CONFERENCE SCHEDULE

10:00 — 10:10 (CET) Greeting Speech

Elina Valeeva, CEO & Founder of Meditivity / Women in Tech ambassador

Ksenia Matveeva, Dell Technologies, Innovation programs manager

10:10 — 11:10 (CET) Machine Learning in Medical Imaging: who’s in charge?

Ekaterina Kondrateva, PhD Student Skoltech

Who is in charge? We are going to discuss the decision-making process inside the ML diagnostic tools as well as medical policies around the AI systems. I will share my experience in building MRI-based models that can support the decision-making process of a doctor. We will discuss the current state-of-the-art solutions and think about the moral and legal responsibilities that come with AI applications in medicine. A bit of philosophy, Big Data, Computer Vision — a standard kit for any ML practitioner. Vision, Artificial Intelligence — a standard set for any medical ML practitioner.

11:10 — 12:10 (CET) 10 Machine Learning Concepts Every Startup Founder Must Know

Noa Weiss, Data Science & Machine Learning Consultant

Do you often find yourself in meetings with obscure machine learning terms thrown all around you, and no idea what they mean? There is hope yet. Designed for tech professionals, this talk introduces 10 must-know concepts in ML, giving you the basic tools to better understand the work around you. Rock the next water cooler chat with your newfound knowledge of these fundamentals of machine learning.

12:10 — 12:50 (CET) The magic behind your playlist

Anna Aljanaki, Data Scientist and Lecturer

Have you ever wondered how does YouTube always know that a song is copyrighted? What sort of magic are music streaming services doing to organize your music? Or even where is that service that you can use to find a song that’s stuck in your head? In this talk I’ll explain some of the techniques used to do these tricks. We will get acquainted with the field of music information retrieval, its challenges and toolset.

12:50 — 13:40 (CET) Plants weighting based on camera footage

Aelita Shaykhutdinova, Data Scientist

Have you ever wondered what it takes to get a fresh salad on your table, how this process can be improved in terms of ecology, time and money spent. In this topic we will cover how Data Science can help in achieving the goals. In particular, how we did plant weighting based on camera footage using computer vision methods.

13:40 — 14:30 (CET) Why do we need Data Science for the Largest Food Retailer in Russia

Valeriy Babushkin, Senior Director of Data Science at X5 Retail Group

X5 Retail Group, with more than 17000 stores is the largest food retailer in Russia. In 2 years the Data Science team grew up from five to more than a hundred people and Big Data in total from twenty to five hundred. Why do we need so many Data Scientist and what we do them? Pay attention to this talk and you will find out.

14:30 — 15:40 (CET) Can we make machines understand human emotions

Rajasekhar Nannapaneni, Dell Technologies, Consultant Solutions Architect

This talk starts with a basic overview of Machine Learning and Deep Learning along with insights on how they differ. We will then cover one of the use cases of human emotion recognition using Machine Learning techniques and it’s applicability.

15:40 — 16:20 (CET) DS career during the crisis (feat. all speakers)

Polina Polunina, Data Science expert

The global industry outlook during COVID-19: how the pandemic has changed work and hiring processes both in business and science? What’s happened to the data science job market? Are there bounds to hire talents from non-residence geographical regions? How did business vectors change during COVID? How did it affect regural tasks for a data scientist? Career perspectives, business challenges, personal failures and successes will be discussed with top industry and academy professionals.

Please, find the additional information on the website of the conference http://witdatascience.tilda.ws/