The hardest computer science and machine learning problems you have; the ones with the potential to change the world we live and work in. Despite recent work history on the CTO-level, I'm still hands-on, believing that you cannot lead technical teams unless you continuously hone your technical skills.
Open as employee, freelancer, founder, or pre-seed investor where I can be genuinely useful.
Primary domain: online (incremental) machine learning — algorithms that update 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 and non-stationarity. Author of Formative, a causal estimation library, and author of the off-policy evaluation tutorial in the official Vowpal Wabbit documentation.
Secondary domains: linear and mixed-integer optimisation (LP/MIP formulations for scheduling, routing and resource allocation), time-series and signals analysis (spectral methods, change-point and anomaly detection, pattern-of-life / normalcy modelling), and distributed backend architectures for real-time inference (streaming ingestion, feature stores, sub-millisecond serving layers).
Built some of the first real-time contextual bandit systems for high-volume recommendations in the Nordics, and real-time webpage optimisation for fashion retail. Constraints: per-observation online updates, partial (bandit) feedback, non-stationary reward distributions, and serving inside sub-second request budgets. Evaluation done off-policy (IPS / doubly-robust) rather than on live traffic where experimentation was constrained.
Bidding algorithms for second-price advertisement auctions under partial information, allocating ten-digit (€1B+) annual media budgets. Constraints: censored feedback from lost auctions, budget pacing, and non-stationary competition.
Estimated the causal impact on retention of a third-party bonus/loyalty program carrying eight-digit (€10M+) annual license fees, to decide whether the spend was actually driving incremental behaviour. Constraints: no clean randomised holdout, confounding from self-selecting engaged users, and a decision that had to withstand scrutiny. Used instrumental-variable regression and difference-in-differences to isolate the effect; the incremental retention did not justify the fees, and the program was decommissioned, saving €10M+ annually.
Implemented payment processing (charges, holds, automatic refunds) and high-availability hosting for retail with multi-million annual revenues, alongside event-driven backends and a storefront for personalised fashion.
MSc, Computer Science, University of Helsinki; major in software systems, extended minor in mathematics and statistics. Natively trilingual (Swedish, Finnish, English).
Finnish Defence Forces active reservist.
On Prompting, Priors, and What It Takes for LLMs to Produce Novelty, 2026
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