Topi Paananen
Senior Trading Engineer at Capalo AI
Finland
Summary
🤩
Rockstar🎓
Top SchoolTopi Paananen is a Senior Trading Engineer with nine years of experience applying probabilistic machine learning and Bayesian statistics to real-world trading and data science problems. He progressed from doctoral research at Aalto University into industry roles at Smartly and now Capalo AI, combining deep academic expertise with hands-on production engineering. His contributions to an Aalto Bayesian Data Analysis course reveal a practical focus on teaching and reproducible workflows, including improving R-based exercises for importance sampling and MCSE calculations. Comfortable moving between research and product, he brings a track record of translating advanced inference methods into robust, test-backed code deployed in trading systems. Based in Finland, he pairs rigorous technical foundations in engineering physics and ML with pragmatic delivery in fast-moving AI/finance teams.
9 years of coding experience
10 years of employment as a software developer
Doctor of Science (Technology) Machine learning Bayesian statistics, Doctor of Science (Technology) Machine learning Bayesian statistics at Aalto UniversityRWTH Aachen University
English, Finnish, German, Swedish
Github Skills (5)
bayesian-methods10
bayesian10
bayesian-data-analysis10
bayesian-inference10
r10
Programming languages (2)
Github contributions (5)
avehtari/BDA_course_Aalto
Sep 2018 - Sep 2020
Bayesian Data Analysis course at Aalto
Role in this project:
Data Scientist
Data ScientistContributions:2 reviews, 49 commits, 21 PRs in 2 years
Contributions summary:Topi's commits focus on improving and expanding an existing Bayesian Data Analysis course. The contributions involve modifying and adding to exercises, including changes to R code for posterior density plots, importance sampling, and MCSE calculations. The user also adds and modifies test cases, documentation and data. This work is directly aligned with enhancing the learning materials and practical exercises within the course.
aaltostatisticsdata-analysisbayesian-inferencebayesian
topipa/iwmm
Dec 2020 - Sep 2024
iwmm: an R package for adaptive importance sampling
Contributions:7 reviews, 19 PRs, 57 pushes in 3 years 9 months
bayesianbayesian-data-analysisbayesian-methodsrr-package
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