cv | Ivan Melev

Full Name	Ivan Melev
Languages	English, Bulgarian, Macedonian

Uncertainty Quantification (UQ) in Machine Learning (ML).
- MC Dropout. Its interpretation and properties.
- HiGrad. How it can (if it can) be utilised for Bayesian UQ.
- Adaptations of Bootstrapping for ML Models.
Aleatory and Epistemic Uncertainty.
- Formalization of Epistemic and Aleatory Uncertainty.
- Frequentist Interpretations of Bayesian UQ.
Robustness of ML models.
- Sensitivity of ML Models to Changes in the Inputs.

2023 -
Doctoral Candidate in Statistics

Ludwig-Maximilians University, Munich
- I am part of Göran Kauermann's chair at the Institute of Statistics at LMU - Munich. My work focuses on uncertainty quantification in machine learning.
2022-2023

Data Analyst

Allianz SE, Munich
2020-2023
Student Assistant

Ludwig-Maximilians University, Munich
- Responsible for literature reviews, data collection and data analysis for various projects related to research in cognitive psychology.

2023
- Estimation Theory (TUM Course)
- Decision Theory (LMU Course, audited)
- Generalized Linear Models (TUM Course)
- Real Analysis of a Single Variable (Self- study)
- Advanced Topics in Real Analysis (Self- study, still not finished)
2022
- Regression Analysis (TUM Course)
- Computational Statistics (TUM Course)
- Statistics on Graphs (TUM Course)
- High-Dimensional Statistics (Self- study)
- Information Theory (TUM Course)
2021
- Discrete Mathematics (Self- study)
- Time Series Analysis (Self- study)
- First Course in Probability Theory (Self- study)
2020
- Electric Circuit Theory (Self- study)
- Calculus (Single and Multivariate) (Self- study)
- First Course in Linear Algebra (Self- study)
- Psychometric Theory (LMU Course, used this book)

2020-
MSc, Neuroengineering

Technical University Munich
- A research oriented program in the intersection between experimental neuroscience and computational neuroscience. The electives I attend go in the direction of statistics and machine learning.
  - Final thesis: Measurement Error Statistical Models: A Neuroscientific Case Study (Prof. Augustin, Prof. Ploner), grade: 1.0
2019-2022
MSc, Psychology: Learning Sciences

Ludwig-Maximilians University, Munich
- A research oriented program with a focus on learning, cognition and the tools used for their statistical modeling.
2014-2018
BA, Psychology

Ss. Cyril and Methodius, Skopje
- Focus on psychometrics and statistical modeling in general.