Hello, my name is Konstantinos (for short, Kostas) Sechidis and I am a machine learning researcher with experience in developing, enhancing and delivering novel statistical and machine learning methods tailored to healthcare analytics. I work in the Advanced Methodology and Data Science group of Novartis and I am honorary research fellow in Machine Learning and Robotics within the Department of Computer Science in the University of Manchester. I am member of the editorial board of Machine Learning Journal (MLJ) and vice-chair of the technical committee on Statistical Pattern Recognition Techniques of the International Association for Pattern Recognition (IAPR).
Disclaimer: this is my personal page, the content is my own responsibility and it is not connected to/supported by any entity with which I have been, am now, or will be affiliated.
News
March 2024: There is a PhD opportunity by Gavin Brown in the UKRI AI Center for Doctoral Training (CDT) in Decision Making for Complex Systems (jointly run between the University of Manchester and the University of Cambridge). The project will focus on the areas of causality and information geometry, with the main objective to study the statistical properties of various causal effect measures and understand/reduce their inherent uncertainty. Frank Bretz and I will also co-supervise/advise the student.
March 2024: A new paper, All that Glitters Is not Gold: Type-I Error Controlled Variable Selection from Clinical Trial Data, published in Clinical Pharmacology and Therapeutics (CPT). Furthermore, an R package that implements the methods described is available in GitHib: knockofftools package.
April 2024: Together with Mark Baillie, Frank Bretz and Prashanti Goswami we organise the Data science thinking: making an impact workshop in AMLD 2024.
Nov 2023: Gave an invited presentation in the European Statistical Forum. This year’s topic was: Statistical Methodology in Precision Medicine and the role of Artificial Intelligence.
Sep 2023: Present our work on controlled biomarker discovery in the 5th Conference of the Central European Network (CEN).
Key research interests
- Feature selection
- Information theory
- Biomarker discovery for personalised healthcare
- Digital biomarker discovery
- Multi-target learning