Insights on AI research, hardware innovation, leadership, and the future of computing.
If both quantization and pruning compress models and speed up inference, which one ships in production? Quantization, almost always. A practitioner's view of model compression for LLM inference, the four places pruning still lives, and the layered recipe that real systems use.
Training a frontier model gets the headlines. Inference pays the bills. A walk down the four-layer stack — algorithm, compiler, runtime, hardware — showing how they compound to the 10-100x gains the industry has seen in the last eighteen months.
Frontier AI is simultaneously becoming a capital infrastructure business, a nascent scientific partner, and a weapons-grade security threat. Three stories from the week of April 7 that reveal something larger than any single headline.
How do you optimally map a neural network across analog and digital hardware? Our new Nature Communications paper introduces Mixed-Precision Supernetwork — a framework that finds the best mapping 2.2x faster while achieving 3.4% higher accuracy.
At the annual IBM/RPI Future of Computing Research Collaboration workshop, Track 2 showcased groundbreaking research spanning analog computing, efficient LLM inference, MoE quantization, and real-world AI infrastructure — all driven by exceptional students.
AI is undergoing a fundamental shift — from models that generate text to agents that autonomously plan, act, and self-correct. A deep dive into agent architectures, enterprise use cases, and the four trends shaping the field.
Why the next breakthrough in LLM inference won't come from bigger models, but from smarter memory. Our ASPLOS 2026 paper introduces STARC, a sparsity-optimized mapping scheme that achieves up to 93% latency reduction on Processing-in-Memory systems.
From Anthropic's eval-aware Claude to Alibaba's crypto-mining agent, this week marked the moment AI containment strategies fundamentally broke. A deep dive into four stories that define the Agency Era.
From CUDA kernels to analog accelerators, the gap between AI algorithms and the silicon that runs them is where the next breakthrough will come from. Here's what I teach Columbia students about bridging that divide.
A behind-the-scenes look at my research seminar on Scaling LLMs — where graduate students critique frontier papers and explore the path from foundation models to autonomous AI agents.
We can't just build bigger models — we need smarter systems. Drawing from both my IBM Research and Columbia teaching, here's why co-design thinking is the most important skill in AI today.
