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OUR SPEAKERS
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I am a Machine Learning Researcher with experience in Computer Vision, Reinforcement Learning and Deep Learning among others. Currently, I am the Head of Research at Healthy.io, where I lead the algorithmic aspects of several healthcare-related products in various medical domains.
My academic background includes an M.Sc. in Computer Science from the Weizmann Institute, with a focus on Computer Vision and Deep Learning, as well as a B.Sc. in Computer Science and Neuroscience from Bar Ilan University.
I’m enthusiastic about using my skills and knowledge to improve people’s health and life.
I always strive to expand my knowledge and leveraging my expertise to make meaningful contributions.
Outside of work, I enjoy running and practicing yoga in my spare time.
Feel free to reach out if you’d like to connect or collaborate.
Sivan Biham
Head of Researcher
English, Hebrew
Languages:
Location:
Ramat Gan, Israel
Can also give an online talk/webinar
Paid only. Contact speaker for pricing!
MY TALKS
The Unspoken Relationship - Product Managers & Data Scientists
Data / AI / ML, Product, Soft Skills
Everyone talks about the relationship between product managers and the development team, but did you ever hear anyone talk about the relationship between product managers and the data science team? Maybe it’s time to start!
In this talk, Sivan will shed some light on this unspoken subject and explain what are the unique challenges of data scientist-product managers interactions. Drawing on her experience as an algorithm researcher in a product-oriented startup, and insights from working in a cross-platform team that includes product managers, developers, and data scientists, she will help you build a better product-data science relationship in your organization.
Quality Over Quantity - The Secret Behind Active Learning
Data / AI / ML
We all face the never-ending chase to collect more and more labeled data for training our models. What if the real story is not just the data volume, but its quality? What if I tell you that by collecting your labeled data wisely, you can not only use fewer data samples, which save time and money but also achieve equivalent or better performance levels? That's the secret of Active Learning.
In this talk, Sivan will present the Active Learning field. She will introduce the key concepts and how they can help you to improve your data collection process. As the active learning developer in her team, she will share tips for choosing a suitable active learning approach for your task.
Wounds Over Time - Tracking Wound Healing via 3D Models
Data / AI / ML
Measurement of changes to the area of chronic wounds over time is the cornerstone for wound management and assessment. In this Sivan will present a new framework to allow clinicians to visually track the healing progress over time. The presented framework artificially creates a consistent timeline from different visits of the same wound, where all views are shown at the same scale, location, and orientation. This framework receives as input a series of video scans and then outputs a consistent timeline of 2D projected images. The resulting timeline allows clinicians to visually monitor the wound healing process for the first time.
Garbage In - Garbage Out: The Art and Science of Effective Data Annotation
Data / AI / ML
Today's world is all about AI models, they are everywhere. Unfortunately, most of these models heavily depend on some kind of feedback, such as labels. The higher the quality and accuracy of our labels, the better our model can be. Our model is as good as our labels.
How do we acquire such labels? How can we monitor and evaluate its quality? What is the correct metric? Where should we start?
In this talk, Sivan will go over different types of annotation cycles and the different phases they contain. She will describe the impact each phase has on the quality and usability of the output labels. In addition, she will review the considerations and tradeoffs that need to be taken into account when defining a new label collection project. At the end of this talk you will know where to put your attention next time, in order not to throw your labels away.
Organizational Structure Shapes Software Design - A Practical Case Study of Conway's Law
Software Engineering, Leadership
You have been assigned to co-design a new software in collaboration with another team. You are focused on building your part of the software and consider your challenges. At the same time, the other team is doing just the same. Consequently, the process of software design becomes less effective.
What if we all were on the same team? Will it be easier and more effective? Will it change your perspective on how to design this software?
In this talk Sivan will introduce Conway’s law which links between the organizational structure and the software we design. She will share her real-life use case of how the organizational structure shaped the software design. She will describe the scenario, the considerations and what led to each choice. She will review the pros and cons of different organizational structures and share some tips that might help other teams. In this talk you will see how the organizational structure affects your point of view about software design, and how changing the structure changes the perspective.
The Unspoken Relationship - Product Managers & Data Scientists
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Quality Over Quantity - The Secret Behind Active Learning
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Wounds Over Time - Tracking Wound Healing via 3D Models
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Garbage In - Garbage Out: The Art and Science of Effective Data Annotation
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Organizational Structure Shapes Software Design - A Practical Case Study of Conway's Law
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