About
I am a Machine Learning Engineer at DataCrunch, a cloud company where I focus on making AI models more efficient. My work centers on optimizing large language models and foundation models for improved performance and cost-effectiveness.
I also hold a visiting position at the Machine and Human Intelligence group at the University of Helsinki, Finland. Previously, I completed my PhD in Arno Solin's research group at Aalto University, Finland, and collaborated with the Approximate Bayesian Inference Team @ RIKEN AIP.
I'm particularly interested in the intersection of probabilistic modeling and foundation models (GenAI), with a focus on developing efficient AI solutions. I regularly share my findings and insights here.
Publications
Amortized Probabilistic Conditioning for Optimization, Simulation and Inference
28th Int. Conf. on Artificial Intelligence & Statistics (AISTATS 2025)
arXivFunction-space Parameterization of Neural Networks for Sequential Learning
International Conference on Learning Representations (ICLR), 2024
arXivMemory-based dual Gaussian processes for sequential learning
International Conference on Machine Learning (ICML), 2023 (Oral Presentation)
arXivSequential Learning in GPs with Memory and Bayesian Leverage Score
Asian Conference in Machine Learning (ACML) workshop "Continual Lifelong Learning", 2022
OpenReviewFantasizing with Dual GPs in Bayesian Optimization and Active Learning
NeurIPS workshop "Gaussian Processes, Spatiotemporal Modeling, and Decision-making Systems", 2022
arXiv