ABOUT ME

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Pavol Bielik

I am a PhD student at the SRI Lab at ETH Zurich. My main research interests include Programming Languages, Program Synthesis and Machine Learning.

EDUCATION

since 2015

PhD in Computer Science

ETH Zurich

Supervisor: Prof. Martin Vechev

2012 - 2014

MSc in Computer Science

ETH Zurich

Thesis: Scalable Concurrency Analysis for Android Applications

2008 - 2011

BSc in Informatics

Slovak University of Technology in Bratislava

Thesis: Energy Efficient Activity Tracking and Recognition for Mobile Devices

AWARDS

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Romberg Grant2016

at 4th Heidelberg Laureate Forum (1 out of 40)

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Facebook PhD Fellowship Finalist2016

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University Mobile Challenge World Finals2013

Represented Team Switzerland

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Students project of the year 2011 2011

Best Bachelor Thesis Awarded by the Czech and Slovak ACM Chapter

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Excellence Scholarship2009, 2010, 2011

Slovak University of Technology

EMPLOYMENT

2016 - 2017

Software Engineer

DeepCode (ETH Zurich SpinOff)

Development of infrastructure for learning from millions of open source programs, their revisions and natural language descriptions.

2013 - 2014

Research Assistant

Chair of Information Architecture, ETH Zurich

High performance optimization of graph algorithms for interactive visualization.

2013

Software Engineer (Internship)

Adello Schweiz AG

Development of predictive models and optimization algorithms for advertisement targeting and bidding on advertisement exchanges.

RESOURCES

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MSR AI Breakthroughs

2020

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FDL

2020

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ICML

2020

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NeurIPS

2018

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OOPSLA

2018

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Big Code Summit

2018

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LLVM Compiler and Code Generation Socials

2018

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Facebook Fellows Research Workshop

2017

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CAV

2017

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CAV Tutorial

2017

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21st PeWe

2017

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ICML

2016

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OOPSLA

2016 (presented by Veselin)

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OOPSLA

2015

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SOSR

2015

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SNAPL

2015

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StrangeLoop

2014

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OOPSLA

2018

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NeurIPS

2018

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ICLR

2017

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ICML

2016

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Google’s 2nd PhD Student Summit on Compiler & Programming Technology

2014

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RESEARCH

TOOLS

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PUBLICATIONS

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Robustness Certification with Generative Models

PLDI'21

Matthew Mirman, Alexander Hägele, Timon Gehr, Pavol Bielik, Martin Vechev

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Adversarial Attacks on Probabilistic Autoregressive Forecasting Models

ICML'20

Raphaël Dang-Nhu, Gagandeep Singh, Pavol Bielik, Martin Vechev

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Adversarial Robustness for Code

ICML'20

Pavol Bielik, Martin Vechev

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Learning Program Synthesis by Learning to Generate Examples

ICLR'20

Larissa Laich, Pavol Bielik, Martin Vechev

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Learning to Solve SMT Formulas

NeurIPS'18 (oral)

Mislav Balunovic, Pavol Bielik, Martin Vechev

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Robust Relational Layout Synthesis from Examples for Android

OOPSLA'18

Pavol Bielik, Marc Fischer, Martin Vechev

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Learning a Static Analyzer from Data

CAV'17

Pavol Bielik, Veselin Raychev, Martin Vechev

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Program Synthesis for Character Level Language Modeling

ICLR'17

Pavol Bielik, Veselin Raychev, Martin Vechev

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Probabilistic Model for Code with Decision Trees

OOPSLA'16

Veselin Raychev, Pavol Bielik, Martin Vechev

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PHOG: Probabilistic Model for Code

ICML'16

Pavol Bielik, Veselin Raychev, Martin Vechev

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SDNRacer: Concurrency Analysis for Software-Defined Networks

PLDI'16

Ahmed El-Hassany, Jeremie Miserez, Pavol Bielik, Laurent Vanbever, Martin Vechev

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Learning Programs from Noisy Data

POPL'16

Veselin Raychev, Pavol Bielik, Martin Vechev

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Scalable Race Detection for Android Applications

OOPSLA'15

Pavol Bielik, Veselin Raychev, Martin Vechev

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SDNRacer: Detecting Concurrency Violations in Software-Defined Networks

SOSR'15

Jeremie Miserez, Pavol Bielik, Ahmed El-Hassany, Laurent Vanbever, Martin Vechev

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Programming with Big Code: Lessons, Techniques and Applications

SNAPL'15

Pavol Bielik, Veselin Raychev, Martin Vechev

STUDENTS

Korbinian AbstreiterResearch in CS

Improving Robustness for Models of Code via Sparse Graph Neural Networks

Matus ZilinecResearch in CS

Adversarial Attacks for Code

Larissa LaichMSc

Guiding Program Synthesis by Learning to Generate Examples

Philippe SchlattnerMSc

Learning to Infer User Interface Attributes from Images

Mislav BalunovićResearch in CS

Learning to Solve SMT Formulas

Cristina CristescuResearch in CS

Learning a Personalized Linter Tool

Pavle ĐorđevićMSc

Neural Source-to-source Compiler/Decompiler

Ylli MuhadriBSc

Attention for Neural Language Models of Code

Prabhakaran SanthanamMSc

Learning Word Embeddings for Code

Benjamin MularczykMSc

Guided Attention for Neural Language Models

Jeremie MiserezMSc

Detecting Concurrency Violations in Software Defined Networks

Svetoslav KaraivanovResearch in CS

Statistical Bug Localization

Christine ZellerMSc

Statistical Feedback Generation for Programs

Christian FehlmannBSc

Race Reduction for Event-Driven Applications

TEACHING

Introduction to Programming2018, 2016

Software Architecture and Engineering2015