Training and fine-tuning LLMs

Jonathan Frankle is Chief Scientist at MosaicML, where he leads the company's research team toward the goal of developing more efficient algorithms for training neural networks. In his PhD at MIT, he empirically studied deep learning with Prof. Michael Carbin, specifically the properties of sparse networks that allow them to train effectively (his "Lottery Ticket Hypothesis" - ICLR 2019 Best Paper). In addition to his technical work, he is actively involved in policymaking around challenges related to machine learning. He will be joining the computer science faculty at Harvard in the fall of 2023. He earned his BSE and MSE in computer science at Princeton and has previously spent time at Google Brain, Facebook AI Research, and Microsoft as an intern and Georgetown Law as an “Adjunct Professor of Law.”

Weiwei Yang is a Principal Software Development Engineering Manager leading an applied machine learning team at Microsoft Research (MSR). Her research interests lie in resource-efficient learning methods inspired by biological learning. Weiwei aims to democratize AI by addressing sustainability, robustness, scalability, and efficiency in ML. She has successfully applied her research to organizational science, countering human trafficking, and stabilizing energy grids. Before joining Microsoft Research, Weiwei worked extensively in Bay Area startups and managed several engineering teams.

Mark Saroufim is an engineer on the PyTorch Applied AI team where he maintains and contributes to a wide variety of PyTorch projects. His interests are in broadly improving the performance and usability of real world ML systems. Mark will be setting up the evaluation pipeline, answering technical questions from the community as well reproducing winning models.