Publications per year
Publications per year
Personal profile
Other information
Visiting scholar at Columbia University, 2007
Visiting postdoc at Cambridge University 2008-2009
Research areas
Statistical models are used in all areas of science to describe stochastic relations between variables. In statistical modeling, probability theory is used to describe the uncertaincy that is present due to inaccurate measurements, model mismatch, missing data, etc. The process of drawing conclusions based on statistical models is called statistical inference. The aim of my research is to develop novel statistical methodology, which includes:
- Formulating probabilistic models and devising procedures for computational inference, evaluation, and validation.
- Applying the developed methodology to solve problems in various application areas in science and industry.
Expertise related to UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
Fingerprint
Dive into the research topics where Mikkel N. Schmidt is active. These topic labels come from the works of this person. Together they form a unique fingerprint.
- 1 Similar Profiles
Network
Recent external collaboration on country/territory level. Dive into details by clicking on the dots or
Publications
-
End-to-End Learning for VCSEL-based Optical Interconnects: State-of-the-Art, Challenges, and Opportunities
Srinivasan, M., Song, J., Grabowski, A., Szczerba, K., Iversen, H. K., Schmidt, M. N., Zibar, D., Schröder, J., Larsson, A., Häger, C. & Wymeersch, H., 2023, (Accepted/In press) In: Journal of Lightwave Technology. 16 p.Research output: Contribution to journal › Journal article › Research › peer-review
Open AccessFile1 Downloads (Pure) -
Bayesian dropout
Herlau, T., Schmidt, M. N. & Mørup, M., 2022, In: Procedia Computer Science. 201, C, p. 771-776Research output: Contribution to journal › Conference article › Research › peer-review
Open AccessFile24 Downloads (Pure) -
Calibrated uncertainty for molecular property prediction using ensembles of message passing neural networks
Busk, J., Jørgensen, P. B., Bhowmik, A., Schmidt, M. N., Winther, O. & Vegge, T., 2022, In: Machine Learning: Science and Technology. 3, 1, 13 p., 015012.Research output: Contribution to journal › Journal article › Research › peer-review
Open AccessFile228 Downloads (Pure) -
Programmatic policy extraction by iterative local search
Larsen, R. & Schmidt, M. N., 2022, Inductive Logic Programming. Springer, p. 156–166 (Lecture Notes in Computer Science, Vol. 13191).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Open AccessFile11 Downloads (Pure) -
Raman spectrum matching with contrastive representation learning
Li, B., Schmidt, M. N. & Alstrom, T. S., 2022, In: Analyst. 147, 10, 9 p., 2238.Research output: Contribution to journal › Journal article › Research › peer-review
Open AccessFile1 Downloads (Pure)
-
Denoising Diffusion Models
Nielsen, B. M. G., Winther, O. & Schmidt, M. N.
01/11/2022 → 31/10/2025
Project: PhD
-
Machine learning for electronic scale inverse design of enzymatic catalysts
Cornet, F. R. J., Schmidt, M. N., Bhowmik, A. & Winther, O.
01/10/2021 → 30/09/2024
Project: PhD
-
Federated Machine Learning for Spectroscopy
Li, B., Alstrøm, T. S., Hansen, L. K. & Schmidt, M. N.
01/02/2021 → 31/01/2024
Project: PhD
-
Towards real time Raman molecular imaging of living organisms
Hansen, D. F., Heinonen, M., Rinnan, Å., Schmidt, M. N. & Alstrøm, T. S.
01/03/2020 → 29/05/2023
Project: PhD
-
Flexible Densities for Deep Generative Models
Nielsen, D., Winther, O., Schmidt, M. N., Hauberg, S., Lobato, J. M. H. & van den Berg, R.
01/01/2019 → 08/04/2022
Project: PhD