Biografie
Aygul Zagidullina studied Applied Mathematics, focusing on applications in Economics and Finance. She obtained her Ph.D. in Quantitative Methods, specializing in the Estimation of High-dimensional Covariance Matrices through the use of Regularization techniques and Factor Models.
Her academic background at the intersection of Statistics, Computer Science, and Data-driven Inference enabled her to succeed in industry 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 has many years of teaching experience at various levels: Bachelor, Master and Ph.D.
She has written the university level textbook on High-Dimensional Covariance Matrix Estimation with Springer Nature (Switzerland), that came out in 2021.
Aygul Zagidullina is also the co-organizer of the Women in Data Science conference in Zürich, WiDS Zürich and a member of Women in AI initiative.
Education
- Ph.D. in Quantitative Methods
- Diploma (B.Sc. + M.Sc.) in Applied Mathematics
- Postgraduate Machine Intelligence Program in Computer Science
- Certificate in Didactics for Higher Education
Publikations
Book:
Peer-reviewed articles:
- Daniele, Maurizio, Winfried Pohlmeier, and Aygul Zagidullina. “A Sparse Approximate Factor Model for High-Dimensional Covariance Matrix Estimation and Portfolio Selection.” Journal of Financial Econometrics (2024): 1–30, https://doi.org/10.1093/jjfinec/nbae017
- Calzolari, Giorgio, Roxana Halbleib, and Aygul Zagidullina. “A Latent Factor Model for Forecasting Realized Variances.” Journal of Financial Econometrics (2021): 860–909, https://doi.org/10.1093/jjfinec/nbz036
Preprints:
- Björn Jensen, Johann Lodewyks, Aygul Zagidullina. “Digital Shadow Hydropower: Projektdokumentation (Kraftwerke Oberhasli AG)” (2024)
- Julia Netzel, Safouane El Ghazouali, Aygul Zagidullina, Aitor Egurtzegi, Liu Ping, Arnaud Gucciardi, Francesca Venturini, Umberto Michelucci. “Advancing Image Quality Assessment: Selective SSIM Method for Fine-Structured Image Evaluation” (2024)
- Aygul Zagidullina, Georgios Patoulidis, and Jonas Bokstaller. “Model Bias in NLP -- Application to Hate Speech Classification Using Transfer Learning Techniques.” arXiv.org (2021): Web.
Bachelor/Master Theses:
- Raphael Gubser, Aygul Zagidullina. “Tracking Topic Evolution over Time using Transformer Models” (2024) (joint work with https://www.gopf.com/)
- Joel Kessler, Aygul Zagidullina. “AI for Recording Procurement Documents” (2024) (joint work with https://konplan.com/)
- Austin George, Aygul Zagidullina. “Leveraging User Feedback to Train LLMs in Evaluating Conversations” (2024) (joint work with https://www.paretolabs.ch/)
Current Roles at HSLU:
- Senior Lecturer in Applied Mathematics, Programming for Data Science, Introduction to AI
- Module Responsible in AI Competition (Kaggle Challenge)
- Co-Head in CAS Machine Learning and Data Science for Medicine and Health
- Thesis Supervisor in AI & ML domain
- Senior Research Scientist at AI Robotics Research Lab
