Daniel Kaiser
CV

Curriculum vitae · October 2026

Daniel Kaiser

Machine learning researcher. PhD Fellow, Integreat & UiT The Arctic University of Norway.

PhD ends in 2027. Open to research visits in spring 2027 and to postdoc and research roles from autumn 2027.

m@dk.fo dk.fo Google Scholargithub.com/kaiserdanx.com/spectate_orlinkedin.com/in/kaiserd Tromsø, Norway

Research

Test-time compute and reasoning efficiency in large language models. Benchmarks that separate the sources of difficulty (CogniLoad), exact decompositions of what generated tokens buy (Beyond Accuracy) and per-question allocation of reasoning effort (When Is More Reasoning Worth It?). Controlled, factorial evaluations at scale, with simultaneous inference. Earlier, machine learning on financial statements. Experiments run on the LUMI supercomputer (EuroHPC) and on commercial APIs.

Publications

  1. 2026

    When Is More Reasoning Worth It?

    Daniel Kaiser, Benjamin Ricaud. Under review · 2026

  2. 2026

    Beyond Accuracy: Decomposing the Reasoning Efficiency of LLMs

    Daniel Kaiser, Arnoldo Frigessi, Ali Ramezani-Kebrya, Benjamin Ricaud. Under review · 2026 · Earlier version: NeurIPS 2025 Workshop on Efficient Reasoning · arXiv (earlier version), Workshop version

  3. 2026
  4. 2025

    Decomposing Reasoning Efficiency

    Daniel Kaiser, Arnoldo Frigessi, Ali Ramezani-Kebrya, Benjamin Ricaud. NeurIPS 2025 Workshop on Efficient Reasoning · OpenReview

  5. 2020

    Machine Learning-Based Financial Statement Analysis

    Amir Amel-Zadeh, Jan-Peter Calliess, Daniel Kaiser, Stephen Roberts. SSRN · 2020 · SSRN

Education

  1. 2024 –

    PhD, Machine Learning · UiT The Arctic University of Norway, with Integreat

    Thesis on test-time compute and reasoning efficiency in large language models. Supervised by Arnoldo Frigessi and Benjamin Ricaud, with Ali Ramezani-Kebrya. Expected 2027.

  2. 2018 – 20

    MSc by Research, Engineering Science · University of Oxford, Mansfield College

    Thesis “Machine Learning Based Financial Statement Analysis”, with Stephen Roberts, Jan-Peter Calliess and Amir Amel-Zadeh. Full scholarship from the Oxford-Man Institute of Quantitative Finance. Completed in half the standard time. Commendation from the MPLS Division for the viva.

  3. 2014 – 17

    BSc Economics · WU Vienna University of Economics and Business

    Quantitative economics and econometrics. Thesis “Deep Learning Fundamental Market Valuations”, presented at IPAM, UCLA (2018). Top 1% of a 3,200-student cohort by study speed.

  4. 2008 – 13

    Higher Technical School, Computer Science · HTL St. Pölten

    Thesis on high-performance computing on virtualised GPUs in the cloud. Graduated with high distinction.

Positions

  1. 2024 –

    PhD Fellow, Machine Learning · Integreat & UiT The Arctic University of Norway · Tromsø / Oslo

    Test-time compute and reasoning efficiency in large language models. Experiments on the LUMI supercomputer (EuroHPC) and on commercial APIs. Three papers so far, one at ICLR 2026 and two under review.

  2. 2023 – 24

    Machine Learning Engineer · Orderfox

    Fine-tuned and self-hosted multimodal LLMs for structured extraction, replacing third-party APIs. Load-scaling ML services on AWS with Terraform. Multimodal embeddings for CNC machining.

  3. 2023

    Data Engineer, then consultant · CH Media

    Snowflake warehouse and behavioural-data pipelines for regional news portals. Serverless ingestion on AWS. A real-time store for article recommendations.

  4. 2022

    Data Scientist · Smallpdf

    Transformers for document AI in three product teams. Causal models of churn, lifetime value and revenue. Experiments on large event logs.

  5. 2020 – 21

    Quant · Rothschild & Co

    Quantitative investment management for discretionary multi-asset mandates. Allocation and hedging models, automated reporting, monitoring and scoring.

  6. 2019 – 20

    ML Research Scientist · avax / Deep Impact · Winterthur

    Real-time biometric face recognition and re-identification pipelines (CUDA, TensorRT, DeepStream). Swiss security clearance.

  7. 2017 – 18

    Co-founder · plan.uni

    Non-profit course-scheduling web app used by more than 10,000 students, with several awards. A large single-page app, native PWAs and distributed crawling.

  8. 2014 – 17

    Liquidity risk, tax technology, portfolio management · Raiffeisen Bank International · PwC Austria · KA Finanz

    Liquidity risk for 15 network banks and Basel III reporting at RBI. A big-data tax platform at PwC. Fixed-income analysis at KA Finanz. All alongside the BSc.

Recognition and service

  • Reviewer for NeurIPS, ICLR, ICML and NLDL.
  • Top 0.3% of SSRN papers by downloads, top 1.5% of SSRN authors.
  • Second place, Chicago Quantitative Alliance academic competition, Fall 2020.
  • Oxford-Man Institute scholarship and an MPLS Division commendation for the viva.
  • Invited talk on deep learning for market valuation at IPAM, UCLA, 2018.
  • Lectures in a Generative AI course and at seminars and workshops.

Skills

Research
Controlled evaluation design, factorial experiments, exact decompositions, bootstrap and simultaneous inference, causal inference.
Models
PyTorch, Hugging Face, fine-tuning and quantisation, LLM serving at scale, multimodal embeddings, reasoning-model evaluation.
Systems
Python, TypeScript, C/C++, Rust, Elixir, SQL. AWS, Kubernetes, Terraform. Snowflake, Spark, Kafka, dbt. GPU cluster jobs on LUMI.
Finance
Portfolio construction, asset allocation and hedging, liquidity risk, fundamental analysis.

Software

  • CogniLoad: puzzle generator, evaluation harness and released dataset (code, dataset, results site).
  • plan.uni: non-profit course-scheduling web app used by over 10,000 students in Vienna, co-founded 2017.
  • This site: a static Astro build with a WebGL Physarum simulation and hand-written research explorables.