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PhD student in probabilistic deep learning at the Technical University of Denmark.
PhD student at the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU Compute).
Deep learning, probabilistic modelling, Bayesian inference, AI4Science.
I am a second year PhD student in the Section for Cognitive Systems at DTU Compute. My research is focused on probabilistic deep learning and development of uncertainty quantification methods for deep learning, specifically in the context of graph neural networks and molecular data science. I am primarily working with approximate Bayesian inference methods for deep learning with a current focus on ensemble methods. Prior to my enrollment as a PhD student, I obtained a M.Sc. degree in Mathematical Modelling and Computation at the Technical University of Denmark. My M.Sc. degree was mainly focused on machine learning and my thesis was on approximate Bayesian inference for active learning with Bayesian neural networks.
2021 - 2023
M.Sc.Eng., Mathematical Modelling and Computation, Technical University of Denmark.
GPA: 11.3/12.
Thesis: Approximate inference for active learning with Bayesian neural networks (Grade: 12).
2017 - 2020
B.Sc.Eng., Human Life Science Engineering, Technical University of Denmark.
GPA: 11.4/12.
Thesis: Polygenic risk scores and machine learning approaches in childhood acute lymphoblastic leukemia (Grade: 12).
2023 - now
PhD student, Section for Cognitive Systems, DTU Compute, Denmark.
PhD student in probabilistic deep learning. My work is primarily focused on advancing approximate Bayesian inference methods for deep learning and their applications in molecular modelling. I am advised by Assoc. Prof. Mikkel N. Schmidt.
2023
Research assistant, Section for Cognitive Systems, DTU Compute, Denmark.
Research assistant in a project concerned with neural machine translation of childrens' writing to conventional writing. The project was led by Assoc. Prof. Michael Riis Andersen and was in collaboration with WriteReader ApS.
2022
Student assistant, Section for Cognitive Systems, DTU Compute, Denmark.
Student assistant in the natural language processing research project AI for supporting and developing dyslexics' functional writing skills. The project was led by Assoc. Prof. Michael Riis Andersen.
2020 - 2021
Student assistant, iCOPE, Kongens Lyngby & Copenhagen, Denmark.
Student assistant in the interregional Childhood Oncology Precision medicine Exploration (iCOPE) research project. The iCOPE project was a joint effort by Rigshospitalet in Copenhagen, Lund University in Sweden, and DTU to improve diagnostics, treatment, and quality of life for childhood cancer patients.
Mikkel Jordahn, Jonas Vestergaard Jensen, Søren Vejlgaard Holm, Michael Riis Andersen:
Calibrated Transformer Models for Supporting Children’s Writing in Low-Resource Languages.
ArXiv preprint, 2024.
Jonas Vestergaard Jensen, Mikkel Jordahn, Michael Riis Andersen:
Neural machine translation for automated feedback on children’s early-stage writing.
Proceedings of the 5th Northern Lights Deep Learning Conference, PMLR 233:104-112, 2024. Oral presentation.
Sara L. Garcia, Marianne Helenius, Jonas Vestergaard Jensen, Adrian O. Laspior,
Thomas van Overeem Hansen, Ulrik Stoltze, Kjeld Schmiegelow, Ramneek Gupta,
Rikke Linnemann Nielsen, Karin Wadt:
Evaluation of adult cancer polygenic risk scores for stratified disease prevention in childhood cancer.
Manuscript in preparation, DTU Health Tech, 2021.
2024
Teaching assistant, Deep learning, Technical University of Denmark.
2024
Teaching assistant, Bayesian machine learning, Technical University of Denmark.
2019
Teaching assistant, Calculus and algebra 2, Technical University of Denmark.
2018
Teaching assistant, Calculus and algebra 1, Technical University of Denmark.
2024
B.Sc. thesis co-supervisor, Scaling properties of graph neural networks for molecular modelling, Technical University of Denmark.
2023
Course project supervisor, Deep learning, Technical University of Denmark.
July 2024
Attended Cambridge Ellis Unit Summer School on Probabilistic Machine Learning.
June 2024
Attended Nordic Probabilistic AI School.
Jan. 2024
Attended Northern Lights Deep Learning Winter School.
Python, R, MatLab, bash.
PyTorch, Pyro, scikit-learn, Weights & Biases, PySpark, AzureML.
Microsoft Office, Latex, Maple, PyMOL, SAS.
Extensive experience with High-Performance Computing clusters (Computerome and DTU's central HPC cluster).
Danish, English, Swedish.