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ejaeaesa@andrew.cmu.edu

I am a first year PhD student in Machine Learning at Carnegie Mellon University, where I am advised by Tianqi Chen and affiliated with the Catalyst research group. I am interested in automated and scalable machine learning systems and algorithms.

Before starting my PhD, I lived in Cambridge, UK, where I was an early employee at a University of Cambridge spin-out company writing algorithms and building systems for large-scale semantic similarity search. I am originally from Finland and received my BSc and MSc degrees in Computer Science from the University of Helsinki, where I was advised by Teemu Roos and affiliated with the Information, Complexity and Learning research group.

**E. Jääsaari**, V. Hyvönen, T. Roos. Efficient autotuning of hyperparameters in approximate nearest neighbor search. *Proceedings of the 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining* **(PAKDD 2019)**. [pdf]

T. Silander, J. Leppä-aho, **E. Jääsaari**, T. Roos. Quotient normalized maximum likelihood criterion for learning Bayesian network structures. *Proceedings of the 21st International Conference on Artificial Intelligence and Statistics* **(AISTATS 2018)**. [pdf]

**E. Jääsaari**, J. Leppä-aho, T. Silander, T. Roos. Minimax optimal Bayes mixtures for memoryless sources over large alphabets. *Proceedings of the 29th International Conference on Algorithmic Learning Theory* **(ALT 2018)**. [pdf]

V. Hyvönen, T. Pitkänen, S. Tasoulis, **E. Jääsaari**, R. Tuomainen, L. Wang, J. Corander, T. Roos. Fast nearest neighbor search through sparse random projections and voting. *Proceedings of the 4th IEEE International Conference on Big Data* **(IEEE Big Data 2016)**. [pdf] [code]