Work Experience

With a Ph.D. in theoretica/computational physics and an additional B.Sc. in computer science, I have 4 years of experience as a machine learning engineer and scientist in the industry, followed by 4 years of postdoctoral research. My training and work experience are in computational methods, machine learning, distributed and decentralized protocols, as well as statistical and quantum physics. My empirical interests lie in interdisciplinary applications of machine learning and physics.

Currently, I’m working as a Visiting Assistant Professor at the University of Southern California (USC) and a Visiting Researcher at the Information Sciences Institute (ISI) (see the main page for details).

In my prior role as a Lead Research Scientist at Fetch.ai, I focused on ensuring the stability and security of decentralized systems. My team consisted of four researchers, and our primary responsibility was to design a new consensus protocol with high throughput and robust security guarantees, while analyzing potential attack scenarios to ensure the safety of the whole system. Through this work, my team and I were able to publish a conference paper and register one commercial patent. This experience gave me a deep understanding of the intricacies of decentralized systems and showed the importance of balancing security and performance when creating user applications. Additionally, working in the rank of a team leader and a machine learning expert, I was in a position to explore the potential applications of machine learning algorithms in decentralized environments. I have proposed several new projects regarding applications of AI in decentralized multi-agent systems, contributing to the success of the company, see e.g., an academic article or a use case demonstration.

From 2016 to 2018, I worked as a machine learning engineer at TypeScore, a UK-based tech startup. In that role, I focused on addressing challenges that arise when training machine learning models on heterogeneous data types. As one of the primary engineers at the company, I was responsible for overseeing the entire machine-learning pipeline, including data collection, algorithm selection, model training and evaluation, as well as model updating and re-training. My experience at TypeScore required me to develop a deep understanding of machine learning and data analysis techniques, as well as learn how to optimize models for performance when using a diverse set of data sources. Additionally, my responsibility for maintaining and monitoring the entire machine-learning pipeline gave me the skills necessary to manage complex technical systems.

I received my doctorate in Theoretical Physics from Jagiellonian University in 2016. During my Ph.D., I was a research member in two EU-funded projects focused on correlations and coherence in quantum materials and structures, and fundamental properties of strongly correlated systems. While working on my doctorate, I have published seven peer-reviewed articles in leading international journals and presented the results of my work at several international conferences (see the list of publications in the Research section of the page). In addition to my research, I led practical sessions for Physics with Biophysics Elements, Statistical Physics, and Physics Laboratory, and for several years, I was the main organizer of the Physics Classes for Gifted High-School Students outreach program.

To learn more about my research and teaching experience, check the research or the teaching sections of this website.