cv

Basics

Name Katarina Elez
Label Researcher
Email katarina.elez1@gmail.com
Url https://katarinaelez.github.io/
Summary Early-career researcher in drug discovery, 5+ years of experience conducting and analyzing large-scale molecular simulations for medicinal chemistry and developing deep learning models for protein structure representation.

Education

  • 2019.11 - Present

    Berlin, Germany

    PhD
    Free University of Berlin
    Bioinformatics
  • 2017.10 - 2019.07

    Bologna, Italy

    MSc
    University of Bologna
    Bioinformatics
    • Grade: 110/110 cum laude
    • Thesis: Development of a deep learning method for model quality assessment
  • 2013.10 - 2016.12

    Bari, Italy

    BSc
    University of Bari
    Computer Science
    • Grade: 110/110 cum laude
    • Thesis: A novel approach for liver and hepatocellular carcinoma segmentation from triphasic CT images

Work

  • 2023.02 - 2023.06

    Berlin, Germany

    Researcher Intern
    Microsoft, Microsoft Research AI4Science
  • 2019.09 - Present

    Berlin, Germany

    Research Fellow
    Free University of Berlin, group of Prof. Noé
    • Analyzing MD simulations of protease-ligand complexes (MDTraj, PyEMMA).
    • Leading a screening project for TMPRSS2 inhibition which identified a novel nanomolar inhibitor and a combination preparation for treating COVID-19.
    • Majorly contributed to implementing a virtual screening pipeline comprising preparation (Schrödinger, MGLTools), docking (Smina), MD simulations (OpenMM), custom scoring (MDTraj) and active learning on molecular representations (Scikit-learn).
    • Explored different graph neural network architectures. Developed a multi-scale model (PyTorch Geometric) for protein structure representation learning.
  • 2019.01 - 2019.05

    Stockholm, Sweden

    Trainee
    Science for Life Laboratory, group of Prof. Elofsson
    • Explored different protein structure representations and three- dimensional convolutional neural network architectures.
    • Created a deep learning method (Biopython, Keras) for protein model quality assessment.
  • 2017.05 - 2017.03

    Utrecht, Netherlands

    Trainee
    Bijvoet Center for Biomolecular Research, group of Prof. Bonvin
    • Analyzed intermolecular contacts and energetics of biological/crystallographic interfaces in protein complexes.
    • Developed a random forest model (Biopython, Scikit-learn) for distinguishing between biological and crystallographic interfaces.

Skills

Programming/scripting
Python
Bash
R
MATLAB
Java
C/C++
Machine learning
PyTorch
PyTorch Geometric
Tensorflow
Keras
Scikit-learn
Data science
NumPy
SciPy
Pandas
Matplotlib
Cheminformatics
RDKit
Open Babel
Autodock Vina
Biopython
MDTraj
OpenMM
Other
Git
LaTeX
Slurm
Singularity
Docker

Languages

Montenegrin
Native
English, Italian
Full proficiency
German, Spanish
Elementary proficiency

Certificates

IELTS Academic - level C1
British Council 2017-06

Awards

Volunteer

Interests

Outdoor activities & sports
Running
Swimming
Scuba diving
Padel