Dan

Dan has developed a collection of well-structured educational resources, aimed at improving both background knowledge and exam-taking skills. He loves attempting more demanding questions with his students. This allows him to better understand the student’s background and suggest extra exercises specifically designed to remedy knowledge gaps while minimising the number of required hours. He has an excellent understanding of Mathematics, which allows him to draw connections between fields and explain complicated concepts in simple terms. He is positive this is the key factor which helps him instil a sense of confidence in my tutees, despite tackling difficult questions.

Dan has extensive experience teaching in remedial situations too, with excellent results. He is most proud of bringing a secondary school student from failing to the top 3 in his school in under 6 months. He quickly adapts to the student’s style and matches individual needs. He strongly believes teaching should be a guided self-education process and thinks that one of the best ways to tutor (especially in an online environment) is to concentrate on homework exercises. The student has time to attempt the questions in their own time, at their own pace; thus, we can get the most out of the session by mainly discussing questions that the student has had difficulty solving.

Dan has vast teaching experience, from the sixth form to research-level Mathematics. In the past, he obtained excellent results tutoring A-level Maths and Further Maths, IB and specialises in university admission tests such as MAT, TMUA, STEP and interview prep. As a former participant in the National Romanian Olympiad, he likes problem-solving and challenges his students to think outside the box and see Mathematics in a different light. In terms of degree and master’s level, Dan was a teaching assistant for many Analysis, Probability Theory and Statistics courses at Imperial College London. The topics he tutors include:

• Introductory courses: Real and complex analysis, Metric spaces and topology, Vector calculus, Linear algebra, Groups rings and fields
• Statistics: Probability for statistics, Time series, Markov chains and MCMC methods, Statistical machine learning in R and Python, Deep learning in PyTorch, Tensorflow and Jax
• Financial Mathematics: Stochastic Calculus and stochastic integration, Stochastic differential equations, Option pricing, Interest rate modelling

The overall goal of Dan’s research project is to model complex dynamics in a way that incorporates available knowledge about the system of interest, through ambit fields and trawl processes. You can find his project description on the UK research and innovation webpage and his last preprint on arxiv. He does both rigorous mathematical theory and implements numerical simulation studies. Among other topics, he hopes to apply his research to the modelling, prediction and anomaly detection of Earth Surface Dynamics(MoPreAnESD) in partnership with a team from the Technical University of Munich.