Since April 2025, I am a FCAI Research Fellow at Aalto University, a 5-year position funded by the Finnish Center for Artificial Intelligence. I work with groups
- Computational Systems Biology (Prof. Harri Lähdesmäki)
- Probabilistic Machine Learning (Prof. Samuel Kaski)
I am also a member of the ELLIS AI network, affiliated with ELLIS Institute Finland.
My interests span:
- Biology-Informed Machine Learning: blending flexible data-driven approaches and mechanistic models within Systems Biology
$\rightarrow$ For more information, you can have a look to this position piece: Position: Biology is the Challenge Physics-Informed ML needs to Evolve
- Bayesian Optimization, in particular recently, in-context learning flavors, see e.g., our ICLR’26 paper or our recent preprint.
I obtained my PhD in February 2022 from Ecole polytechnique, Inria and Institut Curie, under the supervision of François Fages and Annabelle Ballesta. The manuscript can be
found here and the slides here. A detailed CV can be found here (Last updated: May 2026).
Besides, for a few years now, I have been cultivating a passion for indoor bouldering, with a mild success thus far ðŸ«
News
A paper accepted at ProbML 2026!
- Marshal Sinaga, J.M., and S. Kaski. Anchor-Based Heteroscedastic Noise for Preferential Bayesian Optimization. Stay tuned for camera-ready version!
New work on in-context optimization:
- N. Blumer, J. M., and S. Kaski. In-Context Black-Box Optimization with Unreliable Feedback . arXiv, 2026.
New work on Bayesian variable selection for Pharmacogenetics studies:
- J. M., I. Rebai, D. W. Haas, and J. Bertrand. Joint Bayesian Inference of Genetic Effect Sizes and PK Parameters in Nonlinear Mixed-Effects Models. arXiv, 2026.
New work on Time-Aware Latent Space Bayesian Optimization:
- T. A. Vu, J. M., and H. Lähdesmäki. Time-Aware Latent Space Bayesian Optimization. arXiv, 2026.