Parham Pourmohammadi

I am a quantitative researcher with a solid foundation in financial engineering and data-driven problem-solving. Currently pursuing my M.Sc. in Financial Engineering at EPFL, I have experience in electricity price forecasting, machine learning in finance, and quantitative risk management. My expertise includes developing predictive models and analyzing complex financial datasets using tools such as Python, Matlab, and SQL. I am passionate about applying advanced mathematics and machine learning techniques to real-world financial challenges, and I thrive in environments that foster innovation and analytical rigor.

I have a diverse professional background in quantitative research and financial engineering, with experience in electricity price forecasting, machine learning, and risk management. I have worked as a Quantitative Researcher at Evolta Technologies, where I developed models for market liquidity and price forecasting. At EPFL, I contributed to financial machine learning projects, focusing on advanced data analytics and predictive modeling. My roles have involved using programming languages such as Python and MATLAB to solve complex financial problems, with a strong emphasis on innovation and analytical precision.

I have received advanced training in financial engineering through my M.Sc. program at EPFL, focusing on areas such as Financial Machine Learning, Big Data Analytics, Financial Econometrics, and Quantitative Risk Management. My coursework includes Interest Rate and Credit Risk Models, Advanced Derivatives, and Venture Capital. Additionally, my B.Sc. in Pure Mathematics, with a minor in Economics, provided me with a strong foundation in mathematical concepts like Stochastic Calculus, Convex Optimization, and High-Dimensional Probability, which are crucial for quantitative finance and risk management.