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SPCL_Bcast: Theodoros Rekatsinas, Data Selection - Data Challenges when Training Generative Models, Thursday, 8th May, 9AM CET
by Marcin Chrapek 06 May '25

06 May '25

Theodoros Rekatsinas The Scalable Parallel Computing Lab's *SPCL_Bcast* seminar continues with *Theodoros Rekatsinas**of **Axelera AI* presenting on *Data Selection - Data Challenges when Training Generative Models*. Everyone is welcome to attend (over Zoom)! *When:* Thursday, 8th May, 9AM CET *Where:* Zoom Join <https://spcl.inf.ethz.ch/Bcast/join> *Abstract:* This talk explores how strategic data selection can improve the efficiency of training generative AI models. I will cover approaches for both pre-training and fine-tuning that achieve comparable performance to full training while using only a fraction of the data. During the talk I will cover key filtering techniques and data selection methods for efficient pre-training as well as the connection between data selection and optimal transport for optimized fine-tuning. I will conclude with promising future directions for adaptive data selection research. *Biography:* Theo Rekatsinas is the VP of Machine Learning at Axelera AI. before that he was a tech lead at Apple working on on-device intelligence and a senior manager in the Apple Knowledge Graph (KG) team responsible for the KG construction and Graph Machine learning teams. Theo co-founded Inductiv (acquired by Apple), a company that developed Generative AI solutions for identifying and correcting errors in data. Theo was also a Professor of Computer Science at ETH Zürich and the University of Wisconsin-Madison. Theo's research focuses on scalable machine learning over billion-scale relational and graph-structured data. His research focused on exploring the fundamental connections between data preparation, data integration, and knowledge management with statistical machine learning and probabilistic inference. More details & future talks <https://spcl.inf.ethz.ch/Bcast/> Scalable Parallel Computing Lab (SPCL) Department of Computer Science, ETH Zurich Website <https://spcl.inf.ethz.ch> X(Twitter) <https://twitter.com/spcl_eth> YouTube <https://www.youtube.com/@spcl> GitHub <https://github.com/spcl>

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