Raquel Aoki

Ph.D., SFU
raoki [at] sfu.ca

About

Hello! I'm currently working as a Machine Learning Researcher at Borealis AI.

My research focus is on the intersection between Causal Inference and Machine Learning. My most recent projects [1, 2, 3] combine Machine Learning and Causal Inference by incorporating neural networks (NN) in the treatment effect estimation. These include transfer learning to improve estimates in small high-dimensional datasets [1], new NN architectures to estimate multiple treatment effects [2, 4], and the creation of a new high-dimensional benchmark dataset [3].

During my PhD, I worked with Prof. Martin Ester in the School of Computing Science at Simon Fraser University (SFU), Canada. I also worked as Machine Learning Research Intern at Borealis AI (Fall 2020) and Research Intern at Google Brain (Summer 2021), with research projects on multi-task learning [6] and machine learning for Causal Inference [3], respectively.

Before joining SFU, I received my M.S. in Computer Science in 2017 under the supervision of Prof. Renato Assuncao and my B.S. in Statistics in 2014, both at Universidade Federal de Minas Gerais, Brazil. The focus of my M.S. Thesis was sports analytics [8, 9].

Some of my other interests are:

Supporting women in tech fields;
Hiking (Check on some of the beautiful landscapes I have been in Photography).

Publications

[1] [Paper] Aoki, R., Ester, M.. "Causal Inference from Small High-dimensional Datasets", under review, 2022. (link)

[2] [Paper] Aoki, R., Chen, Y., Ester, M.. "Multi-treatment Effect Estimation from Biomedical Data", Pacific Symposium on Biocomputing, 2023, World Scientific Publishing Co., Singapore. (link)

[3] [Poster] Aoki, R., D’Amour, A., McLean, C., Yadlowsky, S.. "ICETEA Benchmark: Semi-synthetic Treatment Effect Methods Evaluation with Images as Covariates." American Causal Inference Conference (ACIC), 2022. (code)

[4] [Paper] Aoki, R., Tung, F., and Oliveira, G.. "Heterogeneous Multi-task Learning with Expert Diversity." IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2022. (paper, slides, code) Note: This work was previously presented at the 20th International Workshop on Data Mining in Bioinformatics (BIOKDD), 2021.

[5] [Paper] Aoki, R., and Ester, M.. "ParKCa: Causal Inference with Partially Known Causes." Pac Symp Biocomput. 2021. (link, Supplemental Material)

[6] [Poster] Aoki, R., and Ester, M.. "Bayesian Predictive Model combined with Matrix Factorization for Causal Inference Analysis." 14th Machine Learning in Computational Biology (MLCB) meeting, co-located with NeurIPS. 2019. (link)

[7] [Paper] Aoki, R., Assuncao, R., Melo, P.. "Luck is hard to beat: The difficulty of sports prediction." Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2017. (link, video)

[8] [Poster] Aoki, R., Assuncao, R., Melo, P.. "Measuring the Size of the Surprise Box in Soccer Leagues."(Original Title: “Medindo o tamanho da caixinha de surpresas em ligas de futebol.”), 31rd BrazilianSymposium on Databases (SBBD), 2016. (link)

[9] [Poster] Aoki, R., Soier, A., Fiqueiredo, D.. "Analysis of Political Knowledge of University Students Using Item Response Theory." (Original Title: “Análise do conhecimento político dos universitários utilizano Teoria de Resposta ao Item”, 21rd National Symposium on Probability and Statistics (SINAPE), 2014.

Mentorship

Interested on doing a PhD on Canada? Fell free to talk to me or check out my slides (in Portuguese) about doing a PhD at my university! Interessado em fazer um doutorado no Canadá? Sinta-se a vontade para falar comigo ou conferir meus slides (em português) sobre como fazer um doutorado na minha universidade!

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