Dmitri Soshnikov
Associate Professor
Moscow, Moscow City, Russia
Summary
🤩
Rockstar🎓
Top School
Top expert inAzure-Centric AI and Bot Development
Dmitri Soshnikov is an associate professor and veteran technologist with over 12 years of industry experience and a three-decade footprint in academia and consulting, blending roles as a Microsoft Cloud Developer Advocate and long-time faculty at MIPT, HSE and MAI. He co-founded MAILabs to shepherd student AI and novel UI projects and runs SHWARSICO providing training in functional programming, mobile development and new interaction modalities. Dmitri’s open-source contributions to high-profile Microsoft learning repos (Data Science for Beginners, AI for Beginners, Ignite training) reflect hands-on expertise in data science, ML pipelines and Azure-based NLP workflows. Equally comfortable lecturing and coding, he bridges research-grade rigor (PhD, Moscow Aviation Institute) with practical developer advocacy and workshop-driven education. A less obvious strength is his track record of distilling complex AI concepts into accessible, notebook-based tutorials used by global learning communities.
12 years of coding experience
24 years of employment as a software developer
IB Computer Science, IB Computer Science at United World College of the AdriaticPhD Computer Science, PhD Computer Science at Moscow Aviation Institute (National Research University)
Stackoverflow
727reputation
14kreached
29answers
0questions
Github Skills (30)
data-visualizations10
python10
data-science10
matplotlib10
scikit10
pandas10
statistics10
machine-learning10
data-visualisation10
azure-machine-learning10
numpy10
ml10
sentiment-analysis10
deep-learning10
tensorflow10
Programming languages (10)
Github contributions (5)
microsoft/AI-For-Beginners
Sep 2021 - Aug 2022
12 Weeks, 24 Lessons, AI for All!
Role in this project:
ML Engineer
ML EngineerContributions:102 commits, 10 PRs, 53 pushes in 11 months
Contributions summary:Dmitri added a section on perceptrons, including the necessary libraries such as `pylab`, `sklearn.datasets`, and `numpy`. The code creates a toy problem using `make_classification` from Scikit Learn to generate a random classification dataset. The changes include code for data splitting, plotting the dataset, and setting up the basic components for a perceptron model.
nlprnndeep-learningcomputer-visionmachine-learning
microsoft/Data-Science-For-Beginners
Aug 2021 - Oct 2021
10 Weeks, 20 Lessons, Data Science for All!
Role in this project:Data Scientist
Data ScientistContributions:36 commits, 1 push in 1 month
Contributions summary:Dmitri's commits primarily involve adding and modifying Jupyter notebooks focused on introducing probability, statistics, and data science concepts. Their contributions include code and content related to random variables, distributions, and correlation analysis within the context of a baseball dataset. They demonstrate the use of Pandas, NumPy, Matplotlib, and demonstrate the ability to use libraries, such as nlp_rake, to extract relevant concepts from text data.
data-analysispythonsciencedata-sciencemachine-learning
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