CV

Computational biologist and senior bioinformatics scientist working at the intersection of AI, machine learning, cancer biomarkers, and multi-omics. I build reproducible tools and workflows that turn complex sequencing data into practical biological insight.

Education

• University of Canterbury, New Zealand - PhD in Bioinformatics/Biotechnology, 2012-2016
• Middle East Technical University (METU) - MSc in Bioinformatics (Machine Learning), 2009-2012
• Middle East Technical University (METU) - BS in Physics, 2001-2005

Work experience

Department of Pathology, University of Oslo - Researcher and Senior Bioinformatics Scientist
Mar 2022-Present

• Collaborate with clinicians and pathologists on translational studies in colorectal cancer, mucosal immunology, and autoimmune disease using sequencing and clinical data on secure HPC infrastructure.
• Profile immune-cell populations with bulk RNA-seq, single-cell RNA-seq, spatial transcriptomics (Visium, Visium HD, and Xenium), and Akoya spatial proteomics.
• Integrate microbiome and metagenomic data with single-cell and multimodal datasets spanning WGS, GEX, ATAC-seq, BCR sequencing, and feature barcoding.
• Develop machine-learning methods for spatial deconvolution with Easydecon and investigate molecular markers for early pancreatic and colorectal cancer detection.
• Develop and maintain cellsnake, a reproducible single-cell workflow distributed through Bioconda and Docker Hub.

Cancer Registry of Norway - Advisor and Bioinformatics Scientist
Oct 2016-Mar 2022

• Managed large clinical, registry, EHR, and molecular datasets from longitudinal and prediagnostic cancer cohorts in secure HPC environments.
• Analyzed serum RNA profiles from more than 4,500 samples and developed machine-learning models for early lung and colorectal cancer prediction.
• Built reproducible workflows for RNA analysis, NGS variant calling and annotation, and HPV whole-genome analysis with TaME-seq.
• Developed MirMachine for microRNA annotation and computational models for RNA-RNA interactions.

University of Canterbury - Bioinformatics Scientist
Nov 2012-Aug 2016

• Built reproducible SLURM, Snakemake, Docker, and Singularity workflows for microbial Illumina, PacBio, Nanopore, and RNA-seq data.
• Annotated prokaryotic genes and non-coding RNAs, benchmarked RNA-RNA interaction methods, and developed parallel Python workflows.
• Modelled protein production in bacteria and archaea, leading to the avoidance hypothesis; the work appeared on the cover of eLife and received a PhD publication award.

Department of Chemistry, METU - Research Assistant in Computational Science
Aug 2006-Oct 2012

• Developed an HMM-based machine-learning tool for signal-peptide detection while completing an MSc focused on machine learning.
• Applied R, SPSS, SQL, MySQL, PostgreSQL, and HPC to biological and biostatistical research.

Core expertise

• AI and machine learning: classification, regression, feature engineering, model evaluation, deep learning, scikit-learn, H2O, caret, mlr3, Keras, TensorFlow, PyTorch
• Bioinformatics and multi-omics: scRNA-seq, spatial transcriptomics, bulk and small RNA-seq, WGS, ATAC-seq, ChIP-seq, BCR/TCR-seq, CITE-seq, metagenomics, microbiome, non-coding RNA
• Programming: R, Python, Linux/Bash, Git, tidyverse, ggplot2, Shiny, pandas, polars, NumPy, SciPy, Biopython
• Workflows and infrastructure: Snakemake, SLURM, HPC, TSD, Educloud/FOX, Docker, Singularity, Podman, Conda/Bioconda
• Sequencing platforms: 10x Genomics, Parse Biosciences, Illumina, PacBio, Oxford Nanopore

Selected software

• Easydecon: lightweight cell-type analysis for high-definition spatial transcriptomics
• MirMachine: accurate microRNA annotation from genome sequences using trained covariance models
• Cellsnake: scalable, user-friendly single-cell RNA-seq analysis

Selected research highlights

• Apply molecular and cancer epidemiology to longitudinal, population-based, and prediagnostic cohorts, linking molecular profiles with clinical outcomes for biomarker discovery and risk modelling.
• Demonstrated that serum RNA profiles can predict lung cancer up to ten years before diagnosis (eLife, 2022).
• Contributed to longitudinal mucosal transcriptomics in ulcerative colitis (Journal of Crohn’s and Colitis, 2026) and MirGeneDB 3.0 (Nucleic Acids Research, 2025).
• Developed and published cellsnake in GigaScience and MirMachine in Cell Genomics (2023).
• Co-inventor on patent WO2023152568A2, covering RNA biomarkers and machine-learning methods for lung-cancer characterization.
• Author of 25 peer-reviewed publications across molecular epidemiology, cancer biomarkers, non-coding RNA, microbial genomics, and computational tool development.

See the publications page or Google Scholar for more.

Awards, leadership, and teaching

• Best PhD Publication Award, University of Canterbury (2017).
• Selected talks and travel awards from 10x Genomics, RoBioinfo, EMBO/EMBL, and other international meetings.
• Supervisor and mentor for PhD and master’s students in bioinformatics, single-cell analysis, and reproducible workflows.
• Instructor for the Oslo Bioinformatics Workshop Week and introductory bioinformatics teaching.