Population and Behavioural Sciences Division workshop and seminar

Event details

  • When: 14th January 2019
  • Where: N Haugh, St Andrews
  • Format: Seminar

Seminar Details
Using Intensive Longitudinal Methods to Study Fear of Breast Cancer Recurrence in Everyday Life
Jean-Philippe Laurenceau, Ph.D.

Venue: Seminar room 1
Date: 14 January 2019
Time: 3-4pm.

Intensive longitudinal methods (also called experience sampling, daily diary, or ecological momentary assessment methods) allow researchers to study people’s health-related behavior, thoughts, and emotions as experienced in their natural contexts. Such data can reveal life as it is actually lived and provide insights that are not possible using conventional experimental or survey research methods. Dr. Laurenceau will review several findings from a study consisting of twice daily assessments of fear of cancer recurrence over 21 days obtained from breast cancer patients and their spouses/partners at key points in the cancer survivorship trajectory. This type of intensive longitudinal research design allows estimation of effects reflecting within-person change (versus between-person differences) in health-related outcomes and supports inferences that have high ecological validity, clinical relevance, and patient focus.

Refreshments will be served from 2.45pm.

Please note if you would like to attend the workshop, please let Karen Hunter know so that she can ensure there is enough space.

Workshop Details
Introduction to Analysing Intensive Longitudinal Data
Jean-Philippe Laurenceau, Ph.D.

Venue: Level 3 meeting room
Date: 14 January 2019
Time: 1-2pm

Intensive longitudinal methods (also called experience sampling, daily diary, or ecological momentary assessment methods) produce data that allow researchers to study people’s behavior, thoughts, and emotions as experienced in their natural contexts. The multilevel or mixed-effects model for longitudinal data is a flexible analytic tool that can take account of complexities stemming from the multiple levels of analysis and temporal dependencies in the data. The goal of this workshop is to provide an overview of a full-cycle treatment of two fundamental research questions that can be addressed using intensive longitudinal methods: (a) What is the time course of the outcome variable, and (b) what is the within-person causal process that underlies the time course? A full-cycle treatment will take workshop participants through five stages of answering each research question: (1) Design study & collect data, (2) Visualize, (3) Analyze, (4) Write up results, and (5) Power the next study. Material for this workshop will be drawn from introductory sections of the presenter’s 2013 Guilford Press book “Intensive Longitudinal Methods: An Introduction to Diary and Experience Sampling Research” (www.intensivelongitudinal.com). Using SPSS and Mplus statistical software, attendees can follow along with the examples in the workshop handout or conduct analyses on their own laptops.

Dr. Jean-Philippe Laurenceau is Unidel A. Gilchrist Sparks III Chair in the Social Sciences and Professor of Psychological & Brain Sciences at the University of Delaware. He is also Senior Research Scientist at CCHS’s Helen F. Graham Cancer Centre and Research Institute. He completed his B.A. cum laude at Cornell University and received his master’s and doctorate degrees from The Pennsylvania State University. Recently, Dr. Laurenceau has been studying how patients and spouses/partners cope with and maintain connection amid health-related adversity, including breast cancer and diabetes. He was an appointed member of a social and behavioural sciences grant review panel of the National Institutes of Health and has been PI or co-I on several research projects funded by the National Institute of Mental Health, National Institute of Child Health and Human Development, and the National Cancer Institute. He regularly teaches methodological workshops at the University of Michigan’s Summer Program in Quantitative Methods of Social Research and at Penn State’s Summer Institute for Longitudinal Methods. Dr. Laurenceau is co-author of the book “Intensive Longitudinal Methods: An Introduction to Diary and Experience Sampling Research” (2013, Guilford Press).

Job vacancies: Lecturers in Computer Science

The School of Computer Science is recruiting two new Lecturers as part of a large on-going expansion of our academic staff.

You will be a scholar with a growing international research reputation in Computer Science and a commitment to delivering high quality teaching within the broad field of Computer Science and its applications. The successful candidate will be expected to have a range of interests, to be active in research publication that strengthens or complements those in the School and to be capable of teaching the subject to undergraduate and taught postgraduate students who come to us with a wide range of backgrounds.

Candidates should hold a PhD in a cognate discipline. Excellent teaching skills and an interest in promoting knowledge exchange are essential. You should also have some familiarity with grant seeking processes in relation to research councils and other sources.

Closing date: 14th January 2019

Informal enquiries can be directed to Professor Simon Dobson (hos-cs@st-andrews.ac.uk) or Dr Dharini Balasubramaniam (dot-cs@st-andrews.ac.uk).

Find out more about the vacancies further particulars on the recruitment website.

Professors Quigley and Kitamura to co-chair ACM CHI 2021 in Asia

Professors Quigley and Kitamura

Professor Aaron Quigley and Professor Yoshifumi Kitamura (Tohoku University, Japan) have been appointed the general co-chairs for the ACM CHI conference on Human Factors in Computing Systems in Asia in 2021.  CHI is hosted by the ACM SIGCHI, the Special Interest Group on Computer-Human Interaction

The ACM CHI Conference on Human Factors in Computing Systems is the premier international conference for the field of Human-Computer Interaction (HCI). This flagship conference is generally considered the most prestigious in the field of HCI and attracts thousands of international attendees annually.


CHI provides a place where researchers and practitioners can gather from across the world to discuss the latest HCI topics. It has been held since 1982 and this is only the second time CHI will be held in Asia.  CHI 2020 will be held in Hawaii while CHI 2019 will be held in Glasgow next May. The location for CHI 2021 will be announced to the global research community during CHI 2019.

This week Professor Quigley was invited to present at the Third ACM SIGCHI Asian Symposium hosted in the Research Institute of Electrical Communication at Tohoku University, Sendai. The ACM SIGCHI Asian Development committee organised this event to bring together early career researchers, students and more from multiple countries in the Asia-Pacific region to discuss ideas that can lead to innovations and to inspire us all. The event served to  develop connections and regional/local societies through promoting collaboration among Asian-Pacific HCI researchers and practitioners. Professor Quigley will be spending his upcoming sabbatical in Asia.  

Job vacancies: Interdisciplinary Data Scientists

The Schools of Medicine and Computer Science are seeking to appoint three highly motivated data scientists with a passion for computer vision and deep learning, and specifically their application to medical imaging. The data scientists will be based in the Schools of Computer Science and Medicine at the University of St Andrews and will work on a national Innovate UK funded initiative to create a pan Scotland Industrial Centre for AI Research in Digital Diagnostics (iCAIRD).

The successful candidates will have the opportunity to work alongside and learn from clinicians, industrial experts from Philips Healthcare and academics to help develop artificial intelligence solutions for the automatic reporting of cancer diagnoses in endometrial and cervical cancer. The main duties of the role will involve being an active member of an interdisciplinary team of scientists to help develop deep learning algorithms, within industry standard guidelines, to analyse patient samples in a manner that allows rapid clinical transfer. This work will therefore have the opportunity to impact both patient welfare and relieve pathologist work burden.

Applicants should have experience in machine learning, demonstrable experience in computer programming languages and an interest in the medical applications of computer science. The candidates would benefit from a track record in scientific writing and working in interdisciplinary teams as well as experience in computer vision.

The posts are full time and over a period of 36 months.
Closing Date: 18 January 2019

Find out more about the vacancies further particulars on the recruitment website.

December Graduation 2018

Congratulations to the Masters Class of 2018, and PhD students Dr Daniel Rough and Dr Adeola Fabola who graduated last week. The School also celebrated the Installation of Professor Adam Barker. Students and guests were invited to a reception in Computer Science after the ceremony to celebrate their achievement and reflect on their time in the School.

Our graduates move on to a wide variety of interesting and challenging employment and further study opportunities, and we wish them all well with their future careers.

PhD viva success: Shyam Reyal

Congratulations to Shyam Reyal, who successfully defended his thesis yesterday. He is pictured with Internal examiner Dr Tom Kelsey and external examiner Dr Mark Dunlop , from the University of Strathclyde. Shyam’s research was supervised by Dr Per Ola Kristensson and Dr Mark-Jan Nederhof.

Image courtesy of Annemarie Paton

PhD viva success: Julian Petford

Congratulations to Julian Petford, who successfully defended his thesis today. He is pictured with internal examiner Professor Aaron Quigley and external examiner Dr Jason Alexander, from Lancaster University. Julian’s PhD research in Full Coverage Displays for Non-Immersive Applications was supervised by Dr Miguel Nacenta.

Image courtesy of Wendy Boyter