Research is a pretty broad term, and therefore this category of projects is quite heterogeneous. I define research as various statistical experiments that go beyond data analysis (hypothesis testing including A/B testing, experimental design, conducting surveys & customer research, descriptive statistics), as well as non-standard applications of analysis (like OSINT) and direct investigation for further integration (as R&D engineers do), e.g. ML papers research as part of business strategy consulting.
The following list represents some of my highlighted research projects.
Deep learning
A TensorFlow implementation and demonstration of Kolmogorov-Arnold Network proposed in April 2024 (arXiv:2404.19756)
— A TensorFlow implementation and demonstration of Kolmogorov-Arnold Network proposed in April 2024 (arXiv:2404.19756)Hypothesis testing
A feature-rich library for running Bayesian A/B tests for experiments and key metrics analysis
ABforge is designed for a variety of use cases, utilizing the Bayesian approach to implement different variables and tests, including binary, Poisson, normal, delta-lognormal and discrete tests on different metrics. It's especially useful for analysing key metrics in marketplaces, such as conversion rates, ticket size, ARPU differences between test variants, etc. The main idea of this library is to create an all-in-one tool for running A/B tests separately from closed-source code and business logic.
— A feature-rich library for running Bayesian A/B tests for experiments and key metrics analysisComputer vision
My thesis project: the lightweight and fast scene text detection based on EAST architecture and MobileNet layers
— My thesis project: the lightweight and fast scene text detection based on EAST architecture and MobileNet layers
