About

I build ultra-large-scale virtual screening pipelines accelerated by machine learning. My goal is simple: reduce the cost and time of drug discovery while improving drug selectivity. This makes new treatments more affordable, accessible, and safer with fewer side effects.

Background #

I earned my B.Sc.Eng. in Artificial Intelligence from Poznan University of Technology (Poland), supervised by prof. Jerzy Stefanowski, partially completed at Universitat Politècnica de València (Spain) and Sapienza University of Rome (Italy). My bachelor’s thesis was recognized with 1st (artificial intelligence) and 2nd (computer science) place in two independent national competitions for the best bachelor’s thesis.

After that, I worked as a Machine Learning Engineer at Molecule.One, where I developed deep learning models for chemical retrosynthesis.

I also spent time at Carnegie Mellon University, AutonLab, (USA) working on robustness of machine learning models to label noise in clinical settings, including critical care systems in collaboration with hospitals in Pittsburgh.

Beyond industry and research, I served on the Scientific Committee of the Polish National AI Olympiad, where I authored and reviewed advanced machine learning problems and lectured top AI talent representing Poland internationally.

Earlier in my career, I worked on projects spanning machine learning applications in materials science, computer vision, time series analysis, and data quality.