I'm a Sr. Software Architect & Team Lead for the Data Science & Engineering team at Konrad helping our clients mature core data capabilities, create new data-driven products, and find actual value creation opportunities using AI. Previously, I was working as a 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.
- Representative & Fair Synthetic Data by Paul Tiwald, Alexandra Ebert & Daniel T. Soukup. Accepted as ICLR 2021 workshop paper.
- Patent on Synthetsizing Mobility Traces with Klaudius Kalcher and Michael Platzer from 2022.
I did my PhD at the University of Toronto and my following research was centred around understanding large, seemingly random and chaotic combinatorial objects, specifically very large graphs. 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. Click for math publications here.
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