Igor Molybog is a researcher working on the training of Generative Artificial Intelligence. His research interests cover the area of computational techniques for learning from data that are highly scalable with the availability of the compute. More specifically, he is working on optimizers and model architecture scaling schemes that would allow predictable and efficient training of Transformer models containing hundreds of billions of parameters.

In 2022, Dr. Molybog graduated with a Ph.D. from the group of Professor Javad Lavaei at the Department of Industrial Engineering and Operations Research at University of California, Berkeley, where he worked on problems of algorithmic analysis and optimal control of complex safety-critical systems, such as power systems, transportation and telecommunication networks, AI recommendation and navigation systems, robotic systems, and others. His research spanned the theory of non-convex and conic optimization, stochastic control, machine learning, and computational and sampling complexity of learning algorithms. He designed data processing algorithms that are robust to noise and highly scalable with the amount of available computational resources.