ÐÓ°ÉÂÛ̳

My teaching philosophy is sharing

I come from a family of teachers and from a young age I aspired to follow my father’s steps, who would bring purpose, moral, and a touch of magic in every topic he was teaching. My path brought me again to ÐÓ°ÉÂÛ̳, that has a magic of its own, where I can share with my students, my experience, knowledge and indeed love for statistics, and by doing so, help them achieve their dreams and aspirations. The idea of sharing was also the drive to introducing the GTA mentoring scheme, where teaching staff can share experiences, tips and assist one another in improving and developing their skills.

Statistics and mathematics are not just numbers but logic

The most common advice I give to my students is this. Break the problem into small components, visualise how the different parts are connected and follow logical steps to navigate your way towards the final answer. If you can’t solve a difficult problem, solve a simpler one. Ideally, solve many simpler problems. Practice, practice and more practice. You will soon decipher the logic behind the formulae and before long you will have surpassed your target.

As a teacher, witnessing my students’ progress from novices to masters is my best reward.

Statistics is not only relevant but essential in life

During my first class, I challenge my students to find a topic where statistics is not used, or to think how many hours ago they came across a statistical result (inadvertently or not). Throughout my teaching, I try to show the connection between the syllabus material and real life statistical applications. The deeper one understands the former, the broader the scope of the latter. In a rapidly changing work space, statistics has a prominent place. Teaching is not simply showing students the solution to existing problems but mainly inspiring them to invent new problems and quest for innovative solutions.

ÐÓ°ÉÂÛ̳, is an exciting pool of diverse talents, backgrounds and dreams, and teachers and students together have the opportunity to take part in shaping a bright future.

Teaching is itself a learning experience

Every class teacher goes through a similar process, that includes preparing and presenting a topic in front of an audience. It is natural to feel nervous at first, but with every class the confidence increases. Below are a few tips to make this process easier and more efficient.

1. Go through the same thought process as the students, by solving the problem independently and using first principles.

2. Do a rehearsal of the key points and have a plan, it provides reassurance.

3. Spend a few minutes in the beginning summarising the relevant theory. This makes the connection between lecture and class and helps establish continuity.

4. Give references to real life applications of the topic covered.

5. Do not feel embarrassed when you make a mistake, everyone does, it’s part of the learning process, just thank the student who spotted it.

6. Explain the logic of a formula or a proof, and feel free to skip some of the detailed calculations. Break the question into a small number of logical steps and emphasise the logic behind the solution.

7. Alternate the questions you discuss between homework and unseen. This increases the student involvement and participation.

8. Finish the class by emphasising the importance of the material covered during the class.

9. You are strongly encouraged to attend a class of a more experienced class teacher or lecturer.

Teaching improves important interpersonal skills, like presentation, assertiveness, communication and listening that are extremely useful both inside and outside the classroom. 

Henry Wynn is an Emeritus Professor of Statistics at the ÐÓ°ÉÂÛ̳. Please have a look at the  video of his interview. 

Howell Tong has been an Emeritus Professor of Statistics at the London School of Economics since 1st October 2009. Please find an  with Howell conducted by Kung-Sik Chan and Qiwei Yao.

How would you describe your research problem to general audience? 

In this information age featured with big data, various forms of observation are recorded over time at many locations or for many individuals. At each given time point, the recorded data can be a long vector, a curve, a space, or a network. My major research interest is to analyse those complex time series for better understanding the underlying dynamics and for forecasting accurately the future values.

How did your research influence the field? 

I try to transform a complex problem into a simpler one or several simple ones. One approach is to find a latent and low-dimensional structure in a high-dimensional problem. This has been proved effective in analysing high-dimensional time series, high-dimensional and high-frequency financial returns, spatial-temporal data, and curve time series.

What new results have you established? Did it change your work? 

The newly proposed time series principal component analysis (TS-PCA) should have wide impact in analysing multiple time series, especially when the dimension of time series is high. It transforms a multiple time seires into several low dimensional uncorrelated series (across all time lags), leading to better influence and better future prediction. I expect TS-PCA to be used as a standard pre-step in analysing multiple time series.

How does your work is used by outside the academic sector? 

I enjoy working with industry. In addition to the satisfaction of solving reallife problems,  it also leads to interesting, challenging and non-standard research questions. I have written several research papers through working together with EDF and Barclays Bank on their business problems. Each of them was great fun!

How would you describe your research in your own words?

I am deeply interested in inter-disciplinary work. For me, research makes sense if I am at the boundary between different disciplines. I have been lucky to work in a wide range of areas, such as economics, finance and more recently the environment. What I particularly appreciate in my research is to see what happens when these areas meet.

Recently, my interest has been about understanding "what is a model". Not necessarily from a statistician’s point of view but more generally. What does a model mean for different fields of research? How do they approach a problem and develop a model to solve it? In this sense, I'm a bit of an outlier, reaching researchers outside of my own field.

A problem encountered by people working on a trans-disciplinary project is that concepts have different meanings depending on your area of expertise. Other scientists will have a different understanding than yours; the vocabulary is not the same for a biologist, an economist or a physicist. Therefore, a lot of time is spent going back to that point, communication, and then you increase your knowledge about other disciplines.

When it works, it challenges the way you think and generates something rich and useful.

How do you export the statistical tools used for the financial market to environmental management?

In a way, looking at the things I have been doing in my research requires thinking about management of risk as well as the decision process. Looking at how to transfer risks from the insurance sector to the financial market and manage them in a better way. Regarding environmental issues, it is also very much about considering the risk you are taking to develop a policy that can lead to an improvement. That is why it goes beyond finance and insurance but also the environment. It is more general.

For a couple of years now, I have been working on a project precisely on this, where I am looking at the very definitions of model, risk and uncertainty in different areas. I have been interviewing researchers from diverse fields, such as statistics, cosmology, biology, chemistry, medical research or drug design. At some point it always comes down to the same idea: to consider a risk and take a decision. 

How do you see research?

For me, research is influenced by the way you are trained. A PhD student will follow a supervisor who might shape his vision of research. Mine had a very sociable approach, and through her I learned how to do research as a sort of nice human experience. Nowadays, a research project can easily start from a conversation with someone I get along with. This is what happened for the project on environmental management; I met this person, an environmental economist, who was very nice and we started this project together.

I don't dissociate the process of doing research from the human experience behind it, and I get a great deal of learning from each other. In a way, what really matters to me, more than the direct outputs, is the experience I get through it.

Professor Pauline Barrieu is the Head of Department of Statistics.  

How would you describe your research to a general audience?

My recent research has focused on methods for analysing longitudinal data from surveys that track individuals over time. There are many sources of longitudinal data, including large-scale birth cohort surveys and household panel surveys that follow individuals over many years, and studies that collect ‘high frequency’ data (e.g. daily or weekly) over a shorter time period. The most appropriate study design depends on the research question of interest, and each has its own challenges when it comes to data analysis. A particular issue with any longitudinal design is that data are clustered from having repeated observations on the same individual, which means that standard statistical methods need to be adapted. One methodological challenge that interests me is how to account for household or area effects on individual behaviour when individuals are clustered in households and areas, but household membership and area of residence change over time. Another active area of research is how to model the complex interrelationships between the various types of decisions that people make over time, for example decisions about if and when to have children and when to move house.

The methodological topics I work on are motivated by real-world applications. Most of my research has been in demography, but I have also worked in health, psychology and education, most often with social scientists who provide subject-matter expertise. For me, one of the main attractions of social statistics is the wide range of application areas that we can research and learn about, and the availability of rich datasets.

My most recent project (co-funded by the Economic and Social Research Council and the Engineering and Physical Sciences Research Council) involves developing methods for the analysis of longitudinal data on pairs of individuals (or ‘dyads’). Such data are commonly collected in social research, for example on pairs of members of the same family to assess agreement in perceptions of the quality of their relationship. We will be applying these methods in a study of exchanges of support between parents and their adult children, using UK data from the Understanding Society survey. The research team includes social statisticians from the Department of Statistics and colleagues from the Department of Social Policy at ÐÓ°ÉÂÛ̳ and the Institute for Social and Economic Research at the University of Essex.

How has your work been used by other researchers?

My work on methods for analysing multilevel time-to-event data has been widely used by social scientists and researchers from other disciplines. These data are highly complex because individuals can experience an ‘event’ (e.g. birth or divorce) more than once, they may move between different ‘states’ (e.g. employment and unemployment) over time, and there may be different reasons for moving out of a state (e.g. reasons for leaving a job). These methods have found applications in a range of areas, including sociology (e.g. the evolution of social networks), business (e.g. decisions about the location of multinational enterprises), and veterinary medicine (e.g. transitions between foot conformation, lameness and footrot in lambs).

A key factor in encouraging other researchers to adopt a new approach is to explain it in an accessible way, with examples, and to show how it can be implemented in software. Capacity building has been an important part of my work and can take many forms, such as tutorial articles in applied statistics and social science journals that explain how to apply a particular method (and interpret the results), online training materials, and traditional face-to-face training workshops. For example, as part of the LEMMA project (funded through the ESRC National Centre for Research Methods) I led the development of an extensive online training course in multilevel modelling. The course now has over 20,000 registered users, with a high proportion of students (60%) and many from overseas (75%).

And outside the academic sector?

My research has been used primarily by academic social scientists, and so its impact outside the academic sector is largely indirect through the substantive findings of academics who apply the methods in their own research.

One example of research that has had a more direct impact is a recent study on whether intentions, beliefs and mood following acute coronary syndrome predicts patients’ subsequent attendance at outpatient cardiac rehabilitation (CR). The study, led by healthcare researchers from the Universities of Dundee and Aberdeen, involved structural equation modelling of real-time diary data on cardiac-related beliefs and mood collected using an electronic diary. The findings have influenced work on the redesign of CR through the 2020 vision for CR.

Another way that my research has been taken up outside the academic sector is through the provision of on-line training materials. A large number of users (15%) of users of the LEMMA online course are non-academics, while attendees at face-to-face tend to be academics. It seems that the flexibility of the online format is especially attractive to non-academics. 

Professor Fiona Steele is a Professor in the Department of Statistics.