About

I research machine learning methods in database systems and self-supervision, with an emphasis on reproducibility, methodological rigor, and applications in medicine.

I have received my MA and am continuing on with the PhD at the Computer Lab at the University of Cambridge, advised by Prof Ferenc Huszár. In the past, I spent some time at the University of Oxford for the MSc and Google for three internships.

For academic years 23/24 and 24/25 I am visiting Prof Kilian Q. Weinberger’s lab at Cornell.

Publications and preprints

PhantomWiki: On-Demand Datasets for Reasoning and Retrieval Evaluation. Albert Gong*, Kamilė Stankevičiūtė*, Chao Wan*, Anmol Kabra, Raphael Thesmar, Johann Lee, Julius Klenke, Carla P. Gomes, Kilian Q. Weinberger (2025). [preprint] [code]

Bridging the worlds of pharmacometrics and machine learning. Kamilė Stankevičiūtė*, Jean-Baptiste Woillard*, Richard W. Peck, Pierre Marquet, Mihaela van der Schaar. Clinical Pharmacokinetics (2023). [pdf]

Conformal time-series forecasting. Kamilė Stankevičiūtė, Ahmed M. Alaa, Mihaela van der Schaar. NeurIPS (2021). *Lithuania’s best early-career AI publication of 2021.* *Best short presentation at the 10th Meeting of Early Career Mathematicians of Lithuania.* [pdf] [code] [video]

Population graph GNNs for brain age prediction. Kamilė Stankevičiūtė, Tiago Azevedo, Alexander Campbell, Richard A. I. Bethlehem, Pietro Liò. ICML Workshop on Graph Representation Learning and Beyond (2020). *M2L outstanding poster award.* [pdf] [code] [video]

Software

MEDS Decentralized, Extensible Validation (MEDS-DEV) Benchmark. MEDS-DEV Working Group. Featured in ML4H 2024 Demo Track. *Invited talk at the 13th Meeting of Early Career Mathematicians of Lithuania.* [spec sheet] [docs] [code]