Data science
I'm a Manager at Konrad helping businesses mature core data capabilities, create new data-driven products, and find actual value creation opportunities using AI. Previously, I was working as Lead Educator & Team Lead at BrainStation and as a data scientist at mostly.ai on problems in data privacy, fairness in ML and building new generative models that produce the safest and most accurate synthetic data from complex data sources, such as mobility data. I have experience in various machine learning and deep learning frameworks, NLP methodology and have plenty of coding under my belt in Python.
Our Representative & Fair Synthetic Data paper with Paul Tiwald at MOSTLY AI made it to the ICLR and was quite prominently featured in multiple publications:
- in the Slate tech column with even a quote from me,
- in Andrew Ng's deeplearning.ai newsletter, The Batch, and
- in Builtin.
math
I did my PhD at the University of Toronto and my following research was centred around understanding large, seemingly random and chaotic abstract mathematical objects. How do local and global properties of certain structures interact and affect each other? Can a large network be sparse and highly connected at the same time? I have been focusing on such questions in graph theory, logic and combinatorics.
I am also passionate about teaching, active learning methods and I dabbled in high-school outreach and criptography.
Follow this link to my academic website for
- publications and preprints,
- talks, videos and presentations,
- teaching materials, and
- academic CV.