Hybrid Conferencee

International Conference on Statistical Learning and Stochastic Methods (ICSL-SM - 26)

14th - 15th August 2026 | Washington DC, USA
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Conference Brochure
Sample Full Paper
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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 Washington DC. 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 Washington DC 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 Washington DC conference. Build collaborations and gain insights from leading experts."
Keynote Speaker Sessions:
"Don’t miss our Keynote Sessions in Washington DC, 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 Washington DC."
SDG-Inspired Conference Focus:
"Our conference will highlight research that addresses global sustainability, inclusive education, and solutions for environmental challenges."

Call for Paper

The ICSL-SM 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 Probability Theory,Statistics, 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
Stochastic methods in machine learning
02
Statistical learning for dynamic systems
03
Stochastic modeling in data science
04
Applications of stochastic methods in finance
05
Statistical learning in time series forecasting
06
Stochastic methods for optimization problems
07
Statistical learning in computer vision
08
Stochastic modeling in healthcare systems
09
Applications of stochastic methods in engineering
10
Statistical learning for anomaly detection
11
Stochastic processes in artificial intelligence
12
Statistical learning in social sciences
13
Stochastic methods for risk management
14
Statistical learning in environmental modeling
15
Stochastic modeling in telecommunications
16
Applications of stochastic methods in logistics
17
Statistical learning for user behavior analysis
18
Stochastic methods in operations research
19
Statistical learning in energy systems
20
Stochastic modeling in supply chain optimization