:
Nitesh Chawla (University of Notre Dame), [intermediate] Synthetic Data
Generation and Learning from Imbalanced Data: From SMOTE to LLMs
Jianfei Chen (Tsinghua University), [intermediate] Efficient Large Model
Training and Inference
Yuejie Chi (Yale University), [introductory/intermediate] Statistical and
Algorithmic Foundations of Reinforcement Learning
Bo Han (Hong Kong Baptist University), [introductory/intermediate] Trustworthy
Machine Learning from Data to Models
Jiawei Han (University of Illinois Urbana-Champaign), [intermediate] Structure-
Guided, Theme-Based Knowledge Discovery with Large Language Models
Mingyi Hong (University of Minnesota), [intermediate] Bilevel Optimization:
Theory, Algorithms, and Applications in Machine Learning and Foundation Models
Cho-Jui Hsieh (University of California Los Angeles), [intermediate/advanced]
Optimizers for Large Language Model Training
Furong Huang (University of Maryland), [advanced] Generative AI Agents
Tara Javidi (University of California San Diego), [intermediate] Active Physical
Intelligence: Integrated Multimodal Sensing, Controlled Inference, and Spatio-
Temporal Attention
Yan Liu (University of Southern California), [intermediate] Time Series
Foundation Models: From Forecasting to Reasoning
Zhijin Qin (Tsinghua University), [intermediate/advanced] Token-Based Semantic
Communications
Aarti Singh (Carnegie Mellon University), [intermediate] Human Centered AI:
Challenges and Opportunities
Suvrit Sra (Technical University of Munich), [introductory/intermediate]
Introduction to the Theory of Learning with Transformers
Ivor Tsang (A*STAR Centre for Frontier AI Research), [introductory/intermediate]
Long-Horizon Agentic Intelligence
Ming-Hsuan Yang (University of California Merced), [advanced] Recent Advances in
Multimodal Understanding and Generation
Tong Zhang (University of Illinois Urbana-Champaign),
[introductory/intermediate] Reinforcement Learning for Large Language Models
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 12, 2026.
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.
Abstracts have to be submitted to david@irdta.eu by July 12, 2026.
HACKATHON:
A hackathon will take place, where participants can voluntarily 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 the end of
August 2026. 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/2026/sponsors/
ORGANIZING COMMITTEE:
Karim Abed-Meraim (Orléans, local co-chair)
Sergei V. Gleyzer (Tuscaloosa, hackathon chair)
Meryem Jabloun (Orléans, local co-chair)
Carlos Martín-Vide (Tarragona, program chair)
Santiago Montes (Tarragona, webpage)
Sara Morales (Luxembourg, finances)
Florian Nowicki (Orléans, social networks)
Philippe Ravier (Orléans, local chair)
David Silva (London, organization chair)
REGISTRATION:
It has to be done at
https://deeplearn.irdta.eu/2026/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 are available at
https://deeplearn.irdta.eu/2026/accommodation/
CERTIFICATE:
A certificate of successful participation will be delivered indicating the
number of hours of academic activities (40). This should be sufficient for those
participants who plan to request ECTS recognition from their home university.
QUESTIONS AND FURTHER INFORMATION:
david@irdta.eu
ACKNOWLEDGMENTS:
Université d’Orléans
Collège Doctoral Centre-Val de Loire
Universitat Rovira i Virgili
Institute for Research Development, Training and Advice – IRDTA,
Luxembourg/London
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