Hybrid Conferencee

International Conference on Statistical Inference in Machine Learning and AI (ICSIMLAI - 26)

28th - 29th August 2026 | Toronto, Canada

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Conference Notifications:

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Call for Papers Extended:
"The deadline for full paper submissions has been extended for the Research Plus International Conference in Toronto. Submit your research by today to participate in one of the top conferences."
Certificate of Presentation:
"Present your research and receive a Certificate of Presentation to recognise your valuable contribution to the conference."
Abstract Submissions Open:
"Abstract submissions for the Toronto event are now open! Don’t miss the chance to present your research. Submit now."
Networking with Global Experts:
"Engage with researchers and professionals from around the world at the Toronto conference. Build collaborations and gain insights from leading experts."
Keynote Speaker Sessions:
"Don’t miss our Keynote Sessions in Toronto, featuring global leaders and innovators sharing their knowledge."
Best Paper & Best Paper Presentation Award:
"Submit your paper and stand a chance to win the Best Paper Presentation Award. The winner will be recognized at the conference in Toronto."
SDG-Inspired Conference Focus:
"Our conference will highlight research that addresses global sustainability, inclusive education, and solutions for environmental challenges."

Call for Paper

The ICSIMLAI aims to explore emerging trends and future directions in research and innovation. It provides a collaborative platform for researchers and professionals to share ideas that shape the future of their respective domains.

The conference highlights advancements in Statistics,Data Science, encouraging innovative, solution-oriented research that addresses global challenges and technological evolution.

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