PyData @ ActiveFence

We would like to thank ActiveFence for hosting us PHYSICALLY

Agenda
18:00-18:30 Gathering and snacks
18:30-18:45 Welcome words from our host
18:45-19:15 Data-centric AI | Shimon Harush, Data Science Team Lead at ActiveFence
19:15-19:45 VisiData: Data exploration in the terminal | Ram Rachum, ML Researcher at Bar-Ilan University
19:45-20:00 A short break
20:00-20:30 Modeling COVID-19 Spread in a Complex Reality | Hilla De-Leon, postdoc for data-science in the Technion
20:30-21:00 Unlocking the Power of Ordinal Data in Machine Learning | Assaf Ben Shimon, Data Scientist at NeuraLight

RSVP now to secure your spot!
Beit Avgad
Zeev Jabotinsky St 5, 7th Floor, Ramat Gan

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Data-centric AI | Shimon Harush, Data Science Team Lead at ActiveFence
In this talk, Shimon will discuss Data-centric AI and why it is important to prioritize data quality and quantity before investing in the algorithmic model. He will provide practical examples and share the benefits of this approach, including increased accuracy, scalability and flexibility

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VisiData: Data exploration in the terminal | Ram Rachum, ML Researcher at Bar-Ilan University
VisiData is a Pythonic tool for tabular data. It combines the clarity of a spreadsheet, the efficiency of the terminal, and the power of Python, into a lightweight utility which can handle millions of rows with ease. In this talk we'll learn to use VisiData for exploratory data analysis.

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Modeling COVID-19 Spread in a Complex Reality | Hilla De-Leon, postdoc for data-science in the Technion.
Over the past three years, the SARS-CoV-2 virus has infected and killed hundreds of millions of people. Along with the immediate need for treatment solutions, the COVID-19 pandemic has reinforced the need for mathematical models that can predict the spread of the pandemic in an ever-changing environment. In this talk, I will present a novel, dynamic Monte Carlo Agent-based Model (MAM), which is based on the basic principles of statistical physics. Using data from Israel on three major outbreaks, we compare predictions made by two models: MAM and SIR (susceptible-infectious-removed or recovered), a model that has been widely used to model COVID-19 transmission. We show that MAM outperforms SIR in all aspects. As a result of its flexibility, MAM allows for accurate examination of the effects of vaccinations and new variants

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Unlocking the Power of Ordinal Data in Machine Learning | Assaf Ben Shimon, Data Scientist at NeuraLight
Have you ever found yourself using satisfaction ratings, education levels or other ordered categorical data in your machine learning models? Common practices for dealing with such data often result in the loss of valuable information. In this talk, we'll explore the unique challenges posed by ordinal data and dive into techniques for unlocking the full potential of the data's latent structures.

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