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added wllfm 2026 page and past events overview
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‎_includes/navigation.html‎

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<li class="dropdown">
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<a href="#" class="dropbtn">Workshops & Special Sessions</a>
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<div class="dropdown-content">
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<a href="{{ site.baseurl }}/wllfm2025/">WLLFM2025 (Bigdata 2025)</a>
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<a href="{{ site.baseurl }}/ssllfm2025/">SSLLFM2025 (DSAA 2025)</a>
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<a href="{{ site.baseurl }}/ssllfm2026/">SSLLFM2026 (DSAA 2026)</a>
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<a href="{{ site.baseurl }}/wllfm2026/">WLLFM2026 (Bigdata 2026)</a>
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<a href="{{ site.baseurl }}/past_events/">Past events</a>
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</div>
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‎past_events/index.md‎

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---
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layout: default
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title: Past Events
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description: Archive of past workshops and special sessions organized by the AML Lab
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---
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<link rel="icon" type="image/x-icon" href="/assets/aml_lab_tight.ico" />
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# Past Events
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An archive of workshops and special sessions organized by the Applied Machine Learning Lab.
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---
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### Workshop on Large Language and Foundation Models (WLLFM 2025)
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**From Theory to Practice: Workshop on Large Language and Foundation Models**
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**Conference**: [BigData 2025](https://conferences.cis.um.edu.mo/ieeebigdata2025) (IEEE International Conference on Big Data)
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**Date**: December 9, 2025
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**Location**: Online
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[Event page]({{ site.baseurl }}/wllfm2025/)
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---
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### Special Session on Large Language and Foundation Models (SSLLFM 2025)
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**From Theory to Practice: Special Session on Large Language and Foundation Models**
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**Conference**: [DSAA 2025](https://dsaa.ieee.org/2025/) (IEEE International Conference on Data Science and Advanced Analytics)
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**Date**: October 9, 2025
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**Location**: Edgbaston Park Hotel, University of Birmingham, Birmingham, UK
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[Event page]({{ site.baseurl }}/ssllfm2025/)
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---
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### Workshop on Large Language and Foundation Models (WLLFM 2024)
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**From Theory to Practice: Workshop on Large Language and Foundation Models**
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**Conference**: [BigData 2024](https://bigdataieee.org/BigData2024/) (IEEE International Conference on Big Data)
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**Date**: December 16, 2024
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**Location**: Hyatt Regency Washington on Capitol Hill, Washington D.C., USA
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[Event page](https://sites.google.com/view/wllfm24)
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---
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### Workshop on Large Language and Foundation Models (WLLFM 2023)
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**From Theory to Practice: Workshop on Large Language and Foundation Models**
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**Conference**: [BigData 2023](https://bigdataieee.org/BigData2023/) (IEEE International Conference on Big Data)
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**Date**: December 15–18, 2023
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**Location**: Sorrento, Italy
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[Event page](https://dhavalrepo18.github.io/bigdatafm/)

‎wllfm2026/index.md‎

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---
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layout: default
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title: Workshop on Large Language and Foundation Models 2026
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description: Co-located with IEEE BigData 2026
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---
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# Fourth Workshop on Large Language and Foundation Models (WLLFM 2026)
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**Location**: Sheraton Phoenix Downtown, Phoenix, AZ, USA
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**Conference**: [BigData 2026](https://bigdataieee.org/BigData2026/) (IEEE International Conference on Big Data)
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**Date**: **December 14th–17th, 2026**
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Large language models (LLMs) and foundation models (FMs) have rapidly emerged as pivotal technologies in data science and analytics, offering unprecedented capabilities in text generation, knowledge extraction, and complex decision-making. However, a significant gap remains between the rapid theoretical advancements in these models and their robust, scalable deployment in industrial environments.
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This workshop seeks to bridge cutting-edge theory with real-world applications, providing a venue for researchers and practitioners to exchange novel methodologies, deployment strategies, and impact-driven insights. By spotlighting both breakthrough techniques and operational challenges (such as scalability, interpretability, and ethics), the session aims to foster cross-pollination of ideas and accelerate the seamless integration of large language models into diverse data-driven ecosystems.
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- Submission Deadline: **TBA** (tentative: early October 2026)
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- Paper Notification: **TBA** (tentative: early November 2026)
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- Paper Camera-Ready: **November 13th, 2025**
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- Contact: `amllab[at]bit.uni-bonn.de`
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## Submission
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Submission Link: [https://wi-lab.com/cyberchair/2026/bigdata26/scripts/submit.php?subarea=S22&undisplay_detail=1&wh=/cyberchair/2026/bigdata26/scripts/ws_submit.php](https://wi-lab.com/cyberchair/2026/bigdata26/scripts/submit.php?subarea=S22&undisplay_detail=1&wh=/cyberchair/2026/bigdata26/scripts/ws_submit.php)
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Papers should be submitted single blind.
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Paper formats are:
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- Long papers: up to 10 pages including all figures, tables, and references
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- Short and vision papers: up to 5 pages including all figures, tables, and references
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- Papers should be submitted single blind.
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All papers must be submitted in the IEEE conference format:
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- Official templates: [https://www.ieee.org/conferences/publishing/templates.html](https://www.ieee.org/conferences/publishing/templates.html)
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- Overleaf templates: [https://www.overleaf.com/latex/templates/ieee-conference-template/grfzhhncsfqn](https://www.overleaf.com/latex/templates/ieee-conference-template/grfzhhncsfqn)
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## Call for Papers
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The topics of interest are, but not limited to:
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- State-of-the-Art Model Research:
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- Model training, optimization, and architecture
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- Architectures beyond the Transformer: state-space models, linear-attention variants, hybrid designs, and diffusion language models
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- Reasoning models and test-time compute scaling
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- Systems and Efficiency:
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- Inference at scale: efficiency, latency, memory use, routing, and serving-time optimization for large models, including long-context and sparse architectures
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- Efficient fine-tuning, adaptation, and post-training of LLMs or FMs: supervision design, synthetic data, reward signals, preference optimization, distillation, and curated training pipelines
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- Industrial use cases: challenges in latency, cost-optimization, and hardware constraints
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- Retrieval and Context Management:
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- How models obtain, select, organize, and use external information through retrieval, reranking, and context construction
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- Retrieval-Augmented Generation (RAG) in enterprise environments
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- Agentic and Multi-Agent Systems:
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- Multi-agent LLM systems and orchestration: role specialization, agent-to-agent protocols, and standards such as MCP for tool integration
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- Autonomous software-engineering agents and LLM-driven code generation in production codebases
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- Multimodal and Domain-Specific Foundation Models:
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- Multimodal and any-to-any foundation models spanning vision, audio, and video
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- Foundation models for structured big-data modalities, including time series, tabular, graph, and geospatial data
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- Domain-specific foundation models for finance, healthcare, legal, and scientific discovery, with specialized constraints and metrics
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- Quantum Foundation Models
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- Privacy, Safety, and Ethics:
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- Privacy-preserving and federated training or inference for LLMs and FMs
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- Evaluation and trustworthiness: robustness, reliability, safety, long-context behavior, and "in-the-wild" usefulness beyond narrow benchmark scores
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- Interpretability and explainability of foundation models in decision-critical systems
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- Ethical, societal, and regulatory considerations in LLM adoption
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## Proceedings and Indexing
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All accepted workshop papers will be published by IEEE in the BigData 2026 Proceedings and will be submitted for inclusion in the IEEEXplore Digital Library.
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## Organizers
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- Prof. Dr. Rafet Sifa (University of Bonn / Fraunhofer IAIS, Germany)
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- Dr. Tobias Deußer (University of Bonn, Germany)
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- Prof. Dr. Aurelio Bariviera (University of Rovira i Virgili, Spain)
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- Dr. Dhaval Patel (IBM Research, USA)
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- Prof. Dr. Wei Liu (University of Technology Sydney, Australia)
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- Dr. Lorenz Sparrenberg (University of Bonn, Germany)
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- Dr. Linsey Pang (PayPal, USA)
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- Dr. Thore Gerlach (European Space Agency, Netherlands)
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This workshop has been partially funded by the Federal Ministry of Education and Research of Germany and the state of North-Rhine Westphalia as part of the Lamarr-Institute for Machine Learning and Artificial Intelligence.
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## Previous iterations
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- [SSLLFM 2026](https://appliedmachinelearning-lab.github.io/ssllfm2026/)
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- [WLLFM 2025](https://appliedmachinelearning-lab.github.io/wllfm2025/)
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- [SSLLFM 2025](https://appliedmachinelearning-lab.github.io/ssllfm2025/)
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- [WLLFM 2024](https://sites.google.com/view/wllfm24)
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- [WLLFM 2023](https://dhavalrepo18.github.io/bigdatafm/)

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