Welcome to the homepage of Frank Miller

I am Professor in Statistics at the University of Linköping where I work since 2022. Between 2013 and 2023, I was (Associate) Professor at the University of Stockholm. From 2003 to 2012, I applied and developed statistical methods in the pharmaceutical industry (at AstraZeneca in Södertälje, Sweden). I received my Ph.D. in 2002 from the University of Karlsruhe (Germany).
In my statistical research I focus on innovative methods needed in practice and feasible to implement. My research interests include:
Achievement tests, active machine learning, adaptive and sequential designs, biostatistics, clinical trials, optimal experimental designs, optimization algorithms.

Teaching at Linköping University and previous teaching at Stockholm University

  • Mar-May 2025: Ph.D. course Advanced computational statistics
  • Jan 2025 - Feb 2025: Computational statistics
  • Sep 2024 - Jan 2025: Course organisator of master's thesis course
  • Sep 2024 - Jan 2025: Course organisator of the course research project
  • Oct 2023 - Jan 2024: Computational statistics
  • Mar-May 2023: Ph.D. course Advanced computational statistics
  • Jan 2023: One lecture about Computer based methods: randomization, bootstrap within the course Biological statistics III
  • Jan/Feb 2022: Computational statistics
  • Jan 2022: One lecture about Computer based methods: randomization, bootstrap within the course Biological statistics III
  • Nov 2021: Experimental design
  • May 2021: Two lectures about Active machine learning within the course Machine learning
  • Mar/Apr 2021: Ph.D. course Optimisation algorithms in statistics II
More about my teaching

Supervision of PhD projects

Currently, I am supervising the PhD student Karl Sigfrid as main supervisor. His research topic is: Design of computerized adaptive tests.

Previous PhD projects


I was co-supervisor for Oskar Gustafsson from 2015-2020. Pär Stockhammar was main supervisor; here the PhD thesis.

Research projects
  • 2020 to 2023: Optimal calibration of questions in computerized achievement tests. Grant by the Swedish Research Council (Vetenskapsrådet). I am main applicant. Link to the project homepage. Here an article in Swedish from the start of the project.
  • 2014 to May 2017: Innovative methodology for small populations research (InSPiRe). Grant by European Union's Seventh Framework Programme. Main applicant: Nigel Stallard (University of Warwick). I was co-applicant. Link to the project homepage.
           
  • 2015 and 2016: Integrated DEsign and AnaLysis of small population group trials (IDEAL). Grant by European Union's Seventh Framework Programme. Main applicant: Ralf-Dieter Hilgers (University Hospital Aachen). I contributing to a workstream lead by co-applicant Carl-Fredrik Burman (AstraZeneca). Link to the project homepage.
           

Selected publications

More about my research with a complete list of my publications

Recent conference talks

  • Parallel optimal calibration of mixed-format items for achievement tests
    Conference: FREMO (Oslo, Norway, 2023)
  • About c- and D-optimal dose-finding designs for bivariate outcomes
    Conference: mODa 13 (Southampton, UK, 2023)
  • Parallel optimal calibration for Swedish national tests in school
    Conference: 36th IRT workshop (Twente, The Netherlands, 2022)
  • Optimal pretesting of questions for Swedish national tests in school
    Webex of the Cram rs llskapet, section of the Swedish Statistical Association (Sweden, 2022)
  • Optimal pretesting of questions for Swedish national tests in school
    Conference: COMPSTAT (Bologna, Italy, 2022)
  • Optimal dose finding for efficacy-safety-models of Emax-type
    Conference: DAGStat (Hamburg, Germany, 2022)
  • Optimal pretesting of questions in large achievement tests
    Seminar talk at KU Eichst tt-Ingolstadt, Department of Mathematics (Eichst tt, Germany, 2019)
  • Optimal item calibration designs for computerized achievment tests
    Conference: DAGStat (Munich, Germany, 2019)
Complete list of my talks at conferences or research seminars