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J

Speaker

Jonathan Frankle

4

talks

Chief Scientist - Neural Networks Databricks

Jonathan Frankle is Chief Scientist (Neural Networks) at Databricks, where he leads the research team to efficiently train neural networks. He joined Databricks through its $1.3B acquisition of MosaicML, where he was a founding member. He earned his PhD at MIT, where he empirically studied how neural networks learn, including the Lottery Ticket Hypothesis, which earned ICLR Best Paper status. He is also noted as a special guest AI/ML expert.

Bio from: Data + AI Summit 2025

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AI Evaluation from First Principles: You Can't Manage What You Can't Measure

Is your AI evaluation process holding back your system's true potential? Many organizations struggle with improving GenAI quality because they don't know how to measure it effectively. This research session covers the principles of GenAI evaluation, offers a framework for measuring what truly matters, and demonstrates implementation using Databricks.Key Takeaways:-Practical approaches for establishing reliable metrics for subjective evaluations-Techniques for calibrating LLM judges to enable cost-effective, scalable assessment-Actionable frameworks for evaluation systems that evolve with your AI capabilitiesWhether you're developing models, implementing AI solutions, or leading technical teams, this session will equip you to define meaningful quality metrics for your specific use cases and build evaluation systems that expose what's working and what isn't, transforming AI guesswork into measurable success.

TAO and Reinforcement Learning: Building AI With the Data You Have

Curious about the cutting-edge technology that's revolutionizing AI model performance? Join us for an in-depth exploration of TAO and discover how this innovative approach is transforming the capabilities of modern AI systems. This research-focused session peels back the layers of theoretical foundations, implementation challenges, and breakthrough applications that make TAO one of the most promising advancements in AI development. Key takeaways: Understanding the fundamental principles behind TAO and how it differs from conventional optimization techniques Examining the quantifiable improvements in model accuracy, efficiency, and generalization capabilities Exploring real-world case studies where TAO has solved previously intractable AI challenges Analyzing current research directions and future potential for further enhancements Whether you're a research scientist, AI engineer, or technical leader, this session will equip you with valuable insights into how TAO can be leveraged to push your AI models beyond current limitations.

talk
with Jonathan Hsieh (LanceDB) , Cathy Yin (Databricks) , Andrew Shieh (Databricks) , Ziyi Yang (Databricks) , Andy Konwinski (Databricks) , Denny Lee (Databricks) , Asfandyar Qureshi (Databricks) , Yuki Watanabe (Databricks) , Brandon Cui (Databricks) , Andrew Drozdov (Databricks) , Anand Kannappan (Patronus AI) , Harsh Panchal (Databricks) , Tomu Hirata (Databricks) , Daya Khudia (Databricks) , Jose Javier Gonzalez (Databricks) , Jasmine Collins (Databricks) , MAHESWARAN SATHIAMOORTHY (Bespoke Labs) , Jonathan Chang (Databricks) , Matei Zaharia (Databricks) , Alexander Trott (Databricks) , Tejas Sundaresan (Databricks) , Pallavi Koppol (Databricks) , Jonathan Frankle (Databricks) , Erich Elsen (Databricks) , Ivan Zhou (Databricks) , Davis Blalock , Gayathri Murali (META)

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