Software Engineering for
Machine Learning Applications Symposium

Verification, Validation and Operations of AI Systems with a special focus on AgentOps and Intelligent Systems
June 17th to June 19th 2025
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Uniting Minds to Shape the Future of Data-Driven Software!

SEMLA international symposium aims at bringing together leading researchers and practitioners in software engineering and machine learning to reflect on and discuss the challenges and implications of engineering complex data-intensive software systems.

The SEMLA 2025 symposium focuses on two themes that are redefining how we design, develop, and operate AI-driven software systems.

  1.  AgentOps focuses on the emerging discipline of operationalizing intelligent agents within software pipelines; agents that can not only generate and evaluate code but also support debugging, validation, and continuous deployment.
  2.  Intelligent Systems highlights the role of Digital Twins as powerful tools for modeling, simulating, and optimizing real-world systems in real time. In particular, Digital Twins are becoming essential components in driving sustainable development, offering actionable insights for infrastructure, energy, and environmental applications.
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Historical Context and Ambitions in Machine Learning

From the early attempts in the late 80s (such as the MAIA project) to the most recent breakthroughs in applications of deep learning, humankind dreams of building machines capable of learning new tasks, adapting to the environment, and evolving. Yet this exploration poses important computational, practical, and ethical challenges. Failure to properly address these challenges in such software-intensive systems can lead to catastrophic consequences. Consider, for example, the recent human toll incidence caused by the $47-million Michigan Integrated Data Automated System (MiDAS) (see Broken: The human toll of Michigan’s unemployment fraud saga), or the recent finding that simple tweaks can fool neural networks in identifying street signs (see Robust Physical-World Attacks on Deep Learning Visual Classification).

Ethical and Social Implications of Machine Learning

The increasing concern of machine learning impacting people’s lives found a strong advocate in Prof. David Parnas, who expressed his concern in an ACM communication article. These challenges are also reflected in new IEEE standardization initiatives. With data science and deep learning becoming increasingly pervasive in the contemporary world, it is now imperative to engage software engineers and machine learning experts in in-depth conversations about the necessary perspectives, approaches, and roadmaps to address these challenges and concerns.

New Edge of the Experience!

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Sponsors

Agent operations (AgenOps) and lifecycle management

Interfacing with LLMs and agent-driven user experiences

Modern data platforms that support intelligent querying and data acquisition

Privacy, safety, security issues and ethical concerns

Software-intensive machine learning applications

Cognitive Digital Twins

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