SEPTEMBER 28, 2019 / WARSAW / GTM +2

STATE OF THE ART
Machine Learning Conference

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The place where meet engineering, business and art. Applied machine learning & real use cases.

This year's leitmotif is "STATE OF THE ART", so be ready for the most advanced designs and ML implementations with a strong focus on practice and real-world use cases.

Meet experts who implement cutting-edge machine learning solutions in areas such as
medicine and healthcare, finance, industrial processes, transport and logistics, conversations and communication, design and art.

Join if you are...
ML engineer
Data Scientist
Head of Innovation
CxO who (want to) runs a business based on ML
Developer who wants to start working with ML
Geek-artist who uses ML
Start-up founder
Investor
You're hungry for knowledge of applied ML
You touch ML topic in another way
SEPTEMBER 28, 2019 / GTM +2
IT'S MORE THAN JUST A TYPICAL ONLINE CONFERENCE
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By sending the message I give my consent to process my personal data within the DataWorkshop project. The consent above is given to Biznes Myśli – Uladzimir Aliakseichanka, Grodzka 42/1 street, 31-044 Kraków, Tax Identification Number (NIP): 6751364881. You can withdraw your consent at any time.
What you'll get...
Live streaming
You can watch the live broadcast, or watch recorded lectures at a convenient time.

By joining the conference live, you have the chance to talk with participants and ask questions to experts via Slack. You will also receive materials after the conference: summary with practical tips and links to tools, libraries, etc. written by experts.

Check our Agenda and Subscribe to the list :)
Practical ML knowledge
& use cases
The presentations will contain:
- a use case,
- a classic way of solving the presented problem,
- the latest production solutions (state of the art in ML),
- success metrics,
- useful technical and business advice.

Each presentation is a 30-minutes intense dose of practical knowledge of Machine Learning and applied AI.
Expert experience & motivating stories
Among our speakers, you can meet experts from small and large companies/organizations, who are actively working on new models and show the most advanced designs and ML implementations that solve real problems.

Don't miss the opportunity and join AMA sessions with Speakers Online!

AMA is a less formal form of Q&A and has one rule: ask me anything! So take a cup of coffee, go to the AMA zone just in time on dedicated Slack Channel, sit comfortably and ask anything you want to know.
Effective game networking
If you want to go somewhere, it is best to find someone who has already been there (R.Kiyosaki).

Unlimited networking including access to dedicated Slack before and after the conference, game-networking session.

We designed #Slack Game Networking to optimize the search for such people. During the hour you will meet people who have similar interests and different experience. You will not have to talk about the weather, don't worry.
The moderator will keep his finger on the pulse and will take care of the topic of conversation and a good atmosphere.
Video pack after the conference

Don't worry if you still couldn't be online with us.

Few weeks after the conference you'll get a pack with all recorded talks.
Post-conference publication
After the conference, you will also get a summary of all lectures with extra practical tips and links to tools, libraries, etc. written by experts.
DWCC2019 in numbers
1
day
2
tracks
30+
experts
1000+
online attendees
Meet our speakers
Tomas Mikolov
Research Scientist / Facebook
He is a research scientist at Facebook AI Research since May 2014. Previously he has been a member of Google Brain team, where he developed and implemented efficient algorithms for computing distributed representations of words (word2vec project). He has obtained PhD from Brno University of Technology (Czech Republic) for his work on recurrent neural network based language models (RNNLM). His long term research goal is to develop intelligent machines capable of learning and communication with people using natural language.

Interests:
Machine learning, and natural language processing.

LinkedIn
Emeli Dral
Chief Data Scientist, Mechanica AI
Emeli Dral leads the data science team at Mechanica AI. She is responsible for the development of core products and technologies for the application of artificial intelligence in industrial processes.
Prior to co-founding Mechanica AI, she served as the Chief Data Scientist at Yandex Data Factory. She led a team of accomplished data scientists and oversaw the development of machine learning solutions for various industries - from banking to manufacturing. Emeli is a lecturer at the Yandex School of Data Analysis and Harbour.Space University, where she teaches courses on machine learning and data analysis tools. In addition, she is a co-author of the "Machine Learning & Data Analysis" and "Big Data for Data Engineers" curriculums at Coursera.

LinkedIn
Janusz Marecki
Senior Research Scientist / DeepMind
He is currently a senior research scientist at Google DeepMind where he conducts research advancing the state-of-the-art in deep learning, reinforcement learning and multi-agent systems towards solving intelligence.

His specialties include Deep Machine Learning, Markov Decision Processes, Reinforcement Learning, and High-Performance Computing.


LinkedIn
Mikhail Burtsev
Founder and Leader at DeepPavlov.ai
Mikhail Burtsev is head of Neural Networks and Deep Learning Laboratory at Moscow Institute of Physics and Technology. In 2005, he received the Ph.D. degree from Keldysh Institute of Applied Mathematics of Russian Academy of Sciences. From 2011 to 2016 he was head of the lab of Department of Neuroscience at Kurchatov NBIC Centre. Now he is one of the organizers of NIPS 2017 Conversational Intelligence Challenge, and a head of iPavlov Project.

His research interests are in fields of Natural Language Processing, Machine Learning, Artificial Intelligence, and Complex Systems.

LinkedIn
Valentin Malykh
Applied Research Scientist at Huawei
Valentin has hand-on experience working with Information Retrieval, Natural Language Processing and even Self-Driving tasks. He worked as an engineer in Sputnik & Yandex, as an applied research scientist in iPavlov & VK. Now he is employed as a research scientist at Huawei Noah's Ark Lab.

Recently, Valentin defended a PhD on robustness in NLP tasks. His research interests are robustness to noise, dialog systems, summarisation. Valentin co-organized NeurIPS ConvAI challenges for Conversational AI in 2017 & 2018.

LinkedIn
Marta Markiewicz
Head of Data Science at Objectivity
Born in 1988, a graduate in Mathematical Statistics at the Wrocław University of Technology. Her master's thesis entitled "Stable feature selection methods" won the second place in the XLVI competition of the Polish Mathematical Society for the Best Student Work in Probability Theory and Statistics Applications. Professionally, over almost 8 years, she has been discovering the potential of data in various business domains - from medical data, through retail, HR, finance, aviation, real estate, ... She deeply believes in the power of data in every area of life.

LinkedIn
Łukasz Dziekan
CTO at FinAI
For the 10 years he is dealing with advanced analytics and production deployed machine learning systems. He started in Deloitte where he did complex risk analysis systems in credit risk and stress tests. Then he did 2 years in New York Unicorn Zocdoc where he was analytics tech lead and infrastructure senior developer. However he missed home and came back and built 50 person machine learning advisory team in PwC CEE, It was first such case in Poland. At the end of 2016 he found his entrepreneurship vibe and founded FinAi.com and became CTO. In FinAi he deals with simplifying retail finance – using Machine learning methods – in a way where they are designed to help rather than obstruct. They do things like fraud detection and face recognition and credit risk. And it's interesting, but very hard to do right.

LinkedIn
Bartek Skorulski
Senior Data Scientist at Research Team, Alpha
Bartek Skorulski is Senior Data Scientist in Research Team in Alpha (Telefonica), a team that designs and builds machine learning solutions for improving people mental health. He has extensive experience in both industry and academic word. On one hand he was working in Messaging Team in Schibsted (company knows for portals like Fotocasa, Leboncoin, Infojobs, Segundamano) helping building experimentation driven products like, for example, smart replies in chats. In SCRM (digital hub of Lidl) he was setting up data driven environment, building recommender systems, forecasting demand and AB Test tool. In King (Activision/Blizzard) using data he was helping build best games. On the other hand, he was also working as Associate Professor doing research in Dynamical Systems and teaching graduate and undergraduate courses. Last few years he has been also teaching courses on machine learning, deep learning and data management.

LinkedIn
Łukasz Kidziński
CTO at Saliency.ai , Researcher at Stanford
Łukasz Kidziński is a researcher in the Mobilize Center at Stanford, working on the intersection of computer science, statistics, and biomechanics. He is a co-founder of Saliency.ai -- a startup focusing on improving efficacy of clinical trials using artificial intelligence. Previously, Łukasz was a researcher in the CHILI group, Computer-Human Interaction in Learning and Instruction, at the EPFL. He obtained his Ph.D. at Université Libre de Bruxelles in mathematical statistics, working on time series analysis and functional data. He obtained two master degrees in mathematics and computer science, from the University of Warsaw. His research interests include time series analysis, large scale statistics, alternative data, and applications of data science in biological and medical sciences.

LinkedIn
Dariusz Gross
DATAsculptor
DATAsculptor, an artist interested in machine learning and 3D generative networks in VR/AR. He started the myFATHERintheCloud.ai project as a way of studying the idea of giving the artists digital artistic immortality. The founder of #next_top_architects an Architecture Community provides a unique environment for architects around the globe to meet, share and compete. He started D.E.GROSS in 1994 a residential architecture practice designing one-off luxury homes and commercial developments. Innovative residential architecture that transforms clients' living environments.

Since his project "risky player " CASINO GRAND HOTEL Sopot in 1996, he is developing #DATAsculpting

LinkedIn
Anna Anzulewicz
Head of Research at Harimata
Anna is a cognitive scientist with a background in psychology and data science, and a great interest in using new technologies in diagnosis, therapy, and education. She loves working in multidisciplinary projects and developing technology for social good. She combines her academic work at several universities with business.

For six years Anna worked as head of research at Harimata, a startup company that combines psychology and technology to support assessment and treatment of disorders in children and adults. During her time in Harimata, she developed AI-based solutions supporting early detection of autism spectrum disorder in children as well as applications helping adults who suffer from mood disorders.

LinkedIn
Aditya Guglani
Data Scientist at Uber
He is currently a data scientist at Uber where he is using deep learning and natural language processing to provide magical support experiences to customers and driver partners. He has a background in engineering and a graduate degree in data science, which enables him to solve some hard problems using data and machine learning. His interests currently include neural networks, document semantics, and language understanding.

LinkedIn
SATURDAY | SEPTEMBER 27 | GTM +2

Conference Program
09:00 – 09:10
09:00 – 09:10
Conference Opening
09:10 - 09:40
09:10 - 09:40
We'll announce soon
09:40 - 10:10
09:40 - 10:10
We'll announce soon
10:10 - 10:40
10:10 - 10:40
Towards the Future of AI
Janusz Marecki / Senior Research Scientist / DeepMind
Recent years have seen a rising interest in developing AI algorithms for real world big data domains ranging from autonomous cars to personalized assistants. At the core of these algorithms are architectures that combine deep neural networks, for approximating the underlying multidimensional state-spaces, with reinforcement learning, for controlling agents that learn to operate in said state-spaces towards achieving a given objective.

The talk will first outline notable past and future efforts in deep reinforcement learning as well as identify fundamental problems that this technology has been struggling to overcome. Towards mitigating these problems (and open up an alternative path to general artificial intelligence), I will then summarize a brain computing model of intelligence, rooted in the latest findings in neuroscience. The talk will conclude with an overview of the recent research efforts in the field of multi-agent systems, to provide the future teams of humans and agents with the necessary tools that allow them to safely co-exist.
10:40 - 11:00
10:40 - 11:00
Coffee break
11:00 - 11:30
11:00 - 11:30
Efficient algorithms for processing natural language
Tomas Mikolov / Research Scientist / Facebook
We'll announce soon
11:30 - 12:00
11:30 - 12:00
Will announce soon
Emeli Dral / Chief Data Scientist / Mechanica AI
We'll announce soon
12:00 - 12:30
12:00 - 12:30
Running it on prod - how to do deployment for machine learning and not be scared
Łukasz Dziekan / Chief Technology Officer / FinAI
They say a data scientist will either bloom or wilt after he/she deploys her first model to production where real decisions will be made, without human intervention. This presentation will be all about dirty, not so glamorous, getting the job done, but still state of the art part of machine learning engineer job - managing the whole process ending on production. Combining our 2-year experience of running extremely important biometrics model on production for the banking sector I will explain that moving beyond Jupyter, getting this 1% more on AUC is really way more important - because this differentiates between making a real impact and affecting people's lives - and staying in the research part.FinAi has built it's platform over 2 years - and while it is by no means finished, will tell you some stories from the trenches, hoping you will learn, maybe be scared a little (and it's healthy) and move you to the next step - having a model working online.
12:30 - 13:40
12:30 - 13:40
Game Networking
The efficient way to meet people with whom you can do a something job, project or even business.
13:40 - 14:40
13:40 - 14:40
Lunch
14:40 - 15:10
Track 1
14:40 - 15:10
Track 1
Data augmentation in speech recognition
Tomasz Jadczyk / CTO and Co-founder / Techmo
On the one hand, deep learning methods require a lot of data to build accurate models, so this is especially crucial to cover the variety of speech types, styles and contexts, on the other hand collecting audio-sets and authentic utterance corpora is expensive and time-consuming. The solution comes with the artificial data augmentation, which makes it possible to build reliable models based on the limited data resources. My aim in this presentation is to present several available data augmentation methods that can be used both in acoustic and language modeling. Since in this industry every solution brings with it brand new problems, I will also discuss limitations of the presented methods, their effect on the speech recognition performance and compatibility issues in the production environment.
14:40 - 15:10
Track 2
14:40 - 15:10
Track 2
Artificial Intelligence in Mental Health
Bartek Skorulski / Senior Data Scientist at Research Team / Alpha
One out of four adults experience mental health problems at a certain point in life, including anxiety, depression, sleep disturbance, phobias, etc. Limited access to healthcare services results in that most cases remains undiagnosed and untreated. At Telefonica Alpha (Health Moonshot) we try to solve this problem by building mobile apps that provide personalised coaching, therapy, monitoring, and crisis prediction. These apps collect passive data (from mobile sensors) and active data (through questionnaires). Then, on one hand, have built could-based infrastructure with tools like airflow, spark, pytorch which allows us to deploy and test our ML algorithms like recommender systems and behavioural models. On the other hand, we collaborate with neuroscientists or psychologists from institutions like MGH, UCL, who help us in establishing a hypothesis, interpreting the developed models and testing our products with clinical trials.
15:10 - 15:40
Track 1
15:10 - 15:40
Track 1
How to Do a Topic Modeling with Neural Networks
Valentin Malykh / Applied Research Scientist / Huawei
Having a big pile of texts sometimes you want to split them into the groups of some kind. One of the classic ways to do this is to do a topic modelling. It is a powerful tool even now, but there are some issues: the modern topic modeling techniques cannot handle big volume of data. And also they do not prone to typos. I show you a different way using neural network architecture which we use to cluster the reviews from our users. It is based on word embeddings and modern attention mechanism which helps it to overcome mentioned flaws.
15:10 - 15:40
Track 2
15:10 - 15:40
Track 2
Deep learning for clinical trials in movement-related diseases
Łukasz Kidziński / CTO at Saliency.ai , Researcher at Stanford
Every second person in the world will be affected by symptomatic movement disorders at some point in their life. Proper treatment of these disorders requires very precise measurements of body mechanics. Collecting data requires sophisticated motion capture hardware and a few hours of work of highly trained medical personnel, bringing the cost to more than $2k per visit. We propose a deep learning approach where expensive hardware and trained personnel is replaced by mobile phone and a neural network. We are now recruiting patients to track their movement at their homes. Measurements derived using our method correlate at r = 0.7 - 0.8 with most of the tested metrics, enabling their clinical use. In this talk, I will present all the steps I had to take to execute this medical data science project, from data sharing agreement with a hospital until deployment. I'll discuss the impact of our solution on drug development.
15:40 - 16:10
Track 1
15:40 - 16:10
Track 1
Developing conversational AI systems with DeepPavlov open-source framework
Mikhail Burtsev / Founder and Leader / DeepPavlov.ai
An open-source DeepPavlov library is specifically tailored for development of dialogue systems. The library prioritizes efficiency, modularity, and extensibility with the goal to make it easier to create dialogue systems from scratch with limited data available. It supports modular as well as end-to-end approaches to implementation of conversational agents. In DeepPavlov framework an agent consists of skills and every skill can be decomposed into components. Components are usually trainable models which solve typical NLP tasks such as intent classification, named entity recognition, sentiment analysis or pre-trained encoders for word or sentence level embeddings. Sequence-to-sequence chit-chat, question answering or task-oriented skills can be assembled from components provided in the library. ML models implemented in DeepPavlov have a performance on par with the current state of the art in the field.
15:40 - 16:10
Track 2
15:40 - 16:10
Track 2
Play.Care: bridging psychology and technology for better autism assessment
Anna Anzulewicz / Head of Research / Harimata
Play.Care, developed by Harimata, analyses movement patterns that children make while using a tablet device and assesses the risk of autism spectrum disorders. The solution uses state of the art CNN architectures to analyse users' behaviour during the gameplay. Additionally, features that characterise movement kinematic characteristics are classified with boosted trees and RNN-s. This new, automated approach to detection of developmental disorders can make a difference in the way in which we learn about children's health and make the initial assessment faster and widely available. The results of three major studies (including 520 participants) conducted so far, has shown that the solution has an overall sensitivity 80% and specificity of 89%. Currently, the solution is undergoing clinical study, which will further assess its effectiveness.
16:10 - 16:40
Track 1
16:10 - 16:40
Track 1
We'll announce soon
Marta Markiewicz / Head of Data Science / Objectivity
16:40 - 17:00
16:40 - 17:00
Coffee break
17:00 - 17:30
17:00 - 17:30
COTA: Improving the customer support experience using Deep Learning
Aditya Guglani / Data Scientist / Uber
As the leading ridesharing platform, Uber receives thousands of support tickets daily. The tickets are in different global languages and range from missing item from an EATS order in India to fare adjustment request in North America. To scale up the ticket resolution process, we built COTA (Customer Obsession Ticket Assistant) an intelligent system based on machine learning (ML) and natural language processing (NLP) techniques that are integrated with Uber's customer support platform. It provides customer support representatives, suggestions for the ticket type, appropriate reply and relevant actions to take based on ticket text and additional context such as trip information. As a result, we were able to reduce the average number of re-routings by 15% and the time an agent spends on a ticket by 5% on average, thus making the support experience a little more magical.
17:30 - 17:50
17:30 - 17:50
We'll announce soon
17:50 - 18:00
17:50 - 18:00
Closing Conference
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By sending the message I give my consent to process my personal data within the DataWorkshop project. The consent above is given to Biznes Myśli – Uladzimir Aliakseichanka, Grodzka 42/1 street, 31-044 Kraków, Tax Identification Number (NIP): 6751364881. You can withdraw your consent at any time.
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