About Conference

Experience GHOST Day yet again in 2022!

Last edition's overwhelmingly positive feedback surprised us all, and we're happy to be bringing increasingly more machine learning experts and fans together!

We are just a few days away from the conference (24th-26th March 2022). We gave our best to prepare this remarkable event, so we are very excited about it being just around the corner, and we hope you do as well. Finally, we will have the chance to meet one another on our platform - it will be a time of inspiring lectures given by our speakers and a chance to exchange the views and experience between our participants.

By the end of 26th March, you will have grasped the up-to-date knowledge from domains ranging from AI in Medicine to Deep Learning NLP, provided to you by the wisest minds from the field.

We highly encourage all registered participants to visit the conference platform, which you can find either in the confirmation e-mail or in the section above. If you didn't manage to register in time - don't worry, as most of the talks will be uploaded on our YouTube channel after the event.

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Conference Date

24-26 March 2022

Our Speakers

Who’s speaking

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Registration

The registration of participants is now closed.
Every applicant should receive an email with a final decision. If for some reason you have not received an email from us, please contact us to learn about the status of your application.

Our Timetable

Agenda

CET time DAY 1: 24/03
15:00 – 15:30 Conference opening
15:30 – 17:00 Session 1: Computer Vision Session 2: Complex Data Session 3: Society & AI
17:00 - 17:30 Coffee break
17:30 - 18:30 Keynote lecture 1: Visual Self-supervised Learning and World Models (Dumitru Erhan, Google Brain - San Francisco)


CET time DAY 2: 25/03
9:30 - 10:30 Keynote lecture 2: Complex systems for AI (Tomas Mikolov, Czech Institute of Informatics, Robotics and Cybernetics - Prague)
10:30 - 11:00 Coffee break interview with Michał Kosiński, Stanford University
11:00 – 12:30 Session 4: Emerging technologies for AI Session 5: NLP Session 6: Recommendation & Search
12:30 - 14:00 Lunch break
14:00 - 15:00 Keynote lecture 3: Self-supervised learning for images, video, and 3D (Ishan Misra, Meta AI Research - New York)
15:00 - 15:30 Coffee break
15:30 – 17:00 Session 7: Speech & Audio Session 8: AI in Medicine Session 9: Data Science


CET time DAY 3: 26/03
9:30 - 10:30 Keynote lecture 4: Skilful precipitation nowcasting using deep generative models of radar (Piotr Mirowski, DeepMind - London)
10:30 - 11:00 Coffee break interview with Jarek Wilkiewicz, Google Brain
11:00 – 12:30 Session 10: ML Ops Session 11: ML Applications Session 12: Student Session
12:30 - 13:00 Closing session

Conference Sessions

Training (vision) models on geodata for the protection of water reservoir ecosystems

by Paulina Knut & Zuzanna Szafranowska-Skorupko | deepsense.ai

15:30-16:00

Virtual Room 1

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How to build a video classification system when you can't rely on visual features

by Karol Żak | Microsoft

16:00-16:30

Virtual Room 1

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Neural radiance fields and their applications

by Marek Kowalski | Microsoft Mixed Reality & AI Lab

16:30-17:00

Virtual Room 1

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Fast Synthetic Graph Generators for Graph Neural Networks

by Piotr Bigaj | NVIDIA Poland

15:30-16:00

Virtual Room 2

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How AI boosted Audio Processing?

by Adam Kupryjanow | Intel

16:00-16:30

Virtual Room 2

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Causal discovery in Python

by Aleksander Molak | Ironscales & Tensorcell

16:30-17:00

Virtual Room 2

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Teaching Machine Learning to Children

by David Touretzky | Carnegie Mellon University

15:30-16:00

Virtual Room 3

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The Consciousness of AI

by Smriti Mishra | Earthbanc

16:00-16:30

Virtual Room 3

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Semantic information extraction

by Mateusz Półtorak | Pearson

16:30-17:00

Virtual Room 3

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An introduction to quantum machine learning

by Paweł Gora | Quantum AI Foundation

11:00-11:30

Virtual Room 1

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Accelerating AI processing on the edge

by Karol Gugala | Antmicro

11:30-12:00

Virtual Room 1

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Probabilistic programming, why we need it in business settings

by Luciano Paz | PyMC-Labs

12:00-12:30

Virtual Room 1

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How can we learn the structure of customer service data @Allegro?

by Aleksandra Chrabrowa | Allegro

11:00-11:30

Virtual Room 2

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Tweet-Topic Classification: The Real-Life Perspective

by Mateusz Fedoryszak | Twitter

11:30-12:00

Virtual Room 2

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Doctor-in-the-loop: Interactive Machine Learning in Healthcare AI

by Rachel Wities | Zebra Medical Vision

12:00-12:30

Virtual Room 2

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Learning Representations for Hotel Ranking

by Ioannis Partalas | Expedia Group

11:00-11:30

Virtual Room 3

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Algorithmic Balancing Models for Multi-stakeholder Recommendations

by Rishabh Mehrotra | ShareChat

11:30-12:00

Virtual Room 3

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Challenges in developing Visual-Search system at Allegro

by Bartosz Ludwiczuk & Bartosz Paszko | Allegro

12:00-12:30

Virtual Room 3

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A brief history of Neural TTS

by Andrew Breen | Amazon TTS Research

15:30-16:00

Virtual Room 1

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Harmonic Analysis: A Complex Classification Problem

by Gianluca Micchi | IRIS Audio Technologies

16:00-16:30

Virtual Room 1

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Optimizing training datasets for expressive text-to-speech synthesis

by Monika Podsiadło | Google NYC

16:30-17:00

Virtual Room 1

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Towards in-silico drug design

by Marta Stępniewska-Dziubińska | NVIDIA

15:30-16:00

Virtual Room 2

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Linguistic markers predict onset of Alzheimer

by Elif Eyigoz | IBM Watson

16:00-16:30

Virtual Room 2

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How artificial intelligence and deep learning speed up synthesis planning in drug discovery

by Stanisław Jastrzębski | Molecule one

16:30-17:00

Virtual Room 2

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Data Privacy - building ML solutions for your customers without looking at the data

by Tomasz Marciniak | Microsoft

15:30-16:00

Virtual Room 3

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Deep Neural Deduplication

by Marcin Mosiolek | SII Poland

16:00-16:30

Virtual Room 3

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Embracing the Range of Data Science

by Jev Gamper | Vinted

16:30-17:00

Virtual Room 3

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Methods for efficient management and deployment of complex deep learning systems: a SportsTech use case

by Wojciech Rosinski | ReSpo.Vision

11:00-11:30

Virtual Room 1

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How to detect silent model failure

by Wojtek Kuberski | NannyML

11:30-12:00

Virtual Room 1

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Effective ML system development

by Leonard Aukea | Volvo Cars

12:00-12:30

Virtual Room 1

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Bayesian Optimization with Categorical and Continuous Variables

by Vu Nguyen | Amazon

11:00-11:30

Virtual Room 2

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Deep Learning for Automated Audio Captioning

by Wenwu Wang | University of Surrey

11:30-12:00

Virtual Room 2

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AI for creative applications & art

by Ivona Tautkute | Tooploox

12:00-12:30

Virtual Room 2

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FairPAN - Achieving fairness through neural networks

by Hubert Ruczyński | Warsaw Univeristy of Technology

11:00-11:30

Virtual Room 3

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Ligands classification using sparse convolutional neural networks

by Jacek Karolczak | Poznan University of Technology

11:30-12:00

Virtual Room 3

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Is music a natural language?

by Sebastian Chwilczyński | Poznan University of Technology

12:00-12:30

Virtual Room 3

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