Dear Colleague,

I am pleased to invite you to submit your valuable research to the 2026 International Conference on Next-Generation AI Systems (NGEN-AI 2026).

NGEN-AI 2026 brings together researchers, practitioners, and industry leaders working on the next wave of artificial intelligence, including foundational models, generative AI, agentic AI, federated learning, deep learning, explainable and trustworthy AI, and edge/cloud AI systems.

Please find the Call for Papers below for full details. We look forward to receiving your submission and hope to welcome you to Trento, Italy.

Best regards,
Fahed Alkhabbas
On behalf of the NGEN-AI 2026 organizing committee

---

Note: If you received multiple copies of this CFP, please accept my apologies. If you prefer not to receive further messages about NGEN-AI 2026, please reply with unsubscribe (or email <a href="mailto:fahed.alkhabbas@mau.se?subject=Unsubscribe%20NGEN-AI%202026" title="mailto:fahed.alkhabbas@mau.se?subject=Unsubscribe%20NGEN-AI%202026” style=”color:rgb(153,0,250);text-decoration:none” target=”_blank”>unsubscribe request) and I will remove your address from our outreach list.

You are receiving this invitation because your published work appears relevant to the conference scope.

CFP: The 2026 International Conference on Next-Generation AI Systems (NGEN-AI 2026)

Springer CCIS Proceedings

https://ngen-ai.org/

Theme: One conference for every AI direction: Foundational models, Generative, Agentic, Federated, and Deep Learning, XAI, Trust, and Edge Intelligence.

Venue: Trento, Italy     Dates: 1–4 September 2026

Scope

We invite high-quality, original contributions that advance the theory, engineering, and real-world impact of Next Generation AI Systems—spanning federated and distributed intelligence; small, large, and generative models; agentic and interactive AI; deep learning and representation learning; explainability and transparency; trustworthy, responsible, and sustainable AI; MLOps and lifecycle management; AI systems and infrastructures; and application-driven research with societal impact.

NGEN-AI 2026 brings together researchers, practitioners, and industry leaders working on the next wave of artificial intelligence. The conference provides a platform for interdisciplinary collaboration, bridging theoretical foundations and practical implementations in intelligent, trustworthy, and sustainable AI systems deployed across diverse domains and real-world environments.

Indexing

All accepted papers will be published in the Springer CCIS series, indexed in leading databases including SCOPUS, Norwegian Register for Scientific Journals and Series, DBLP, EI Compendex, INSPEC, SCImago, zbMATH, and the Japanese Science and Technology Agency (JST).

General Chairs

• Marco Roveri, University of Trento, Italy
• Sadi Alawadi, Blekinge Institute of Technology, Sweden

Topics of Interest

The NGEN-AI conference welcomes research, experience, and vision papers that explore foundational methods, systems, and applications of next generation AI. Topics of interest for each track include, but are not limited to, the following.

Federated Learning

• Architectures for cross-device and cross-silo federated learning
• Federated optimization under non-IID, sparse, or unbalanced data distributions
• Personalized and on-device adaptation strategies in federated settings
• Communication-efficient FL (compression, sparsification, update scheduling)
• Privacy-preserving FL: secure aggregation, differential privacy, homomorphic encryption
• Robustness to poisoning, backdoor, and Byzantine attacks in federated scenarios
• Energy- and resource-aware FL on mobile, edge, and IoT devices
• Federated learning in vertical, horizontal, and hybrid data partitioning settings
• Federated analytics and federated evaluation techniques
• MLOps for FL: lifecycle management, monitoring, and deployment at scale
• Benchmarking, simulators, datasets, and reproducibility studies for FL
• Real-world applications in healthcare, finance, smart industry, and smart cities
• Regulatory, ethical, and governance aspects of federated and collaborative learning

Small & Large Language Models and Generative AI

• Architectures and training recipes for SLMs, LLMs, and foundation models
• Pre-training, instruction-tuning, alignment (e.g., RLHF, DPO, preference optimization)
• Domain-specific and compact SLMs for on-device and resource-constrained settings
• Prompt engineering, in-context learning, function calling, and tool-augmented pipelines
• Retrieval-augmented generation and knowledge-grounded generative models
• Generative models for text, code, images, audio, video, and multimodal content
• Model compression, distillation, quantization, and sparsity for efficient deployment
• Edge and on-device deployment of SLMs/LLMs and generative models
• Safety, robustness, and red-teaming of generative systems (toxicity, hallucinations, bias)
• Evaluation methodologies, benchmarks, and human-in-the-loop assessment
• Generative AI for scientific discovery, simulation, and data augmentation
• Software engineering with LLMs: code generation, refactoring, testing, and verification
• Governance, transparency, IP, and regulatory aspects of foundation and generative models

Deep Learning Architectures & Representation Learning

• Novel neural architectures (transformers, graph neural networks, diffusion models, etc.)
• Self-supervised, contrastive, and representation learning at scale
• Multimodal learning and fusion of heterogeneous data sources
• Curriculum learning, meta-learning, and continual / lifelong learning
• Robust and certified deep learning under distribution shift and adversarial attacks
• Interpretable and explainable deep learning methods
• Data-centric AI: dataset curation, quality, and augmentation strategies
• Efficient training and inference: pruning, low-rank adaptation, and sparse models
• Neural architecture search and automated model design
• Applications of deep learning in vision, language, time series, recommender systems, and beyond

Agentic AI

• Architectures for autonomous, semi-autonomous, and mixed-initiative agents
• Planning, reasoning, and long-horizon decision making for agentic systems
• Reinforcement learning, hierarchical RL, and model-based control for agents
• LLM-driven agents, tool-using agents, and workflow / task orchestration
• Multi-agent systems: coordination, negotiation, communication, and cooperation
• Human-agent interaction, explainability, and trust in agentic AI systems
• Safety, verification, alignment, and oversight for autonomous agents
• Simulation environments, digital twins, and benchmarks for agentic AI
• Agents in robotics, autonomous vehicles, logistics, smart grids, and IoT environments
• Social, economic, and ethical implications of pervasive agentic AI
• Engineering methodologies, software frameworks, and tooling for large-scale agent systems
• Hybrid symbolic-subsymbolic approaches for reasoning and acting

MLOps, AI Engineering & Lifecycle Management

• MLOps platforms and infrastructure for scalable training and deployment
• CI/CD for ML, continuous training, and continuous evaluation
• Data and feature management: data versioning, feature stores, and lineage tracking
• Monitoring, observability, and incident response for AI systems
• Model governance, risk management, and compliance (e.g., AI Act, sectoral regulation)
• Testing, debugging, and quality assurance for ML components and pipelines
• Infrastructure for serving LLMs and generative models at scale
• Cost- and energy-aware deployment and scheduling of AI workloads
• Organizational processes and roles for AI/ML teams
• Case studies and lessons learned from real-world AI production deployments

Explainable AI (XAI) & Transparency

• Post-hoc explanations (e.g., feature attribution, saliency, local surrogate models)
• Intrinsic interpretability and transparent model design
• Counterfactual and contrastive explanations
• Uncertainty estimation, calibration, and communicating confidence to users
• Explainability for LLMs and generative AI (faithfulness, grounding, rationale analysis)
• Explainability in federated, privacy-preserving, and edge AI settings
• Explainable decision making for agentic and multi-agent systems
• Human-centered explanation design, usability, and user studies
• Evaluation and benchmarking of explanations (faithfulness, robustness, usefulness)
• Auditing, debugging, and root-cause analysis for AI systems
• Transparency documentation (e.g., model cards, datasheets) and reporting standards
• Regulatory, ethical, and governance aspects related to transparency and explainability

Trustworthy, Responsible & Sustainable AI

• Trustworthiness by design: safety, reliability, and robustness under distribution shift
• Fairness, bias mitigation, and inclusive AI across populations and contexts
• Accountability, transparency, and auditability in AI systems
• Human values and alignment: human-centered objectives, oversight, and control
• Responsible AI governance: policies, risk management, and compliance practices
• Privacy, security, and protection against adversarial and data poisoning attacks
• Evaluation frameworks, metrics, and benchmarks for trustworthy and responsible AI
• Monitoring and lifecycle management for responsible AI in production
• Sustainable AI: energy-efficient training/inference, green AI, and carbon-aware operation
• Responsible data practices: provenance, consent, documentation, and data stewardship
• Socio-technical studies of AI adoption, impact, and organizational readiness
• Case studies and lessons learned from responsible and sustainable AI deployments

AI Systems, Hardware & Edge/Cloud Infrastructures

• Distributed and parallel systems for large-scale training and inference
• Scheduling and placement of AI workloads across edge, fog, and cloud
• Hardware accelerators (GPUs, TPUs, NPUs, FPGAs) and co-design for AI
• Systems support for LLMs and foundation models (sharding, offloading, caching)
• Energy-efficient and green AI computing, including carbon-aware orchestration
• Runtime systems, compilers, and libraries for AI workloads
• Edge AI and embedded AI for IoT, CPS, and real-time applications
• Resilience, fault tolerance, and reliability of AI systems and infrastructures
• Benchmarks, performance analysis, and optimization of AI systems

Applications & Societal Impact of Next Generation AI

• Next generation AI applications in healthcare, finance, education, mobility, and industry
• AI for sustainability, climate, energy, and environmental monitoring
• Human-AI collaboration, co-creation, and augmented decision making
• Fairness, accountability, transparency, and ethics in AI systems
• Regulation, standards, and governance frameworks for AI
• Socio-technical analyses of AI deployment and organizational transformation
• User studies, field deployments, and longitudinal evaluations
• Public sector and civic applications of AI (e-government, public services, smart cities)
• Education, upskilling, and capacity building for AI-literate societies

Submission Types

• Long Papers (16 pages): original research with clear methodology, results, and contributions.
• Short Papers (8 pages): short research contributions, focused studies, and demo or artifact papers.
• Poster Papers (6 pages): concise presentations of work in progress and undergraduate research.

Important Dates

• Paper submission deadline: May 25, 2026
• Notification of acceptance: July 15, 2026
• Camera-ready submission: August 10, 2026
• Conference dates: September 1–4, 2026 (Trento, Italy)

All deadlines are in Anywhere on Earth (AoE) time.

Submission Portal

Submissions are handled via EasyChair.

For submission guidelines and the submission link, please visit: https://ngen-ai.org/index.php

Contact Information

For questions about submissions, please contact me:

• <a href="mailto:fahed.alkhabbas@mau.se" title="mailto:fahed.alkhabbas@mau.se” style=”color:rgb(153,0,250);text-decoration:none” target=”_blank”>fahed.alkhabbas@mau.se

We look forward to receiving your contributions and to welcoming you at NGEN-AI 2026 in Trento, Italy!

13th INTERNATIONAL SCHOOL ON DEEP LEARNING

DeepLearn 2026

Orléans, France

July 20-24, 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: June 19, 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 15 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

STRUCTURE:

2 or 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] Trustworthy Agentic Artificial Intelligence

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

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

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

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:

ACKNOWLEDGMENTS:

Université d’Orléans

Collège Doctoral Centre-Val de Loire

Universitat Rovira i Virgili

Institute for Research Development, Training and Advice – IRDTA, Luxembourg/London