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 in the University of Manchester. I am member of the editorial board of Machine Learning Journal (MLJ) and 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
November 2024: I am pleased to announce my appointment as the chair of the technical committee on Statistical Pattern Recognition Techniques of (IAPR). Together with Maura Pintor, I look forward to promoting interaction and collaboration among researchers engaged in statistical pattern recognition and machine learning.
September 2024: I attended the 2024 EFSPI regulatory statistics workshop to present a short topic on the quality standards of exploratory analysis, and two posters describing our work with the Subgroup Analysis SIG and the Biomarkers SIG, of which I am a member.
August 2024: Frank Bretz and I organized an online webinar with the Basel Biometric Society (BBS) on “Controlling the chances of false discoveries in exploratory analysis of clinical trials”. The workshop took place 29th of August and you can find the material here
June 2024: I will present WATCH in the PSI (Statisticians in the Pharmaceutical Industry) 2024 conference.
May 2024: In our new paper we introduce the WATCH: A Workflow to Assess Treatment Effect Heterogeneity in Drug Development for Clinical Trial Sponsors. This work goes beyond pure data analysis considerations and approaches the problem of assessing heterogeneity comprehensively, encompassing all critical steps from the initial problem definition, through data processing and analysis, to the incorporation of external evidence and best scientific knowledge, and the final communication of the findings.
April 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.
Key research interests
- Feature selection
- Information theory
- Biomarker discovery for personalised healthcare
- Digital biomarker discovery
- Multi-target learning