Genentech: Natasa Tagasovska | Machine Learning Scientist, Prescient Design, Computational Sciences

Scientists / Our Scientists

Years at Genentech

Awards & Honors

I joined Prescient Design’s team at Genentech in January 2022. Before Prescient, I was a Senior Data Scientist at the Swiss Data Science Center at EPFL-ETHZ where I worked on translational projects applying machine learning to domain-specific and social science research efforts. I hold a PhD in Information Systems - Data Analytics track from University of Lausanne and a BS and MSC in Computer Science and Engineering.

Featured Publication

Distinguishing Cause from Effect Using Quantiles: Bivariate Quantile Causal Discovery

ICML 2020

Tagasovska, N., Chavez-Demoulin V., and Vatter T.

Single-model uncertainty estimates for deep learning

NeurIPS 2019

Tagasovska, N. and D. Lopez-Paz

At Prescient, my focus is on incorporating and developing machine learning methods to protein design and optimization while accounting for uncertainty in measurements and estimation.

University of Lausanne, PhD – 2020
Ss. Cyril and Methodius University in Skopje, MS Embedded Systems – 2014
Ss. Cyril and Methodius University in Skopje, BS Computer Science and Engineering – 2013

Doctoral thesis excellence: 'Prix 2020 de la Fondation Helene et Nicolas Porphyrogenis' – 2020
Deans' Honors for Excellent Undergraduate Student – 2013

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Natasa Tagasovska

Machine Learning Scientist, Prescient Design, Computational Sciences

Featured Publication

Distinguishing Cause from Effect Using Quantiles: Bivariate Quantile Causal Discovery

Single-model uncertainty estimates for deep learning