Anastasiia Makarova

Anastasiia Makarova

PhD student

ETH Zurich

I work on sequential decision-making and representation learning for structured data, such as ​point clouds or graphs.

The main question that motivates my research is: How can we actively learn new complex environments? My interests span multiple topics around sequential decision-making, bayesian optimization, representation learning, generative modeling. I am sparked by designing robust algorithms with quantified uncertainty and well-understood limitations (theoretically and empirically), that would be applicable in society-critical areas.

Previously, I was a doctoral student in Learning and Adaptive Systems Group at ETH Zurich supervised by Andreas Krause. My dissertation focuses on Bayesian optimization, proposing methods for risk-averse and computationally effective decision-making. Prior to that, I received master’s degrees in computer science and math from MIPT and Skoltech and a bachelor’s degree in math and physics from MIPT. In my master’s, I visited Columbia University in NYC and worked on deep learning for weakly-supervised semantic segmentation supervised by Victor Lempitsky and Hod Lipson.

I did research internships at Google DeepMind (RL and RLHF for LLMs) Yandex (deep learning for precipitation nowcasting) and Amazon Web Services (Bayesian optimization for AutoML).

  • Dr. Sc., 2023

    ETH Zurich

  • MSc in Computer Science, 2017

    Skolkovo Institute of Science and Technology (Skoltech), Moscow

  • MSc in Applied Mathematics (with honors), 2017

    Moscow Institute of Physics and Technology (Phystech), Moscow

  • BSc in Applied Math and Physics (with honors), 2015

    Moscow Institute of Physics and Technology (Phystech), Moscow


  • October 2023: Safe risk-averse BO for controller tuning (paper) accepted to IEEE Robotics and Automation Letters.
  • September 2023: I successfully defended my doctoral thesis titled “Bayesian Optimization in the wild: risk-averse and computationally-effective decision-making”! Grateful to my advisor Andreas Krause, and to my brilliant collaborators!
  • August 2023: New preprint on arxiv: Adversarial Causal BO.
  • July 2023: New preprint on arxiv: Safe Risk-averse Bayesian Optimization for Controller Tuning.
  • May 2023: Attending ICLR in Rwanda and giving a talk about Model-based Causal BO, slides and talk
  • April 2023: Joining Google DeepMind, Brain team, as a research scientist intern, going to dive into RL from human feedback for LLM
  • January 2023: Model-based Causal BO accepted to ICLR featured spotlight (top 25% of accepted papers)
  • August 2022: Talk at Google TechTalk (Google BayesOpt Speaker Series), slides and video are available.
  • August 2022: Talk at AWS ML Science Tech Presentations series, slides are available.
  • Summer 2022: Our paper got the best paper award at AutoML Conf! I gave a contributed talk, recording, paper, intuitive blog post.


(2023). Adversarial Causal Bayesian Optimization.

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(2023). Safe Risk-averse Bayesian Optimization for Controller Tuning.

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(2022). Model-based Causal Bayesian Optimization. Spotlight at International Conference on Learning Representations (ICLR) 2023.

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(2022). Automatic Termination for Hyperparameter Optimization. Best Paper Award and Contributed Talk at International Conference on Automated Machine Learning (AutoML-Conf) 2022, ICLR Workshop on Neural Architecture Search 2021.

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(2021). Risk-averse Heteroscedastic Bayesian Optimization. Conference on Neural Information Processing Systems (NeurIPS).

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(2021). Cherry-Picking Gradients: Learning Low-Rank Embeddings of Visual Data via Differentiable Cross-Approximation. International Conference on Computer Vision (ICCV).

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(2020). Mixed-Variable Bayesian Optimization. International Joint Conference on Artificial Intelligence (IJCAI).

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(2020). Hierarchical Image Classification Using Entailment Cone Embeddings. CVPR Workshop on on Differential Geometry (DiffCVML), resulted into the publicly available mobile app BioDex.

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Research Scientist Intern
Google DeepMind
Aug 2023 – Apr 2023 Zurich
Research in RL and RLHF for LLMs.
ML Intern
Jun 2020 – Oct 2020 Berlin
Research Intern at AWS AI working on Automatic Model Tuning and SageMaker Autopilot.
PhD Student / Research Assistant
Oct 2017 – Present Zurich

Research areas: Probabilistic Machine Learning, Bayesian Optimization, Deep Learning, Tensors, Computer Vision

Advisor: Prof. Andreas Krause

Research Intern
Jun 2016 – Aug 2016 Moscow
Research Intern at Yandex.Weather working on precipitation nowcasting using deep convolutional and recurrent models
Visiting Researcher
Dec 2016 – Sep 2016 New York

Worked with Hod Lipson and collaboration with Victor Lempitsky.

  • Worked on deep learning based methods for weakly-supervised semantic segmentation
  • Developed an efficient architecture for image-based plant disease detection


Supervised theses

I (co-)supervised MSc theses of several bright students, some resulting into research publications:

  • Alicja Chaszczewicz: Following Gradients to Calibrate Equilibrium Reaching Simulators,
    jointly with Max Paulus, ETH Zurich, December 2020 - May 2021.
  • Ankit Dhall: Learning Representations for Images With Hierarchical Labels (paper @DiffCVML'20),
    jointly with Octavian Ganea and Dario Pavllo, ETH Zurich, March - September 2019.
  • Erik Daxberger: Mixed-Variable Bayesian Optimization (paper IJCAI'20),
    jointly with Matteo Turchetta, ETH Zurich, October 2018 - April 2019.
  • Stefan Beyeler: Multi-fidelity Batch Bayesian Optimization for the Calibration of Transport System Simulations,
    jointly with Matteo Turchetta, ETH Zurich, October 2017 - April 2018.