Hybrid Conferencee

International Conference on Survival Models and Reliability Analysis (ICSMYRA - 26)

14th - 15th August 2026 | Geneva, Switzerland

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Keynote Speaker Sessions:
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Best Paper & Best Paper Presentation Award:
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SDG-Inspired Conference Focus:
"Our conference will highlight research that addresses global sustainability, inclusive education, and solutions for environmental challenges."

Conference Session Tracks

SDG Wheel

Aligned with

UN Sustainable Development Goals

This conference contributes to global sustainability by aligning its research discussions and academic sessions with key United Nations Sustainable Development Goals. It fosters knowledge exchange, innovation, and collaborative engagement.

SDG 3
SDG 3 Good Health and Well-being
SDG 4
SDG 4 Quality Education
SDG 9
SDG 9 Industry, Innovation and Infrastructure
Track 01

Advancements in Survival Models

This track focuses on the latest developments in survival modeling techniques, emphasizing innovative approaches to time-to-event data analysis. Researchers are encouraged to present novel methodologies and applications in various fields, including healthcare and engineering.

Track 02

Reliability Analysis in Engineering Systems

This session will explore reliability analysis methodologies applicable to engineering systems, including failure time data modeling and risk assessment. Contributions that integrate statistical methods with engineering principles are particularly welcome.

Track 03

Statistical Inference in Hazard Models

This track aims to discuss statistical inference techniques specifically tailored for hazard models, including estimation and hypothesis testing. Participants are invited to share their insights on the challenges and solutions in this area.

Track 04

Biostatistical Applications in Epidemiology

This session will highlight the role of biostatistics in epidemiological studies, focusing on survival analysis and time-to-event data. Contributions that bridge the gap between statistical theory and public health practice are encouraged.

Track 05

Simulation Techniques in Reliability Studies

This track will cover simulation methodologies used in reliability studies, including Monte Carlo methods and bootstrap techniques. Presentations should demonstrate the practical application of these techniques in real-world scenarios.

Track 06

Machine Learning in Survival Analysis

This session will explore the integration of machine learning techniques with survival analysis, focusing on predictive modeling and risk assessment. Researchers are invited to present case studies that illustrate the effectiveness of these approaches.

Track 07

Quantitative Methods in Clinical Research

This track will address the application of quantitative methods in clinical research, emphasizing statistical techniques for analyzing survival data. Contributions that enhance the understanding of patient outcomes through rigorous statistical analysis are welcome.

Track 08

Recent Trends in Time-to-Event Analysis

This session will focus on recent trends and methodologies in time-to-event analysis, including advancements in statistical modeling and inference. Participants are encouraged to share their findings and discuss future directions in this evolving field.

Track 09

Risk Assessment and Management

This track will delve into statistical approaches for risk assessment and management, particularly in the context of survival data. Contributions that highlight innovative risk modeling techniques and their applications are highly encouraged.

Track 10

Applied Statistics in Reliability Theory

This session will explore the application of statistical methods in reliability theory, focusing on empirical studies and theoretical advancements. Researchers are invited to present their work that bridges statistical theory with practical reliability challenges.

Track 11

Data Science Approaches to Survival Analysis

This track will examine the intersection of data science and survival analysis, highlighting the use of big data techniques in modeling and inference. Contributions that showcase the application of data-driven methodologies in survival research are welcome.