About
Daolang is a doctoral student co-supervised by Samuel Kaski (Aalto University) and Luigi Acerbi (University of Helsinki), affiliated with the Probabilistic Machine Learning group at Aalto University, the Machine and Human Intelligence group at the University of Helsinki, and ELLIS Institute Finland. He is fully funded by the Finnish Center for Artificial Intelligence (FCAI). He received his master’s degree at Aalto University, with a major in machine learning, data science and artificial intelligence (Macadamia).
His research combines Bayesian modeling, amortized inference, and in-context learning to make scientific inference, experimentation, and decision-making more data- and compute-efficient.
Outside research, he produces electronic music as RGB and enjoys photography.
I will be on the job market in spring/summer 2027, seeking Machine Learning Research Scientist positions. Please contact me if you know of any opportunities!
Research interests
- Sequential Decision-Making & Optimization
- Bayesian experimental design, active learning, and Bayesian optimization.
- Probabilistic Modeling & Inference
- Amortized inference and meta-learning, simulation-based inference.
- Foundation Models for Scientific Discovery
- Tabular foundation models (PFNs), time-series foundation models, neural processes.
News
2026.09: I will serve as an Area Chair for ICLR 2027.
2026.09: 3/3 papers have been accepted by NeurIPS 2026!
2026.06: Started a 6-month research scientist internship at Meta.
2026.05: Our paper “Constrained Bayesian Experimental Design via Online Planning” has been accepted by ICML 2026, and I have been selected as a Gold Reviewer!
2026.02: Started a 3-month research visit to the RainML group at the University of Oxford, supervised by Prof. Tom Rainforth.
2026.01: 3/3 papers have been accepted by ICLR 2026!
2025.11: Had an amazing week attending the Dagstuhl Seminar: Bayesian Optimisation.
2025.10: Gave an invited talk during a short visit to the RainML group at the University of Oxford.
2025.09: Our paper “ALINE: Joint Amortization for Bayesian Inference and Active Data Acquisition” has been accepted by NeurIPS 2025 as Spotlight (Top 3.2%)!
2025.09: Gave an invited talk at the Seminar on Computational Engineering at LUT in Lappeenranta.
2025.06: Gave an invited talk at the Accelerating statistical inference and experimental design with machine learning workshop at the Isaac Newton Institute in Cambridge. You can check the video of the talk here.
2025.05: I was awarded the Encouragement Grant by The Finnish Foundation for Technology Promotion.
2025.01: Our paper “PABBO: Preferential Amortized Black-Box Optimization” has been accepted by ICLR 2025 as Spotlight (Top 5.1%)!
2025.01: Two of our papers have been accepted by AISTATS 2025!
2024.10: I have been selected as one of the top 3 nominees for the AI Researcher of the Year by AI Finland!
2024.10: Participated in the poster session and served as the photographer at Finland AI Day x Nordic AI Meet 2024.
2024.09: Our paper “Amortized Bayesian experimental design for decision-making” has been accepted by NeurIPS 2024!
2023.11: Gave a talk about conditional neural processes at the Mathematical perspective on machine learning seminar at the University of Helsinki. [Slides]
2023.11: Gave a talk about our RCNP paper at Finland AI Day 2023.
2023.09: Attended the ELLIS RobustML Workshop.
2023.09: Two of our papers have been accepted by NeurIPS 2023!
2023.08: Had a fantastic week attending the ELLIS Doctoral Symposium (EDS) 2023. I presented our work “Learning robust statistics for simulation-based inference under model misspecification” and also gave an introduction about the FCAI amortized inference team.
2023.07: Our paper “Augmenting Bayesian Optimization with Preference-based Expert Feedback” has been accepted by ICML 2023 workshop The Many Facets of Preference-based Learning.