Lorenc Kapllani

Background

About Lorenc Kapllani

Highly skilled applied mathematician with a Masters degree in Computer Simulation in Science, specializing in financial mathematics. Final stages of completing a PhD in applied mathematics using machine learning. Equipped with a strong foundation in applied mathematics, computer science, statistics, machine learning, and high-performance computing. Very good command of Python (NumPy/SciPy, Scikit-learn & StatsModels, TensorFlow & Keras, Pandas), Object Oriented Programming, C++, Matlab, SAS, SQL, CUDA, MPI, OpenMP, with expertise in computational techniques for pricing financial contracts and analyzing complex financial data. Extensive experience in developing state-of-the-art algorithms using deep neural networks for solving previously intractable problems with applications in finance. Pioneered research in assessing the reliability and efficiency of deep learning-based algorithms, quantifying their uncertainty for improved decision-making. Currently focused on improving the accuracy of deep learning-based algorithms through the application of differential deep learning. Former credit risk analyst with 2 years of industry experience, adept at data-driven decision-making and risk assessment in the financial sector. Skilled in handling large-scale datasets, performing comprehensive data analysis, modeling, and optimizing credit risk strategies using SAS statistical software.