|April 2017 - June 2018||
CERN Technical Student
BE-RF-FB, LHC ExperimentProject: Developing Expert Tools for the LHC
|July 2016 - September 2016||
CERN OpenLab Summer Student
EP-LBC, LHCb Computing GroupProject: FPGA Based Data Smoother for Sensor Data
|September 2018 - Present||
MSc degree, Swiss Federal Institute of Technology (EPFL)
Diploma: Master of Science MSc in Data Science
|June 2013 - October 2017||
Software Engineering and Information Technologies
Diploma: Bachelor with Honours in Electrical and Computer Engineering (B.El.Comp.Eng)
|June 2009 - June 2013||
Diploma: Diploma on completion of secondary education for gifted students
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 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.
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
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.