• Name: Tommy Odland
  • Education: M.Sc. in applied mathematics
  • Occupation: Consultant at TOOD AS (Oslo, Norway)
  • Expertise: Machine learning, optimization, statistics and algorithms
  • Email: tommy.odland at gma...
  • GitHub: @tommyod

I’m an applied mathematician working with quantitative modeling and data science.

What I do

I use analytics, machine learning and optimization to create robust solutions that help solve real-world business challenges. To me, this means combining theoretical knowledge with practical see-through.

I work on the entire process from idea to execution: data collection, data processing, mathematical modeling, software development and deployment.

Work experience

Interesting mathematical problems arise everywhere in daily life and industry.

Here are some cases I have worked on in industries such as banking, maritime, energy and retail:

  • Risk analysis in banking. Historically banks used subjective judgement and simple hand-crafted rules to assess the creditworthiness of existing and potential customers. These days statistical models aid decisions by predicting the probability of default. Models are subject to regulatory supervision and must be transparent.
  • Predictive maintenance. Rotating mechanical equipment has sensors that produce huge amounts of data. By looking for anomalies in these high-dimensional time series, it is sometimes possible to detect failures and intervene before the machinery actually break down.
  • Forecasting future customer purchases. By analyzing historical purchases one can forecast future customer traffic and behavior. Such forecasts may be used to, e.g., stock the right amount of product or schedule employee shifts to match expected customer traffic.
  • Shift scheduling. Retailers might have hundreds or thousands of employees working in different physical stores. Each employee has individual preferences (morning shifts, late shifts, reduced work hours, and so forth) and each store has different labor requirements. Algorithms can efficiently allocate workers to stores to make employees happy and stores profitable.
  • Optimizing logistics. A wide variety of logistics problems can be formulated as optimization problems and solved by computers: stores have limited amounts of shelf-space, shipping costs money, storage costs money, etc. Some products are scarce and others abundant, some are in demand and others are out of fashion. Mathematical models can quantify uncertainty and aid in fast and efficient decision making.
  • Large-scale data assimilation. When modeling complex phenomena such as geology or weather, one seeks to update the simulation model by integrating real-world observations. This field is called data assimilation, and is a Bayesian statistics problem. The models contain millions of parameters and might take days to run—specalized algorithms are needed. Implementing these algorithms requires deep theoretical and computational expertise.

Outside of work I have made contributions to the discussion on Norwegian school choice models and helped the Red Cross solve an allocation problem.

Want to learn more about what kind of problems that mathematical modeling can help you solve? Watch the webinar “Maskinlæring for ledere” or read the article “Er maskinlæring noe for deg?”.

I have also written 50+ articles on this website, where I try to highlight how useful and interesting math is. Some fun problems I’ve used mathematics to solve include:


Most of my software is written for clients, but I also participate in open source. My two most popular projects are:

Some of my other projects are streprogen, paretoset, treedoc and abelian.

Papers and writings

  • (2022) “Blandingsmodellens svakheter” by Odland. A note published in utdanningsnytt.no, highligting the disadvantegeous properties of a school choice model proposed by politicians in Oslo.

  • (2020) “Mangelfull evaluering av inntaksmodeller” by Odland and Murray. A discussion of the mathematical properties of school choice models, and how these properties have been absent from public discussion. Two new models are proposed, and the article was published in Bedre Skole” nr 3/2020. [preprint]

  • (2019) “Pyglmnet: Python implementation of elastic-net regularized generalized linear models” by Jas, …, Odland, et al. Paper about a software package published in Journal of Open Source Software. [JOSS]

  • (2019) “Ratio-Balanced Maximum Flows” by Akramia, Mehlhorn, Odland. Published paper on optimizing balanced flow in a bipartite graph. Solved by a combinatorial algorithm and by reduction to QP. [ScienceDirect] [arXiv]

  • (2017) “Fourier Analysis on abelian groups; theory and applications” by Odland. Thesis about group theory, Fourier analysis and abelian categories. Includes software for computations. [BORA]

Educational material

University level

High school level

I worked for ENT3R for many years, and created content for mathematics education.