I specialise in incremental & online maching learning, linear optimisation, signals analysis and backend architectures. I've worked for small startups, mid-size businesses, and Fortune 500 enterprises.
MSc, Computer Science, University of Helsinki; major in software systems, extended minor in mathematics and statistics.
Finnish Defence Forces reservist.
Veikkaus, Elisa, Sanoma Media, Wayfair, Hennes & Mauritz
Aalto University, Aalto Pro, Node School Helsinki, Junction Helsinki, Alma Talent, Helsinki Reinforcement Learning
Improving the Delivery Cycle: A Multiple-Case Study of the Toolchains in Finnish Software Intensive Enterprises, IST, 2016
The Highways and Country Roads to Continuous Deployment, IEEE Software, 2015
A Behavior Marker tool for measurement of the Non-Technical Skills of Software Professionals: An Empirical Investigation, SEKE, 2015
Examining the Structure of Lean and Agile Values Among Software Developers, XP, 2014
Primary domain: online (incremental) machine learning systems -- algorithms that update model parameters per-observation in O(1) amortised time rather than requiring full batch retraining. Applied focus on contextual bandits, off-policy evaluation (IPS, DR, doubly-robust estimators), and reward estimation under partial feedback. Experience in causal estimation and author of Formative, a causal estimation library. Authored the Vowpal Wabbit off-policy evaluation tutorial in the official documentation.
Secondary domains: linear and mixed-integer optimisation (LP/MIP formulations for scheduling, routing and resource allocation), time-series and signals analysis (spectral methods, online anomaly detection), and distributed backend architectures for real-time inference (streaming ingestion, feature stores, sub-millisecond serving layers).
Public talks on contextual bandits, bandit algorithms, and cloud-native ML at Aalto University, Junction Helsinki and Helsinki Reinforcement Learning meetup.