Dr. Jonathan Frankle
Chief Scientist
Chief Scientist at Databricks Mosaic AI and Adjunct Professor at Harvard University. Discovered the Lottery Ticket Hypothesis (ICLR 2019 Best Paper) and led the training and open-source release of MPT-7B, MPT-30B, and DBRX, proving enterprise foundation models can be trained cost-effectively on commodity clusters.
Areas of focus
Professional niches
Proof of Work
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
Authored the seminal ICLR 2019 Best Paper showing dense, randomly-initialized feed-forward networks contain subnetworks ('winning tickets') that—when trained in isolation from early initialization—reach test accuracy comparable to the original network in similar iterations.
Finding winning tickets requires iterative prune-and-retrain cycles, which is computationally heavier than standard one-pass training.
Context: Iterative Magnitude Pruning (IMP), ResNet, VGG, MNIST, CIFAR-10.
View missionMPT-7B and MPT-30B: Commercial-Grade Open-Source Foundation Models
Led the engineering and open-weights release of MPT models trained with FlashAttention, ALiBi positional embeddings, and fast streaming data loaders, providing reproducible blueprints for sub-$200k foundation model pre-training.
ALiBi positional embeddings exhibit slight performance degradation compared to RoPE when scaled past 128k context windows.
Context: MosaicML Composer, ALiBi 65k context length, 1T token pre-training.
View mission