Hybrid Conference (In Person / Virtual)

International Conference on Statistical Inference in Machine Learning and AI (ICSIMLAI - 27)
23rd - 24th January 2027 , Munich, Germany
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Conference Notifications:

"Be sure to check this section regularly for all IIRD Global Conference updates. We’ll keep you informed about deadlines, event details, and more important notifications."

  • Abstract Submissions Open:
    "We are now accepting abstract submissions for the IIRD Global Conference in Munich. Submit your abstract by today to be part of this exciting international event." Call for Abstracts – Submit Now!
  • Paper Submission Guidelines:
    "Please ensure your paper follows the submission guidelines for smooth processing. Visit our paper submission page for more details."
  • Peer Review Process:
    "The peer review process will begin soon. Authors will receive valuable feedback that will help enhance their presentations for the Munich conference."
  • Networking with Global Experts:
    "Join global experts at our conference in Munich and engage in meaningful conversations. Build relationships that last." Register now to join!
  • Best Paper Presentation Award:
    Dr. Shatha Hawarna from Hamdan Bin Mohammed Smart University received the Best Paper Presentation Award at (ICBAI-25) in Bangkok, Thailand.
  • SDG-Inspired Conference Focus:
    "The conference will explore themes related to sustainability and inclusive development, with an emphasis on research that contributes to global well-being."

Call For Papers

The ICSIMLAI provides a supportive platform for both experienced researchers and early-career academicians to present their work and gain recognition.

The conference covers diverse topics such as Statistics, Data Science, encouraging participation from emerging researchers and fostering academic growth.

Authors are invited to submit papers addressing, but not limited to, the following areas:

01
Statistical inference in machine learning
02
Bayesian statistics for AI applications
03
Statistical challenges in deep learning
04
Causal inference in machine learning models
05
Statistical methods for model evaluation
06
Feature selection techniques in AI
07
Statistical learning theory and applications
08
Data preprocessing for machine learning
09
Statistical frameworks for AI ethics
10
Statistical tools for big data analytics
11
Statistical methods for reinforcement learning
12
Interpretability of machine learning models
13
Statistical issues in data privacy
14
Statistical modeling of complex systems
15
Statistical techniques for time series analysis
16
Unsupervised learning and statistical methods
17
Statistical evaluation of AI systems
18
Transfer learning in statistical contexts
19
Statistical power analysis in AI studies
20
Statistical education for machine learning

Assessment

Submissions will be reviewed to ensure quality and relevance, with a focus on encouraging emerging research contributions. Accepted papers will be presented and considered for publication opportunities.

Registration

Early-career researchers are encouraged to register and present their work, gaining valuable feedback and academic exposure.

Publication

The conference provides opportunities for emerging researchers to publish their work in recognized platforms.

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