CHARAL 2025

Challenges in Holistic AI for Real-world Applications and Learning Systems

"From Algorithms and Models to Real-world AI Integration"

Workshop at MICAI 2025

Date: November 4th, 2025

Location: Guanajuato, Mexico. Centro de Investigación en Matemáticas (CIMAT)

Format: Hybrid (In-person & Virtual)

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About CHARAL

As AI technologies mature, the focus is shifting from model development to their effective deployment in real-world systems. In domains such as robotics, manufacturing, healthcare, and intelligent infrastructure, integrating AI into production environments introduces unique challenges related to system design, latency, reliability, monitoring, and trust.

The 1st CHARAL workshop at MICAI 2025 will provide a forum for researchers and practitioners to explore holistic approaches to engineering and operating AI systems that are robust, explainable, and adaptable in complex, real-world conditions.

Systems Engineering for AI Deployment

Monitoring & Observability

Trust & Safety

Data-Centric AI in Production

Specialized Deployment Challenges (Foundational models, AI on Edge, Robotics)

Call for Papers

We invite researchers and practitioners to submit papers addressing the challenges of deploying AI systems in real-world environments. We welcome both theoretical contributions and practical case studies. The technical paper should be written in English in LNCS Springer style, not exceeding 8 pages. The submissions must not contain author's names or affiliations.

The accepted papers will be published in the Springer LNAI Proceedings, and the author fee amount for presentation at MICAI conference and publication is $5,000 MXN (approximately $300 USD). Submission should be made through the CMT system at:

Submit Your Paper

Important Dates

Paper Submission Deadline: September 5th, 2025

Notification of Acceptance: September 19th, 2025

Camera-Ready Deadline: September 26th, 2025

Workshop Date: November 4th, 2025

Systems Engineering for AI Deployment

Architectural patterns, system constraints management, fail-safe design, and graceful degradation in AI pipelines.

Monitoring, Observability & Operational Feedback

Observability instrumentation, logging and tracing, debugging techniques, and performance feedback loops.

Trust, Safety & Assurance in Deployed Models

Uncertainty estimation, explainability methods, red-teaming, adversarial robustness, and evaluation metrics.

Data-Centric AI in Production

Real-world data pipelines, feedback-driven collection, active learning, and weak supervision techniques.

Foundation Models in Production

Adapting large models for latency-sensitive environments, robotics, and manufacturing systems.

AI on Edge & Embedded Systems

Quantization, pruning, distillation, streaming inference, and federated updates for resource-constrained environments.

Organizing Team

Chair Photo

Arturo Gomez Chavez

Workshop Chair
Constructor University Bremen
Co-Chair Photo

Dr. Emmanuel Ovalle-Magallanes

Co-Chair
Universidad de La Salle Bajio
PC Member Photo

Dr. Jose Martinez Carranza

Co-Chair
Instituto Nacional de Astrofisica Optica y Electronica (INAOE)

Program

To be adjusted based on accepted papers and speakers.

09:00 - 09:30
Opening & Welcome
Workshop introduction and overview of the day's activities
09:30 - 10:30
Keynote: Real-world AI Deployment Challenges
Distinguished speaker presentation on current challenges in deploying AI systems in production environments
10:30 - 11:00
Coffee Break
Networking and discussion
11:00 - 12:30
Session 1: Systems Engineering and Architecture
Paper presentations on architectural patterns, system constraints, and fail-safe design
12:30 - 14:00
Lunch Break
Lunch and networking
14:00 - 15:30
Session 2: Monitoring and Trust
Paper presentations on observability, safety, and assurance in deployed models
15:30 - 16:00
Coffee Break
Networking and discussion
16:00 - 17:00
Panel Discussion: Future of AI in Production
Expert panel discussing emerging trends and future challenges
17:00 - 17:30
Closing Remarks
Workshop summary and next steps