Biografie
Aygul Zagidullina studied Applied Mathematics, focusing on applications in Economics and Finance. She obtained her Ph.D. in Quantitative Methods with a major in Econometrics and minor in Public Economics from the University of Konstanz in Germany.
She worked in industry for many years, gaining experience as a Quantitative Modeler and Data Scientist.
She continued her education in Computer Science with specialization in Machine Intelligence at ETH Zürich, obtaining broad expertise in AI & ML domain. Her interest centers around the convergence of Data-driven solutions and its potential for societal benefit.
Aygul Zagidullina is also the co-organizer of the Women in Data Science conference in Zürich, WiDS Zürich.
She has many years of teaching experience at various levels: Bachelor, Master and Ph.D.
She has written the university textbook on High-Dimensional Covariance Matrix Estimation with Springer Nature (Switzerland), that came out in 2021.
Education
- Ph.D. in Quantitative Methods
- Diploma (B.Sc. + M.Sc.) in Applied Mathematics
- Postgraduate Machine Intelligence Program
Publikations
Book:
- Zagidullina, Aygul. High-Dimensional Covariance Matrix Estimation: An Introduction to Random Matrix Theory. Cham, Switzerland: Springer, 2021.
Peer-reviewed articles:
- Calzolari, Giorgio, Roxana Halbleib, and Aygul Zagidullina. “A Latent Factor Model for Forecasting Realized Variances.” Journal of Financial Econometrics 19.5 (2021): 860–909. Web.
- Daniele, Maurizio, Winfried Pohlmeier, and Aygul Zagidullina. “Sparse Approximate Factor Estimation for High-Dimensional Covariance Matrices.” (under revision in JFE)
Preprints:
- Zagidullina, Aygul, Georgios Patoulidis, and Jonas Bokstaller. “Model Bias in NLP -- Application to Hate Speech Classification Using Transfer Learning Techniques.” arXiv.org (2021): Web.
Current Roles at HSLU:
- Lecturer in Applied Mathematics, Programming for Data Science
- Lecturer/Coach in AI Competition (Kaggle Challenge), Data Science Project I and II
- Co-Head in CAS Machine Learning, Data Science for Medicine and Health
- Thesis Supervisor in AI & ML domain
- Research Scientist at AI Robotics Research Lab
