Merge pull request #4 from PriyaTomar/dsaa2026

added ssllm226 webpage

Author: tobideusser

ssllfm2026/index.md

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+---
+layout: default
+title: Special Session on Large Language and Foundation Models 2026
+description: Co-located with DSAA 2026
+---
+<link rel="icon" type="image/x-icon" href="/assets/aml_lab_tight.ico" />
+
+# From Theory to Practice: Special Session on Large Language and Foundation Models
+
+**Location**: Pride Plaza Hotel, Aerocity, New Delhi, India  
+**Conference**: [DSAA 2026](https://dsaa2026.dsaa.co/) (IEEE International Conference on Data Science and Advanced Analytics)  
+**Date**: October 6–9, 2026 (special session slot — *to be announced*)
+
+Foundation models and large language systems have become indispensable technologies in data science and analysis,
+opening up powerful possibilities in the areas of text generation, knowledge extraction, and complex decision-making.
+This special session bridges the gap between theoretical breakthroughs and practical applications, creating a platform
+for researchers and practitioners to present innovative methods, exchange deployment strategies, and discuss actionable
+insights. By focusing on both technology and practical challenges, the session promotes interdisciplinary exchange,
+drives research momentum, and identifies effective approaches for embedding large language models in data-driven
+applications across various fields.
+
+- Important dates (submission, notification, camera-ready): see the **[DSAA 2026](https://dsaa2026.dsaa.co/)** website.
+- Contact: `amllab[at]bit.uni-bonn.de`
+
+## Aims and Scope
+
+This special session examines the deployment of large language and foundation models across diverse application areas:
+
+- Showcase state-of-the-art developments in model architecture, optimization, and computational approaches.
+- Present practical implementations and challenges encountered when adopting large language models in industrial applications.
+- Enable interdisciplinary collaborations that merge fundamental research insights with operational deployment strategies.
+- Provide a collaborative space to deliberate on ethics, privacy, social impact, and compliance requirements stemming from large language and foundation model implementations.
+
+## Agenda
+
+*Program for SSLLFM 2026 will be announced closer to the conference.*
+
+| Time | Paper / Speaker | Presenter |
+|------|-----------------|-----------|
+| TBA  | To be announced | —         |
+
+## Keynotes
+
+*Keynotes for SSLLFM 2026 will be announced later.*
+
+## Submission
+
+To submit a paper to SSLLFM2026, go to [OpenReview (IEEE DSAA 2026 Conference)](https://openreview.net/group?id=IEEE.org/DSAA/2026/Conference),
+and select the "Special Session: From Theory to Practice: Special Session on Large Language and Foundation Models"
+track when it is available.
+
+The length of each paper submitted to SSLLFM2026 should be no more than ten (10) pages and should be formatted
+following the standard 2-column U.S. letter style of IEEE Conference template. For further information and
+instructions, see the [IEEE Proceedings Author Guidelines](https://www.ieee.org/conferences/publishing/templates.html).
+
+All submissions will be double-blind reviewed by the Program Committee based on technical quality, relevance to the
+special session's topics of interest, originality, significance, and clarity. Author names and affiliations must not
+appear in the submissions, and bibliographic references must be adjusted to preserve author anonymity. Submissions
+failing to comply with paper formatting and authors anonymity will be rejected without reviews.
+
+Because of the double-blind review process, non-anonymous papers that have been issued as technical reports or similar
+cannot be considered for SSLLFM2026. An exception to this rule applies to arXiv papers that were published in arXiv at
+least a month prior to SSLLFM2026 submission deadline. Authors can submit these arXiv papers to SSLLFM2026 provided
+that the submitted paper's title and abstract are different from the one appearing in arXiv.
+
+## Call for Papers
+
+The topics of interest are, but not limited to:
+
+- Model Training and Optimization:
+  - Techniques to deal with hallucinations
+  - Training data for LLMs
+  - Efficient and stable techniques for training and finetuning LLMs
+  - Scalable approaches for distributed model training
+  - Middleware for scale out data preparation for LLM training
+  - Workflow orchestration for end-to-end LLM life cycle
+  - Resource management for compute and energy efficient model training
+  - Representation learning
+- Model Utilization and Integration:
+  - Using LLMs effectively as tools for Reinforcement Learning or search
+  - Enhancing LLM capabilities by using external tools such as search engines
+  - Visual Prompt Tuning and in-context learning
+  - Enable easy experimentation with high utilization to train foundational models in the cloud
+  - Strategies to scale resources for training/fine-tuning foundational models
+  - Instruction tuning including generation of instruction tuning data
+  - Parallel training: data model tensor (attention and weights)
+  - Distributed workflows for data cleansing and model usage (Langchain)
+  - Principled AI
+  - Investigating reasoning capabilities of LLMs
+  - Retrieval Augmented Generation
+  - Alternative architectures such as State Space Models
+- Compact Language Models and Knowledge Distillation:
+  - Knowledge representations for training small/compact language models
+  - Evaluation of different teacher-student distillation and model compression strategies
+  - Techniques for efficient data encoding to maintain linguistic properties in compact models
+  - Deployment of lightweight models in resource-constrained environments
+  - Case studies on the effectiveness in various NLP tasks
+- Application-Specific Models:
+  - Math LLMs
+  - Multimodal Foundation Models
+  - Trustworthy Foundation Models
+  - Large-scale Visual Foundation Models
+  - Timeseries foundation models for forecasting, prediction and control
+  - Multi-Agent System using LLMs
+  - Recommender systems using LLMs
+  - Knowledge management using LLMs
+- Knowledge Incorporation and Adaptation:
+  - Approaches to deal with knowledge recency to effectively update knowledge within LLMs
+  - Incorporating domain knowledge in LLMs
+- Evaluation and Benchmarking:
+  - Additional benchmarks to fill gap between human and automatic reference-based evaluation
+
+## Proceedings and Indexing
+
+All accepted full-length special session papers will be published by IEEE in the DSAA main conference proceedings under
+its Special Session scheme. All papers will be submitted for inclusion in the IEEEXplore Digital Library.
+
+## Previous Editions
+
+- **[SSLLFM 2025](https://appliedmachinelearning-lab.github.io/ssllfm2025/)** — Special Session at IEEE DSAA 2025, Birmingham, UK. 35 submissions, 8 accepted papers, 60+ participants.
+- **[WLLFM 2025](https://appliedmachinelearning-lab.github.io/wllfm2025/)** — Workshop at IEEE BigData 2025, Macau SAR, China. 29 submissions, 5 accepted papers, 50+ participants.
+- **[WLLFM 2024](https://sites.google.com/view/wllfm24)** — Workshop at IEEE BigData 2024, Washington DC, USA. 55 submissions, 19 accepted papers, 100+ participants.
+- **[WLLFM 2023](https://dhavalrepo18.github.io/bigdatafm/)** — Workshop at IEEE BigData 2023, Sorrento, Italy. 31 submissions, 11 accepted papers, 50+ participants.
+
+## Organizers
+
+**Prof. Dr. Rafet Sifa** *(Contact Person)*  
+University of Bonn, Germany · `rafet.sifa@bit.uni-bonn.de`  
+Prof. Dr. Rafet Sifa is a leading researcher in AI and machine learning, with over 15 years of experience and a
+regular contributor to the IEEE DSAA conference. His research focuses on hybrid deep learning and large-scale
+analytics, with extensive publications on both theoretical and applied machine learning topics with a deep focus on
+representation learning. He co-organized the special session on Informed and Explainable Methods for Machine Learning
+at ICANN 2019, the three workshops on foundational and large language models at IEEE BigData (2023, 2024, 2025), a
+special session on Large Language and Foundation Models at IEEE DSAA 2025, and workshops on Bridging Neurons and
+Symbols for NLP and Knowledge Graphs Reasoning at COLING 2024 and 2025.
+
+**Prof. Dr. Wei Liu**  
+University of Technology Sydney, Australia · `wei.liu@uts.edu.au`  
+Wei Liu is an Associate Professor of Machine Learning and Director of the Future Intelligence Research Lab at UTS. He
+holds a PhD in Machine Learning from the University of Sydney. His research spans generative AI, adversarial machine
+learning, cybersecurity, game theory, multimodal learning, NLP, and intrusion detection. He has earned 3 Best Paper
+Awards and a Most Influential Paper Award at PAKDD, and serves as senior PC member and area chair at KDD, AAAI, and
+ICDM.
+
+**Dr. Dhaval Patel**  
+IBM Research, USA · `dhaval.patel@ibm.com`  
+Dr. Dhaval Patel is a research scientist specializing in AI model optimization and industrial applications. His work
+bridges fundamental research and real-world deployment, focusing on scalable machine learning solutions. He
+co-organized the previous workshops on foundational and large language models at IEEE BigData as well as the special
+session at DSAA 2025.
+
+**Dr. Lorenz Sparrenberg**  
+University of Bonn, Germany · `lsparren@uni-bonn.de`  
+Dr. Lorenz Sparrenberg's research focuses on large language models and their evaluation, robustness, and limitations.
+His recent work includes research on efficient inference of LLMs and empirical studies on their behavior in real-world
+settings, as well as publications on representative learning for clinical and decision support applications including
+dementia detection and diabetic retinopathy.
+
+**Priya Priya**  
+University of Bonn, Germany · `ppriya@uni-bonn.de`  
+Priya is a data scientist at Fraunhofer IAIS and a PhD candidate at the University of Bonn focusing on deep
+learning-based medical image analysis, in particular Surgical AI. Her work addresses domain-specific challenges in the
+surgical domain by developing data-driven and application-oriented methods to enhance clinical applicability. Her
+recent publications focus on semantic segmentation for robot-assisted abdominal surgery.
+
+## Program Committee
+
+- Lucie Flek — *Lamarr Institute for Artificial Intelligence and Machine Learning*, Germany
+- Christian Bauckhage — *Lamarr Institute for Artificial Intelligence and Machine Learning*, Germany
+- Ozlem Uzuner — *George Mason University*, USA
+- Lorenz Sparrenberg — *University of Bonn*, Germany
+- Priya Priya — *University of Bonn*, Germany
+- Tobias Deußer — *University of Bonn*, Germany
+- Armin Berger — *University of Bonn*, Germany
+- Manuela Bergau — *Fraunhofer IAIS*, Germany
+- Farizeh Aldabbas — *Fraunhofer IAIS*, Germany
+- Johannes Radu Hübers — *Fraunhofer IAIS*, Germany
+- Aashish Jain — *Salesforce*, USA
+- Zian Wang — *Stony Brook University*, USA
+- Qiushui Xu — *Penn State University*, USA
+- Qikai Yang — *University of Illinois Urbana-Champaign*, USA
+- Zheng Liu — *Northeastern University*, USA
+- Tingting Tang — *University of Southern California*, USA
+- Bo Yuan — *Georgia Institute of Technology*, USA
+- Yunzhe Wang — *University of Southern California*, USA
+- Yong Liu — *Salesforce*, USA
+- Mounika Kamsali Veera — *Walmart*, USA
+- Lisa Pucknat — *AXA*, Germany
+- Pengfei Li — *Visa Research*, USA
+- Surya Lakshmi Sujitha Pasumarty — *Albertsons*, USA
+- Yingfan Wang — *Duke University*, USA
+- Tian Long Xu — *Squirrel AI Learning*, USA
+- Hao Yan — *George Mason University*, USA
+- Mingxuan Yang — *Brown University*, USA
+- Dezhi Yu — *University of California, Berkeley*, USA
+- Haodong Zhang — *New York University*, USA