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My name is Noa Lubin, I am a data science researcher at Diagnostic Robotics and formerly worked as a researcher at NASA, Amazon, Elbit and IAI.
I love data, and my main focus is on NLP, healthcare and space. I love public speaking, mostly about topics in AI.
I have a Computer Science Master's degree, Bar-Ilan University (Magna Cum Laude), with an NLP thesis advised by Prof. Yoav Goldberg.
And, an Electrical Engineering Bachelor's degree, Technion (Summa Cum Laude).
I am an Analog Astronaut at D-MARS and the founder and president of Space It Up.
I am also the Founder and Chair of the Board of Hydrocephalus Israel Non-profit.

Noa Lubin

Senior Data Scientist
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English, Hebrew
Kiryat Ono, Israel
Can also give an online talk/webinar
Paid only. Contact speaker for pricing!


Big Data, Small Planets - Using Deep Learning Methods to Find Exoplanets at NASA

Data / AI / ML, Inspirational, Innovation

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TESS, Transiting Exoplanet Survey Satellite, is a critical mission to increase our understanding of earth-like planets outside our solar system. TESS will survey 100,000s of the brightest stars near us to search for exoplanets, planets outside our solar system. TESS will achieve this by looking for transits. Manual classification of transits is very challenging and time consuming. Thus, we need machine classification systems to automatically determine whether a transit indicates an exoplanet or not. In this talk we will talk about TESS and former mission Kepler, NASA, exoplanets and the use of deep learning methods to identify them.
Hebrew: https://www.youtube.com/watch?v=_qnSM5mMUBA
Podcast: https://podtail.com/da/podcast/--853/--3/

Latest Trends in NLP

Data / AI / ML, Innovation

In this lecture, we will discuss the latest trends in Natural Language Processing.
The NLP field evolved into the world of Machine Learning and lately to complex sequential Deep Learning models.
This development leads to amazing tools that are capable of solving different problems such as: text generation, machine translation, language understanding and more. We will see the technology, its capabilities and limitations.
We will also talk about the strong ethical issues these tools bring, such as fairness among minority groups and fake news.

Avoiding the Cobra Effect: Harnessing Data to Improve Healthcare

Data / AI / ML, Innovation

What is the Cobra Effect? And how is it related to AI in healthcare? In this talk, you will learn how at Diagnostic Robotics we create insights from claims data, a form of administrative data at large scale, which provides a great opportunity for AI in healthcare. You will understand how we use medical code embeddings and deep learning methods to build predictive proactive models that benefit the patients and reduce the cost of healthcare. We will also discuss the concept of causal machine learning, its use to emulate randomised controlled trials and see how it’s related to our models.

Aligning Vector-spaces Using Noisy Supervised Lexicons

Data / AI / ML, Innovation

The problem of learning to translate between two vector spaces given a set of aligned points arises in several application areas of NLP. Current solutions assume that the lexicon which defines the alignment pairs is noise-free. We consider the case where the set of aligned points can contain an amount of noise, in the form of incorrect lexicon pairs and show that this arises in practice by analysing the edited dictionaries after the cleaning process. We demonstrate that such noise substantially degrades the accuracy of the learned translation when using current methods. We propose a model that accounts for noisy pairs. This is achieved by introducing a generative model with a compatible iterative EM algorithm. The algorithm jointly learns the noise level in the lexicon, finds the set of noisy pairs, and learns the mapping between the spaces. We demonstrate the effectiveness of our proposed algorithm on two alignment problems.

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