Hi, I'm
Jelena Banjac

Welcome to my page!
I am a student playing with data at the moment.

Date Experience Location
April 2017 - June 2018
CERN Technical Student

BE-RF-FB, LHC Experiment

Project: Developing Expert Tools for the LHC
Geneva, Switzerland
July 2016 - September 2016
CERN OpenLab Summer Student

EP-LBC, LHCb Computing Group

Project: FPGA Based Data Smoother for Sensor Data
Geneva, Switzerland

Date Degree Location
September 2018 - Present
MSc degree, Swiss Federal Institute of Technology (EPFL)

Data Science

Diploma: Master of Science MSc in Data Science

June 2013 - October 2017
BSc degree, Faculty of Technical Sciences, University of Novi Sad

Software Engineering and Information Technologies

Diploma: Bachelor with Honours in Electrical and Computer Engineering (B.El.Comp.Eng)

Novi Sad,
June 2009 - June 2013
Mathematical Grammar School

Diploma: Diploma on completion of secondary education for gifted students


Brain Segmentation

Semester project

Developing software tools for handling magnetically collected ultra-thin sections of brain tissue in a large image to determine section's coordinates. In order to predict these coordinates, the goal was to use state-of-art object instance segmentation framework called Masked Region-based Convolutional Neural Network (Masked R-CNN) on the dataset containing sections of brain tissue in the light microscopy images. The predicted coordinates of the sections will later be used for automated image acquisition in high resolution electron microscope.
– Supervisors: Thomas Templier (CIME), Martin Jaggi (MLO)

Developing Expert Tools for the LHC

CERN Technical Student Project

Developing software tools in Python for automated, precision setting-up of low-power level radio frequency (LLRF) loops, which will help expert users to have better control and faster setting-up of the radio-frequency (RF) system in the Large Hadron Collider (LHC) experiment. The aim was to completely redesign the software architecture, to add new features, to improve certain algorithms, and to increase the automation.
– Supervisor: Helga Timko.

Movie Recommendation System Simulation

Machine Learning course project

The simulation of recommendation system for recommending movies to users, and predicting ratings users could give to movies they have not rated yet. Training models with Item-Based and User-Based K nearest neighbours which belong to Collaborative filtering methods.
– Supervisor: Vuk Malbasa

FPGA Based Data Smoother for Sensor Data

CERN OpenLab Project

Tested to use a sensor smoothing algorithm on an FPGA to directly reduce the noise on raw sensor data in general. Algorithm is implemented on a modern Cyclon V SOC FPGA using HDLs.
– Supervisors: Christian Faerber, Jonathan Machen, Jean-Christophe Garnier.