Sahra Ghalebikesabi Sahra was born in Teheran, Iran, but grew up in Hannover, Germany. She studied Mathematics and Economics at the Leibniz University Hannover and spent a term abroad at the University of Bristol. From her second term, Sahra worked as a research assistant at the Institute for Statistics which specialised on time series analysis. In 2018, Sahra studied Statistics at the London School of Economics and Political Science where she became interested in statistical machine learning. For her MSc Dissertation, Sahra analysed scalable dynamic models for network embedding. In her spare time, she volunteers for diverse associations. Now she is starting her DPhil under the supervision of Chris Holmes and Luke Kelly.
Adam Howes Originally from Bedfordshire, Adam moved to Durham University to complete an undergraduate degree in Mathematics. In his final year at Durham, following a summer research placement in environmental data science at Cranfield University, Adam focused on statistical modules together with writing a dissertation on Bayesian optimisation. After graduation, he worked on statistical radiation biology as an intern at the Basque Centre for Applied Mathematics, before starting a masters in Statistics at the University of Warwick. Adam’s masters dissertation at Warwick was about Markov melding, a computational approach to Bayesian evidence synthesis. At StatML, Adam will be working on Bayesian methods applied to HIV surveillance and intervention in sub-Saharan Africa, funded by the Bill & Melinda Gates Foundation.
Michael Hutchinson Michael is from Norfolk in the UK. He received a Masters degree in Engineering from Cambridge, with a focus on Information, Electronic and Bio-Engineering. During undergrad he picked up an interest in machine learning, in particular statistics based modelling. His Masters dissertation on “Automated Architecture Search for Bayesian Neural Networks” looked to help the design process of Neural Networks without human intuition, and won a department prize. His research interests are broad, including probabilistic learning, uncertainty in models, and learning in abnormal settings, such as Federated or Continual learning. He hopes to explore more areas in the future, such as reinforcement learning and generative modelling. Outside of academic life he enjoys hockey and climbing.
Bryan Liu Bryan is doing a part-time PhD with the CDT in StatML. This has been made possible with the sponsorship from ASOS.com, an online fashion retailer, where he also works part-time. Prior to starting the PhD, Bryan obtained a MEng in Mathematics and Computer Science at Imperial College, with a thesis in (social-like) network models and methods. After which, he spent three years working as a data / machine learning scientist, researching a wide range of topics and transforming them into commercially viable propositions. Currently he is interested in methods in experimentation and measurement, looking into how we can improve online controlled experiments and causal inference to achieve less biased, faster, and more ethical impact measurement for commercial products and services.
Anna Menacher Anna’s interest in Bayesian statistics and Machine Learning developed during her studies in Business Administration at the University of Regensburg in Germany where she had the opportunity to work as a research assistant within those fields. Having recently finished her master’s degree in Statistics with Data Science at the University of Edinburgh, Anna is excited to further pursue her research interest within Bayesian statistics in the form of a collaboration with the pharmaceutical company Novartis and the Big Data Institute at Oxford. In particular, she will be focusing on modelling spatial dependence structures by evaluating MRI images of multiple sclerosis patients in order to quantify the severity of a patient’s disease.
Antoine Meyer Antoine studied both his bachelors and masters degrees (in pure mathematics) at the University of Warwick. In mathematics, his field of predilection was analysis. Antoine is excited to learn all about advanced statistics and machine learning during his time on the StatML programme. Outside of academics, Antoine’s big passion is classical music. He has played the cello for 10 years, listens to classical music a few hours a day and attends many concerts. He is also a huge fan of movies.
Mélodie Monod Mélodie grew up in Paris, France. She studied Statistics and Economics at the University of Geneva as an undergrad and went on to obtain an MSc in Statistics at Imperial College London. Her MSc dissertation focussed on developing statistical models and efficient algorithms to reconstruct epidemic transmission chains (i.e., transmission pairs, and the direction in which transmission occurred) from deep sequence data. Mélodie remains interested in stochastic processes, statistical modelling and computational methods with applications in the epidemiological sciences. She also has a growing interest in Bayesian statistics. Aside from Statistics, Mélodie enjoys running and reading.
Daniel Moss Daniel is interested in asymptotic behaviour of Bayesian nonparametric and semiparametric methods. Before enrolling as a StatML student, Daniel was a student at Cambridge, where he completed his undergraduate and masters (Part III) with specialism in probability and statistics. Outside of his studies, Daniel likes to run and cook/eat vegan food. He is hoping to get better at both of these things over the course of his studies! Daniel would also like to improve his language skills, of both the programming variety and the spoken (especially French).
Phillip Murray Phillip is interested in the intersection of machine learning and mathematical finance. He holds a BA in Mathematics from the University of Cambridge and has worked for several years in the environmental sector and teaching before returning to education to complete an MSc in Statistics from Imperial College. His research interests are applications of statistical learning techniques to finance, in particular the use of deep and reinforcement learning to problems such as optimal hedging of over-the-counter derivatives.
James Wei James graduated with a BSc in Economics in 2014 and a part-time MSc in Statistics in 2019, both at the London School of Economics. He first became interested in Machine Learning during his five years working as a Quantitative Researcher at Bank of America Merrill Lynch, where he focused on developing machine learning methods to forecast macroeconomic time series. However, with the recent advances in experimental capabilities within the life sciences, James became increasingly fascinated by the wide-reaching and socially impactful applications of machine learning to medicine. James’s goal during his PhD research is to help develop new tools that can be applied to this area.
Harrison Zhu Harrison grew up in Beijing and did his MSci in Mathematics at Imperial College London and Ecole Polytechnique Fédérale de Lausanne (EPFL). His masters thesis entitled ‘Topics in Spatiotemporal Modelling for Crop Growth’, supervised by Ben Calderhead, discussed Bayesian methods that can be used to provide early prediction of crop yield via remote sensing data. Currently, Harrison is especially interested in nonparametric Bayes’ and kernel methods with applications to environmental sciences, agriculture and public health and is currently supervised by Seth Flaxman (Imperial) and Maxime Rischard (Cervest Limited). He has also worked on point cloud retrieval algorithms at Shell Research before, where he worked with Farhad Bazyari and Joao Domingos. In his spare time, Harrison enjoys jogging in Hyde Park and occasionally playing some tennis. Harrison speaks English, Mandarin and French.