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Hello there 👋

I'm a Lead Educator & Team Lead at BrainStation, coordinating the delivery and content development of the full-time data science program. Previously, I was working 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.

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 (focusing on 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.

Further points of contact:

  • LinkedIn - reach out here to chat 💬
  • Website - more about my previous work 🤔

Daniel T. Soukup, PhD's Projects

aif360 icon aif360

A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.

autoencoders icon autoencoders

Autoencoder explorations: convolutional variational AE, denoising AE, and ensembles of randomized AE's for anomaly detection.

bert-on-agnews icon bert-on-agnews

A detailed walk-through of using pytorch-transformers and BERT for text classification.

jekyll-now icon jekyll-now

Build a Jekyll blog in minutes, without touching the command line.

mingpt icon mingpt

A minimal PyTorch re-implementation of the OpenAI GPT (Generative Pretrained Transformer) training

noise-dashboard icon noise-dashboard

Noise monitoring application built as part of a CivicTechTO initiative.

vrp-rl icon vrp-rl

Reinforcement Learning for Solving the Vehicle Routing Problem

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