I became interested in Mathematical Sciences in my Secondary school days. I then proceeded to obtain a degree in Statistics at University of Ilorin, Nigeria. After my degree programme, I began to exploit my areas of interest in Statistics. I soon came to realise and was intrigued by the importance of medical statistics. This led me to pursue a master degree in Medical Statistics at UCL. The diversity in the range of research areas at UCL provides a great environment for learning and research.
I am currently a PhD under the supervision of Dr Aidan O’Keeffe and Prof Gianluca Baio. My research is focussed on developing methodologies for estimating the effect of treatments, specifically when the treatment is not randomly assigned. The methods are then applied to real data from primary health care records.
I originally studied English Literature as an undergraduate, but soon realised that I would prefer to do something more technical. While working after graduation, I took an Open University degree in Mathematics and Statistics; eventually I was able to return to full-time study, with an MSc in Statistics at the University of Warwick. My plan was to complete my MSc and find a more interesting job, but I enjoyed immersing myself in my final project (designing methods to identify evidence of large-scale planning in early Medieval archaeological sites) so much that I decided to continue studying. While looking for PhD positions I worked as a research associate at Warwick, investigating the development of defective pixels in x-ray detectors; I came to UCL to study for my PhD in 2016.
I'm now working on an applied Bayesian framework to combine and post-process weather forecasts from multiple forecasting centres, in order to obtain properly calibrated and bias-corrected forecasts. This work is supervised by Richard Chandler in the Department of Statistical Science and Chris Brierley, a climate scientist in the Department of Geography. While the project is obviously focused on the statistical methods required, it also affords opportunities to learn more about the physical processes behind the forecasts. Alongside this I've worked part-time on a feasibility study, investigating the effects on groundwater nitrate levels of changes to farming techniques for the Environment Agency. For me, this is a large part of the appeal of working in statistical science: so far, my academic career has taken in Anglo-Saxon archaeology, x-ray technology, image processing, hydrology, climate science and weather modelling, and I look forward to collaborating with colleagues from many other disciplines in the future.
Ever since I was really young, I have always been interested in understanding the difference between theory and practice and which leads to a reliable answer. As a result, I decided to study a subject that offers tools to investigate such questions: Statistics. To further combine theory and real world applications, I attended an MSc in Financial Econometrics at University of Essex in 2014/2015. During that course, I began to be interested in using non-stationary processes to analyse real data. Particularly, I became highly fascinated by time-frequency analysis methods (Fourier Analysis, Wavelet Analysis, etc.). Within this area, one of the biggest issues is that strong assumptions of these decomposition methods do not hold when real phenomena come into play (speech signals, financial time-series, earthquake signals, weather data, etc.). Finding a methodology to deal with these real data properties and finding a mathematical formulation that holds in these settings has, so far, been the main objective of my PhD at University College London. I have been working with my supervisor Gareth W. Peters and my next step will be to apply my methods voice signals. The final goal is to detect spoofing attacks to reduce the vulnerability of automatic speaker verification (ASV) systems.
Being a PhD student at UCL makes you part of research community with opportunities to learn from several experts and professionals. This creates a nice and challenging environment. Furthermore, you are encouraged to attend international events to share your experience. Last summer (July 2017), I attended a probability summer school in Saint-Flour (France) where I presented my research and discussed it with PhD students from all over the world. This year I am working as an intern at the Institute of Statistical Mathematics (ISM) in Tokyo to apply my research while collaborating with my supervisor in Japan.
In 2015 I completed my Statistics BSc at University of Glasgow as a double degree with the University of Bologna. During that time I realised that I really liked writing reports and exploring new techniques. During my studies I also completed a few internships. I developed a preference for finding out-of-the-box solutions for emerging problems, while the repetitive jobs that are usually assigned to analysts seemed somewhat boring to me. Combining this with the fact that I developed a few research project in high school, I decided to do a PhD in Statistics. The projects I completed in the past were very different – Item Response Theory (undergrad thesis), Analysing students’ performance towards degree classification (University of Glasgow internship), Airport terminal usage analysis (Fraport internship), Fermat problem in Geometry (HSSI) and Sidon sets (HSSI). Thus, I decided to start working in a new field – finance. I applied to the Financial Computing CDT programme at UCL and managed to get a place. During my MRes year I started developing not only understanding of the current research but also shaped novel ideas for my PhD thesis. My current research interests are related to Hawkes process, Bayesian analysis and catastrophe modelling. I am working with Dr Gordon Ross.
Yin Cheng Ng
I am a PhD student researching probabilistic machine learning with Dr Ricardo Silva. My primary goal is to develop interpretable probabilistic models for social networks and time series data that can predict the future well. I am also interested in developing scalable approximate inference algorithms for these models. Before coming to UCL, I completed the Engineering Science Program at the University of Toronto. I became interested in machine learning while developing scalable algorithms to solve data analysis problems in engineering R&D. The skill sets that I have gained from my research experience, especially during the PhD program at UCL, have proven to be extremely useful both inside and outside of the academia. Outside of the academia, I have been able to apply data modelling techniques to tackle technical problems in engineering, business and finance settings.
I believe that with ever-growing amounts of data and increasingly powerful computers, many important and challenging problems can be solved using data-driven techniques. The Department of Statistical Science together with the wider CSML community at UCL provide an excellent environment to study and research these enabling technologies.
I graduated with a BSc (Hons) in Statistics from University of Malaya in 2008 during which I completed a final year project using statistical methods to compare progress in the treatment of Tuberculosis patients as shown by digital X-Ray images versus Doctors' diagnosis. After graduating, I undertook a Research Assistant post at University of Technology Malaysia in collaboration with the Institute of Respiratory Medicine, Kuala Lumpur General Hospital. I worked on a project to discriminate between four lung states using features extracted from digital X-Ray images and clinical features to try and minimise observer error in diagnosing lung diseases with the help of a Computer Aided Diagnostic system. In 2009, I joined another research group at the Faculty of Dentistry, University of Malaya where I helped model a relationship between widths of maxillary anterior teeth and facial measurements to help rehabilitate edentulous patients. In 2012, I decided to do an MSc (Statistics) degree at the National Defence University of Malaysia investigating a Linear Functional Relationship model for circular data where I focused on Bootstrap estimates and outlier detection.
During my tenure as a research assistant, I also took up a part-time tutor post teaching Mathematics to foundation students. After passing my viva for the MSc, I was offered a part-time Statistics lecturer post at the International University of Malaya-Wales. My experiences up to that point motivated me to pursue a career in academia and led me to apply to UCL for a PhD. I am currently working on "The performance of statistical inference after model checking" supervised by Dr Christian Hennig and Dr Giampiero Marra. Working in the Department of Statistical Science at UCL has been a rewarding experience where I get to work with world class people and an environment that is accommodating and encourages research.