I am a final year PhD student @ Jagiellonian University supervised by Prof. Jacek Tabor and co-supervised by Prof. Amos Storkey (University of Edinburgh). Currently at Google AI (Brain) training neural networks which train neural networks. Before that I spent two summers as a visiting student with Prof. Yoshua Bengio. I also completed industrial internships with Palantir and Microsoft.

My email is staszek.jastrzebski (on gmail).

New: papers accepted to ICLR 2019, and AISTATS 2019! Also, our preprint on Neural Architecture Search is online.

My main research goal is to understand how deep network generalize. My research interests include:

  • Optimization in Deep Learning
  • Representation Learning
  • Natural Language Processing
  • Computer Aided Drug Design

Co-supervised MSc/BSc students:

  • Michał Zmysłowski (UW) - Gradient structure and generalization
  • Sławomir Mucha - Towards the ImageNet of virtual screening
  • [Defended, MSc] Tomasz Wesołowski - Relevance of enriching word embeddings in modern deep natural language processing
  • [Defended, MSc] Andrii Krutsylo - Physics aware representation for drug discovery
  • [Defended, BSc] Michał Soboszek - Evaluating word embeddings
  • [Defended, MSc] Jakub Chłędowski - Representation learning for textual entailment
  • [Defended, MSc] Mikołaj Sacha - Meta learning and sharpness of the minima

Selected publications

For a full list please see my Google Scholar profile.

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On the Relation Between the Sharpest Directions of DNN Loss and the SGD Step Length

S. Jastrzębski, Z. Kenton, N. Ballas, A. Fischer, Y. Bengio, A. Storkey

International Conference on Learning Representations 2019
.pdf poster

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Three factors influencing minima in SGD

S. Jastrzębski*, Z. Kenton*, D. Arpit, N. Ballas, A. Fischer, Y. Bengio, A. Storkey

International Conference on Artificial Neural Networks 2018 (oral), International Conference on Learning Representations 2018 (workshop)

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Residual Connections Encourage Iterative Inference

S. Jastrzębski*, D. Arpit*, N. Ballas, V. Verma, T. Che, Y. Bengio

International Conference on Learning Representations 2018
.pdf poster

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A Closer Look at Memorization in Deep Networks

D. Arpit*, S. Jastrzębski*, N. Ballas*, D. Krueger*, T. Maharaj, E. Bengio, A. Fischer, A. Courville, S. Lacoste-Julien, Y. Bengio

International Conference on Machine Learning 2017
.pdf poster slides

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Quo vadis G Protein-Coupled Receptor ligands? A tool for analysis of the emergence of new groups of compounds over time

D. Lesniak, S. Jastrzębski, W. M. Czarnecki, S. Podlewska, A. Bojarski

Bioorganic & Medicinal Chemistry Letters, 2017

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Learning to SMILE(S)

S. Jastrzębski, D. Lesniak, W. M. Czarnecki

International Conference on Learning Representations 2016 (workshop track)
.pdf poster

Short CV

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Google AI

Research intern, automatic machine learning

11.2018-, Zurich, Switzerland

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Research intern supervised by prof. Yoshua Bengio

Summer 2016 & 2017, Montreal, Canda

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Machine Learning intern, fraud detection models and Deep NLP applications

7-9.2016, London, UK

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University of Edinburgh

Research intern, Deep Learning for Go, under the supervision of prof. Amos Storkey

9-11.2015, Edinburgh, UK

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SDE intern, distributed data systems

2015, Palo Alto, USA

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SDE intern, API research and design

2014, Redmond, USA

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SDE intern, data processing framework development

2013, London, UK


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Word Embeddings Benchmarks

Python package for evaluating word embeddings.


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ML algorithms C++ implementations for R, including online clustering, swappable SVM library interface and new clustering algorithm.

github url

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KrakRobot Simulator

Simulator for 2015 and 2016 editions online qualification round.

github url


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Optimization in Deep Learning

Lecture @ GMUM Deep Learning Workshop

2018, Cracow, Poland

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Understanding How Deep Networks Learn

Invited talk @ PL in ML

2017, Warsaw, Poland
slides video