As a Ph.D. graduate in Mathematics/Applied Mathematics, my academic journey has provided me with a deep understanding of mathematical objects, advanced methodological approaches, and their applications in various real-world scenarios. With a interest for complex and latent information within big datasets, I work on statistical mathematical approaches, as well as soft computing, to address real-world problems and gain deeper insights. I have diligently sharpened my analytical skills and developed a strong background in probability theory, statistical inference, statistical modelling and modern statistical methods (i.e., Wavelets in statistics & Fuzzy data analysis).
My doctoral research direction was the analysis of complex networks using Markov chains. Interesting results were obtained by introducing a damping component and coupling theorems. This work not only provided new insights into the current knowledge base, but also demonstrated my ability to conduct research and analyze big data. My current research interest is data science with artificial intelligence. From my time as a teaching assistant to my current position as a lecturer, I have actively involved in teaching, mentoring undergraduate students and graduate students. My dedication to education and knowledge exchange extends beyond the usual face-to-face classroom setting, as I routinely attend international workshops, conferences and community research. In addition, I have written for peer-reviewed publications and book chapters. In conclusion, my vision is to improve existing or develop mathematical methodologies that can be used to contemporary challenges in fields, such as healthcare and climate change. This involves combining my expertise in mathematical statistics with emerging technologies such as machine learning, artificial intelegence and big data analytics.
