About

I am a PhD student in Machine Learning at Carnegie Mellon University, where I am advised by Tianqi Chen and Ameet Talwalkar. My research interests include 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. 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.

Publications

E. Jääsaari, M. Ma, A. Talwalkar, T. Chen. SONAR: Joint Architecture and System Optimization Search. arXiv preprint. [pdf]

V. Hyvönen, E. Jääsaari, T. Roos. A Multilabel Classification Framework for Approximate Nearest Neighbor Search. Proceedings of the 35th Conference on Neural Information Processing Systems (NeurIPS 2022). [pdf]

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]