Edwin Chen Email & Phone Number

A committed postgraduate from Oxford Masters in Mathematical Sciences, keen to develop career in the rapid growing field of quantitative finance. Confident in leveraging mathematical models, computational analysis, and large datasets to analyze and predict the financial markets. Strong capacity to apply programming tools to create robust financial strategies. Passionate in machine learning, clustering algorithms, and AI to identify data patterns. Based in London/Oxford and actively seeking opportunities in the field of Quantitative Analysis/Trading/Data Science. My availability for work commences from September 2023. Key Expertise:• Proficient in Applied Mathematics topics, especially in Optimization, Numerical Linear Algebra, Modelling and Stats.• Strong at performing data analysis, building data models/dashboards to generate business insights• Can shoulder heavy responsibility and exceed expectations in a line of work that requires accuracy• Good communication skills, able to share complex findings to technical/non-technical audiencesResearch Experience: • Distinction in Dissertation in Randomized Numerical Linear AlgebraOriginal Research Dissertation in Perturbations of Functions of Toeplitz and near-Toeplitz Matrices, with original results in establishing perturbation bounds with both the matrix power and exponential cases. Have demonstrated computationally a much sharper decay rate of the matrix elements as one moves away from the main diagonal than previous works. Intensive use of Python in this project.• Distinction in Project in Theories of Deep Learning A computational based study on Neural ODEs. Examined cases experimentally where Neural ODEs fail to learn certain dynamical trajectories and compared the performances of Euler and Runge-Kutta ODE Solvers on learning large data sets such CIFAR-10. Also compared experimentally on how ANODEs can improve upon NODEs. Earned invaluable computational experience in managing and coding a large novel project.• Distinction in Project in Networks A computational study on a generalized approach to estimate the embedding dimensions of various networks. Experimented on various Twitter networks in different UK cities to determine the optimal embedding dimensions, and compare it with the intrinsic dimensions estimated from the Two-NN algorithm. Able to determine an empirical relationship between the two dimensions and studied the effects on these estimates by certain graph properties. Gained the insights of computational modelling and refining existing algorithms in the project