People

Neil Sheldon (Chair, Teaching Statistics Trust, The United Kingdom): Neil is a guest lecturer for courses related to understanding of statistical concepts and communication in statistics.

• Etienne Sebag:Parametric and non-parametric approaches to estimation in a framework of Markovian intermittently observed trajectories - An Application using Multistate Models. The research focuses are: estimation using intermittently observed trajectories under a framework of non-homogeneous Markov processes and applications in medical decision making. Supervisors are Sangita Kulathinal and Dario Gasbarra (University of Vaasa).
   
• Tuuli Kaupala: Prediction and causal explanation in human growth and development across ages. The research focuses on (i) building and evaluating multivariate Bayesian hierarchical models for estimation and prediction purposes and assessing prediction accuracy using several measures, and (ii)  developing a fully Bayesian workflow to examine the dose-response relationship between a drug usage and its outcomes. Supervisors are Sangita Kulathinal and Maarit Leinonen (THL and HUS).
   
• Jenni Pirhonen: Statistical Modeling and Estimation of Diffusion Tensor Distributions and Novel Spectral Data Acquisition Modalities. The project aims to enhance diffusion MRI (dMRI) accuracy by developing non-parametric maximum entropy methods for estimating the diffusion tensor distribution, applying convex regression for MRI data denoising, and creating new models for particle diffusion at mesoscopic-scales. These approaches promise to surpass traditional parametric methods by minimizing bias and capturing complex tissue structures more accurately. Supervisors are Dario Gasbarra (University of Vaasa) and Sangita Kulathinal.
   
• Joel Siurua: Nowcasting respiratory infection disease burden based on multiple data sources and optimizing interventions. The research focuses are: develop methods for monitoring and producing short-term predictions (“nowcasting”) for a respiratory triple epidemic of influenza, COVID-19 and RSV in Finland and to study the optimization of vaccination strategies against simultaneous respiratory epidemics. Supervisors ae Simopekka Vänskä (THL), Matti Lassas, Sangita Kulathinal.
   
• Leo Aarnio: Why and how evidence-based medicine must turn fully Bayesian. I am interested in the foundations of statistics and the practical implications foundational choices have for concrete applications, especially in medical decision making. In my PhD I derive statistical desiderata for evidence-based medicine, show where and why currently reigning methods fall short, establish an alternative and apply it into practice. Supervisors are Elja Arjas and Sangita Kulathinal.
   
• Ashwini Joshi: I work with longitudinal data where multiple outcomes are observed. I develop non-parametric and Bayesian methods for analysing such data. Supervisors are Sangita Kulathinal and Dario Gasbarra (University of Vaasa).
   
• Tuomo Susi (previously Nieminen): My research involves applied statistics and methods development in large health-register data settings. One focus of the research is vaccine safety and recent work relates to possible adverse outcomes related to COVID-19 vaccines. Supervisors are Sangita Kulathinal and Kari Auranen (University of Turku).
   
• Aapeli Nevala: I work on Bayesian event-history analysis applied for cancer screening. The goal of my research is to build models that can combine information from multiple sources and that can be used for decision making in public health and healthcare settings. Supervisors are Sangita Kulathinal, Sirpa Heinävaara (Finnish Cancer Registry) and Tyyti Sarkeala (Finnish Cancer Registry).
   
• Miika Mäki (registered at the Faculty of Social Sciences): I study the determinants, dynamics and outcomes of romantic relationship histories. I use multistate modelling for modelling transition probabilities between different relationship statuses. Supervisors are Mikko Myrkylä (Center for Social Data Science and Population Research Unit, University of Helsinki), Anna Rotkirch (Population Research Institute) and Sangita Kulathinal.
   
• Solomon Christopher: I assess and develop methods to study infection transmission, person-to-person (P2P), based on household-level data under Bayesian framework.I also examine designs for household-based studies that optimise sample information content to estimate transmission parameters.Supervisors are Kari Auranen (University of Turku) and Sangita Kulathinal.

The following past doctoral researchers are continuing collaboration with the group.

• Swapna Deshpande (degree from the Tampere University): My research is about Overweight, diet, physical activity, weight gain during pregnancy and pregnancy outcomes among women of reproductive age from urban India. Maternal anthropometrics and their influence on gestational Diabetes and neonatal outcomes in Indian women (2025, supervised by Tarja Kinnunen, Tampere University and Sangita Kulathinal)
   
• Jorma Kilpi: My general research interest is: statistics and information theory. My current research interests include: Bayesian methods, causality, design of experiments.
  From data processing to distributional modelling of traffic measurements (2022, supervised by Ilkka Norros, Sangita Kulathinal, Eero Saksman)
   
• Ulrike Baum: My current research is mainly the evaluation of Covid-19 vaccine effectiveness. I will continue  monitoring/evaluating the effectiveness of seasonal influenza vaccines at THL.
  Cohort studies of influenza vaccine effectiveness based on Finnish register data (2021, Turku University, supervised by Kari Auranen, Sangita Kulathinal)

Students working on master's thesis:

• Robin Källman
• Lotta Niemi
• Henrik Mannerström

Master's thesis completed (most of the following students are continuing collaboration):

• Rebecca Burton: Accounting for Multiplicity in Clinical Trials (2025)
   
• Laura Salonen: Estimation of incidence rates of pulmonary embolism among persons with COVID-19 during the Omicron period and among persons vaccinated against SARS-CoV-2 infection: Application of time-to-event analysis (2025)
   
• Etienne Sebag: Spatiotemporal Generalized Additive Model: Investigating Weather-Related Road Crashes in Southern Finland (2024)
   
• Dianna Smith: Selected Contributions by C. R. Rao to Multivariate Analysis (2024)
   
• Kirsi Hellsten: GWAS of Triglycerides in Southeast Asian Populations (2023)
   
• Inchuen Huynh: Hawkes processes and their applications (2023)
   
• Eetu Mäkinen: Modeling a graduation process with survival and event history analysis (2023)
   
• Tomas Tanskanen: A multistate analysis of ulcerative colitis and colorectal cancer (2022)
   
• Tuuli Kaupala: Developmental trajectories of height, weight and BMI across childhood: Bayesian hierarchical modeling of longitudinal data (2021)
   
• Arya P. V.: Population as a Cohort: Increasing statistical power in a cohort analysis using register data in addition to population survey datasets (2020)
   
• Aapeli Nevala: Bayesian hidden Markov model for overdiagnosis in colorectal cancer screening (2020)