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RSC 2026 short course: Metaheuristic Methods for Variable Selection & Survival Analysis

23 April 2026
RSC 2026 - Short Course

Deep dive in ISI short course for RCS 2026 - Malta

 

Ahead of RSC 2026 in Malta, the ISI is hosting a series of high-level short courses in collaboration with strategic partners. This curated programme delivers expert-led sessions designed to bridge theoretical rigor with practical application, ensuring participants gain immediate, actionable expertise.

 

Afbeelding
Maria Kontorinaki
Maria Kontorinaki
Afbeelding
Monique Sciortino
Monique Sciortino
Afbeelding
Liberato Camilleri
Liberato Camilleri
Afbeelding
Derya Karagöz
Derya Karagöz

 

Today, we speak with Maria Kontorinaki, Monique Sciortino, Liberato Camilleri and Derya Karagöz. They lead the upcoming short course, 'Metaheuristic Methods for Variable Selection & Survival Analysis'. We sat down with them to discuss the core objectives of the course, the unique value it offers, and the specific skills participants will bring back to their organisations.


What is the main goal of your short course and what will participants be able to apply afterwards?
Liberato: The main goal of this short course is to introduce participants to metaheuristic algorithms as flexible and powerful tools for variable selection in statistical models. Participants will learn how to formulate variable selection as an optimisation problem and how to solve it using methods such as Genetic Algorithms, Simulated Annealing, and Tabu Search. By the end of the course, they will be able to implement these techniques in R or Python and apply them to real-world datasets to build more efficient, interpretable, and accurate models.
 

Who is this course intended for and what background knowledge is expected?
Derya: This course is designed for graduate and advanced undergraduate students, as well as practitioners and researchers in statistics, data science, and related fields. It is particularly relevant for those interested in model building and variable selection in complex settings. Participants are expected to have a basic understanding of regression modelling and some familiarity with optimisation concepts. Prior experience with R or Python will be beneficial, especially for the hands-on component, but detailed guidance will be provided during the session.


What makes your course different compared to similar courses in this field?
Monique: What distinguishes this course is its strong emphasis on bridging theory and practice. While many courses focus either on statistical methods or optimisation techniques in isolation, this course integrates both perspectives through the lens of metaheuristics. In addition, it combines concise theoretical insights with a hands-on session where participants actively implement and experiment with algorithms on real and simulated data. This applied focus helps participants understand not only how the methods work, but also when and why to use them in practice.


What is one key takeaway participants will learn from your course?
Maria: A key takeaway from this course is the ability to view variable selection as a combinatorial optimisation problem and to tackle it using metaheuristic algorithms. Participants will gain practical experience in implementing at least one such method and understanding how to tune and evaluate it. This equips them with a versatile, scalable approach that can be applied to high-dimensional, complex modelling problems where traditional methods may struggle.


Ready to turn AI into real-world spatial insights?

Join Maria Kontorinaki, Monique Sciortino, Liberato Camilleri and Derya Karagöz. Gain practical skills in metaheuristic methods for variable selection and survival analysis, and apply them confidently in your own work.

Secure your spot today - Seats are limited!

 

Date: 2 June 2026 
Venue address: University of Malta Valletta Campus, St Paul Street, Valletta VLT 1216, Malta