Researcher/developer with a demonstrated history of working in academia and industry. Skilled in data analysis and machine learning. Strong in Python and R programming, development of machine learning applications and data analysis pipelines, and system administration. Experienced in organizing technological roll-outs and scientific events. With a Master's degree in Applied Computer Science.
Active administration, development, and maintenance of the in-house automated QC system for Mass-Spectrometry measurements from various technologies. Development of different automation pipelines for proteomics data analysis, in R and Rshiny. Bioinformatics lead of specialized multiomics analysis pipelines for neo-antigen discovery. Collaborative development of a machine learning pipeline for the prediction of phosphoproteomics signatures in cancer cell-lines. Rolling out an ELN for the proteomics groups at multiple sites.
Cluster analysis of RNA-seq data based on alternative splicing patterns., Bioinformatic and statistical analysis of RNA-seq and Chip-seq data for the study of RNA silencing pathways in Yeast.
(Master's thesis) Design and implementation of RelRNA, a reinforcement learning framework for solving the RNA inverse folding problem. Collaboration on RNASynth project, an end-to-end framework for RNA design using machine learning. Development of web-based scikit-learn wrappers for the Galaxy platform (galaxyproject.org). (Masters' project) "Batch Reinforcement Learning in non-stationary environments using Gaussian Process reward models", (Short student job contracts) Integration of Khepera III mini-robot agent into the in-house Reinforcement Learning platform. Development of the network packet transfer feature for the WASABI software (human emotions simulator). Mathematical analysis of human head motion data.
Administration for automotive production line specialized servers. Database administrator, data mining officer. Hardware/network support engineer.