avigal

SOUND: Sanity Checking of Pipelines for Uncertain and Sparse Data Series

🎉 BREAKTHROUGH PAPER ACCEPTED AT #ICDE2025! 🎉 Beyond thrilled to announce our paper “SOUND: Sanity Checking of Pipelines for Uncertain and Sparse Data Series” has been accepted at the prestigious International Conference on Data Engineering (#ICDE2025)! Authors: Hermann J. Stolte, Iftach Sadeh, Elisa Pueschel, Avigdor Gal, and Matthias Weidlich 🚀 We’re revolutionizing how data pipelines are validated! Our […]

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A Rank-Based Approach to Recommender System’s Top-K Queries with Uncertain Scores”

The paper “A Rank-Based Approach to Recommender System’s Top-K Queries with Uncertain Scores”, co-authored by Coral Scharf, Avigdor Gal, Haggai Roitman, and Carmel Domshlak will be presented in #SIGMOD 2025. Thrilled to share this groundbreaking work that revolutionizes how we think about ranking in recommender systems! 🚀 For years, recommender systems have struggled with uncertain scores, often

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Business Process Optimization workshop

We are happy to announce that Avi (Avigdor) Gal will deliver the keynote of the Business Process Optimization workshop (https://lnkd.in/eNfDTprR). In an interactive session, he will discuss his ideas on responsible decision making in business process design. In particular, he will discuss responsible use of data and AI in business processes. Read more: https://lnkd.in/eE-iFs4P

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An unsupervised machine learning approach to the spatial analysis of urban systems through neighbourhoods’ dynamics

The paper “An unsupervised machine learning approach to the spatial analysis of urban systems through neighbourhoods’ dynamics” is a result of a collaborative effort with Alon Sagi, Avigdor Gal, Dani Broitman, and Danny Czamanski. It tells the story of the dynamics that affect the social-environmental sustainability of UK’s urban system using unsupervised learning. Read about

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CopycHats

CopycHats is an LLM-based agent that performs schema matching just like humans, allowing us to test revolving doors research questions that we cannot with humans. Matan Solomon presented this work, co-authored with Bar Genossar and Avigdor Gal as part of the hilda2024 workshop in SIGMOD. Link: https://dl.acm.org/doi/10.1145/3665939.3665963

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battleship

Improving entity resolution while playing battleship? Yes, we can. The paper, co-authored by Bar Genossar, Roee Shraga, and Avigdor Gal, was presented in sigmod2024, offering an active learning approach to entity resolution, using spatial uncertainty over a vectorspace.

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