I am a PhD student in the research group of Joakim Dillner, Karolinska Institutet. I am using Machine Learning approaches to identify which known and yet unknown viruses are present in the human metagenomic datasets that are most significantly related to the development of specific cancers. My background is software engineering – I have an MSc from University of Tartu.
My education is a B.Sc. in Mechanical Engineering from the University of Iceland (UI) and a M.Sc. in Economics also from UI. My previous work experience includes working as a macroeconomist at the Icelandic Ministry of Finance, as a project manager for a pharmaceutical company in Iceland and latest as a department manager for project management in an aluminium plant also in Iceland. I have a big family with three children and cycling is a big part of my life. The aim with my NIASC project is to use statistical modelling to predict the risk of prostate cancer and thereby help to improve current prostate cancer screening. In my first study I will study how PSA testing intervals influence the outcome of cancer and the probability of negative biopsies using data from the STHLM0 study which is a register data on men in Stockholm county. For my second study I will use data from the STHLM3 study to develop statistical methods for comparing the new biomarker test from STHLM3 study to the PSA test. In my third study I will investigate the cost-effectiveness of different prostate cancer screening scenarios in Iceland using a microsimulation model from Andreas Karlsson and Mark Clements. My supervisors are Martin Eklund, Associate Professor at KI, Mark Clements, Associate Professor at KI, Laufey Tryggvadóttir, Clinical Professor at UI and Henrik Grönberg, Professor at KI. I am working at Karolinska Institutet in Stockholm cooperating with the Icelandic Cancer Society and UI
My name is Nicholas Baltzer, I’m a PhD student in bioinformatics at Uppsala university. I have a BSc in Computer Science and an MSc in bioinformatics. I particularly like working with heuristics, programming, and non-trivial computations. I’m from Sweden. The goal of my project is to develop and eventually apply a predictive machine-learning algorithm to the Swedish screening programme. The idea is to use predictions to stratify women into risk groups and adjust the intensity of the screening programme accordingly for each woman. I work at the department of cell- and molecular biology, Uppsala University, under Jan Komorowski, my supervisor, and I do this in collaboration with the department of Medicine, Epidemiology, and Biostatistics at Karolinska institutet, where Karin Sundström is my supervisor.
I am a PhD student with the Clinical Text Mining Group at the Department of Computer and Systems Sciences (DSV), Stockholm University. My supervisors are professor Hercules Dalianis at DSV, and professor Søren Brunak at DTU, Technical University of Denmark. My area is text mining, and my aim is to find cancer symptoms and comorbidities in electronic patient records. I am currently working with the Stockholm Electronic Patient Record Corpus, a large database of Swedish health records, with the aim to identify symptoms and patterns in patients diagnosed with cervical cancer. I am also working with information extraction from pathology reports in cooperation with the Cancer Registry of Norway I have an MSc in software engineering from Lund University, where I wrote my master thesis in natural language processing. I got my degree in the fall of 2014 and began my PhD studies in 2015.