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OUR SPEAKERS
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Sivan Biham is a Computer Vision Researcher and Algorithm Developer. She currently works on healthcare-related products in the research team at Healthy.io.
Sivan holds an M.Sc. in computer science from Weizmann Institute with a specialization in Computer Vision and Deep Learning, and a B.Sc. in both Computer Science and Neuroscience from Bar Ilan University.
She is enthusiastic about using her algorithmic skills and knowledge for improving people's health and life. Sivan is also a technical blogger and public speaker. In her spare time, you can find her practicing yoga or run in a park.
Sivan Biham
Computer Vision 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
In this talk, Sivan will introduce Conway's Law, a principle that highlights how the design of software systems is a direct reflection of the communication structures within the organizations that build them. She will explain how teams tend to create software architectures that mirror their organizational boundaries and workflows, often unconsciously. Through a real-life case study, Sivan will share how her team’s reorganization revealed the influence of this law, prompting them to reevaluate previous design decisions. Rather than simply causing shifts in the software, the reorganization led to an understanding of how the system would have been designed differently if the new structure had been in place from the start. By reviewing the scenario, considerations, and outcomes, she will highlight the pros and cons of different organizational models, offering practical tips for teams to better align their structures with design goals. Attendees will leave with a deeper understanding of how organizational changes can influence software architecture and how to leverage this knowledge to create more efficient and coherent systems.
Strategic Decision-Making in Data Science Projects
Leadership, Data / AI / ML
In this talk, we will introduce a framework to manage data science projects that emphasizes the importance of knowing when to persevere and when to quit. Quitting is often seen as a failure, but in reality, it’s a critical decision-making skill, especially in the fast-evolving field of data science where uncertainty is high and success is not guaranteed. In data science projects, missteps can lead to wasted resources, chasing diminishing returns, or solving the wrong problems. This framework, inspired by Annie Duke’s Quit: The Power of Knowing When to Walk Away, introduces concepts like "turnout points" and "kill criteria" to ensure teams know when it’s time to pivot or stop a project altogether. By tackling the most uncertain challenges early and applying strategic quit points, teams can avoid investing time in projects that won’t deliver value. This approach will help data science teams make better decisions, minimize wasted effort, and maximize impact.
Bridging the SQL Skills Gap: How LLM-Based Text-to-SQL Boosts Team Productivity
Leadership, Software Engineering, Data / AI / ML, Business Development, Product
In modern data-driven organizations, the ability to quickly access and analyze data is key to making informed decisions. However, querying databases often requires specialized SQL skills, which limits access to technical teams and increases the development cycle for data insights. In this talk, we will explore how Text-to-SQL technologies can dramatically enhance team productivity by shortening query development time. In this talk, I will share practical ways to implement Text-to-SQL for your own database, starting with the basics and improving results using prompt engineering techniques and database structure representation. By democratizing data access and improving cross-functional collaboration, Text-to-SQL can reduce bottlenecks, speed up decision-making, and enhance overall organizational efficiency.
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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Strategic Decision-Making in Data Science Projects
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Bridging the SQL Skills Gap: How LLM-Based Text-to-SQL Boosts Team Productivity
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4
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