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Kistagången 16
164 40 Kista
Sweden

KTH EECS
Software and Computer Systems, Daniel Lundén
Electrum 229
164 40 Kista
Sweden

I am a PhD student at the School of Electrical Engineering and Computer Science, Division of Software and Computer Systems, KTH Royal Institute of Technology.

I do research in probabilistic programming, an interdisciplinary field with influences from computer science, probability theory, statistics, machine learning, and artificial intelligence. In my research, I focus on developing mathematical foundations and efficient compilers for probabilistic programming languages. I am particularly interested in programming language theory, compilers, and static program analysis.

My principal supervisor is David Broman (KTH), and my assistant supervisors are Lawrence Murray (Uber AI) and Joakim Jaldén (KTH).

KTH Royal Institute of Technology Stockholm

Doctor of Philosophy (PhD) July 2017 –

Degree programme in Information and Communication Technology. My PhD defense takes place on March 29.

KTH Royal Institute of Technology Stockholm

Master of Science August 2015 – June 2017

Bachelor of Science August 2012 – June 2015

Master of Science in Engineering (Civilingenjör) August 2012 – June 2017

Degree Programme in Computer Science and Engineering (Datateknik).

SICS Swedish ICT Stockholm

Researcher June 2016 – February 2017

I worked with the Unison project: a code generator using a combined constraint model of register allocation and instruction scheduling to generate potentially optimal code. My task was to update the target description of a processor to the most recent version within the project.

Designingenjörerna Stockholm

Software Developer June 2015 – August 2015

I worked as a front-end Android and back-end PHP developer.

Designingenjörerna Stockholm

Software Developer June 2014 – August 2014

I worked with both front-end and back-end web development in JavaScript and PHP.

My Academy Stockholm

Study Coach May 2013 – May 2016

I assisted high school students with mathematics and related topics during the semesters.

European Symposium on Programming

Distinguished Artifact Award 2022

HSB Bostadsrättsförening Östra Polhem 4:2 Järfälla

Board Member (Styrelseledamot) 2020 –

KTH Royal Institute of Technology Stockholm

Doctoral Student Representative in the ICT Doctoral Program Council 2018 –

PROBPROG

Program Committee Member 2021

NeurIPS

Reviewer 2021

KTH Royal Institute of Technology Stockholm

Course Responsible for IS1200 Computer Hardware Engineering Spring 2021

Teacher in IS1200 Computer Hardware Engineering

Teacher in IS1500 Computer Organization and Components August 2017 –

Teaching Assistant in DD1361 Programming Paradigms

Teaching Assistant in DD2395 Computer Security

Teaching Assistant in DD1368 Database Technology September 2014 – September 2016

• Daniel Lundén, Gizem Çaylak, Fredrik Ronquist, David Broman. Automatic Alignment in Higher-Order Probabilistic Programming Languages. To appear at ESOP 2023. [ ESOP ]
• Daniel Lundén, Joey Öhman, Jan Kudlicka, Viktor Senderov, Fredrik Ronquist, David Broman. Compiling Universal Probabilistic Programming Languages with Efficient Parallel Sequential Monte Carlo Inference. ESOP 2022. [ SpringerLink | DiVA | PDF | arXiv (extended) | PDF (extended) ]
• Daniel Lundén, Johannes Borgström, and David Broman. Correctness of Sequential Monte Carlo Inference for Probabilistic Programming Languages. ESOP 2021. [ SpringerLink | DiVA | PDF | arXiv (extended) | PDF (extended) ]
• Fredrik Ronquist, Jan Kudlicka, Viktor Senderov, Johannes Borgström, Nicolas Lartillot, Daniel Lundén, Lawrence Murray, Thomas B. Schön, and David Broman. Universal probabilistic programming offers a powerful approach to statistical phylogenetics. In Communications Biology 4. 2021. [ Nature | bioRxiv | PDF ]
• Lawrence M. Murray, Daniel Lundén, Jan Kudlicka, David Broman, and Thomas Schön. Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic Programs. In Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics. AISTATS 2018. [ AISTATS | PDF ]

• Daniel Lundén, Lars Hummelgren, Jan Kudlicka, Oscar Eriksson, and David Broman. Suspension Analysis and Selective Continuation-Passing Style for Higher-Order Probabilistic Programming Languages. 2023. [ DiVA | PDF | arXiv ]

• Daniel Lundén, Joey Öhman, David Broman. Compilation of Universal Probabilistic Programs to GPGPUs. PROBPROG 2020. [ PROBPROG | PDF | Poster ]
• Daniel Lundén, David Broman, Fredrik Ronquist, and Lawrence M. Murray. Automatic Discovery of Static Structures in Probabilistic Programs. PROBPROG 2018. [ PROBPROG | PDF | Poster ]
• Daniel Lundén, David Broman, and Lawrence M. Murray. Combining Static and Dynamic Optimizations Using Closed-Form Solutions. PPS 2018. [ PPS | PDF | Poster ]

• Daniel Lundén. Correct and Efficient Monte Carlo Inference for Universal Probabilistic Programming Languages. Doctoral thesis, KTH Royal Institute of Technology. 2023. [ DiVA | PDF ]
• Daniel Lundén. Delayed Sampling in the Probabilistic Programming Language Anglican. Master's thesis, KTH Royal Institute of Technology. 2017. [ DiVA | PDF ]
• Erik Forsblom, Daniel Lundén. Factoring Integers with Parallel SAT Solvers. Bachelor’s thesis, KTH Royal Institute of Technology. 2015. [ DiVA | PDF ]