Context Tuning for LLMs
Hosted by Amir Feizpour and Dr. Mengye Ren
Fri, Sep 26, 2025
4:00 PM UTC (45 minutes)
Virtual (Zoom)
Free to join
By continuing, you agree to Maven's Terms and Privacy Policy.
Go deeper with a course
Fri, Sep 26, 2025
4:00 PM UTC (45 minutes)
Virtual (Zoom)
Free to join
Go deeper with a course
What you'll learn
Smarter Prompt Initialization
Boosted Few-Shot Performance
Efficiency Without Fine-Tuning
Why this topic matters
You'll learn from
Amir Feizpour
Founder @ Aggregate Intellect
Amir Feizpour is the founder, CEO, and Chief Scientist at Aggregate Intellect building a generative business brain for service and science based companies. Amir has built and grown a global community of 5000+ AI practitioners and researchers gathered around topics in AI research, engineering, product development, and responsibility. Prior to this, Amir was an NLP Product Lead at Royal Bank of Canada. Amir held a research position at University of Oxford conducting experiments on quantum computing resulting in high profile publications and patents. Amir holds a PhD in Physics from University of Toronto. Amir also serves the AI ecosystem as an advisor at MaRS Discovery District, works with several startups as fractional chief AI officer, and engages with a wide range of community audiences (business executives to hands-on developers) through training and educational programs. Amir leads Aggregate Intellect’s R&D via several academic collaborations.
Dr. Mengye Ren
Head @ Agentic Learning AI Lab (NYU)
Mengye Ren is an assistant professor of computer science and data science at New York University (NYU). He runs the Agentic Learning AI Lab. Before joining NYU, he was a visiting faculty researcher at Google Brain Toronto. From 2017 to 2021, he was a senior research scientist at Uber Advanced Technologies Group (ATG) and Waabi, working on self-driving vehicles. He received a Ph.D. in Computer Science from the University of Toronto. His research focuses on making machine learning more natural and human-like, in order for AIs to continually learn, adapt, and reason in naturalistic environments.
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