USF Data Science & Artificial Intelligence Speaker Series

We are excited to welcome Nathaniel Stevens, Associate Professor in the Department of Statistics and Actuarial Science at the University of Waterloo in Canada, for our first Data Science Speaker Series talk of the year.

Talk Abstract: As a means of continual improvement and innovation, online controlled experiments are widely used by internet and technology companies to test and evaluate product changes, and new features, and to ensure that user feedback drives decisions. However, experiments on networks are complicated by the fact that the stable unit treatment value assumption (SUTVA) no longer holds. Due to the interconnectivity of users in these networks, a user’s outcome may be influenced by their own treatment assignment as well as the treatment assignment of those they are socially connected with. The design and analysis of the experiment must account for this. In this talk we will explore recent work in this area and focus particularly on the general additive network effect (GANE) family of non-linear models that jointly and flexibly model treatment and network effects. We will then consider Bayesian optimal design in the context of such models, proposing the use of the genetic algorithm to optimize for accurate and precise estimation of treatment effects, while accounting for parameter uncertainty. Through numerical studies with various real-life networks and network-outcome models, we demonstrate the robust performance of our methods compared to existing design construction strategies.
Co-authors: Trang Bui (University of Rochester), Stefan Steiner (University of Waterloo)

Speaker Biography
Nathaniel Stevens is an Associate Professor in the Department of Statistics and Actuarial Science at the University of Waterloo. Prior to this Nathaniel held a faculty position at the University of San Francisco in the Department of Mathematics and Statistics. He is and has been Program Director of both universities’ undergraduate data science programs. Having overseen 30+ data science internships at 20+ companies, Nathaniel is interested in using statistics to solve practical problems, and he has a passion for inspiring and training students to do the same. His research interests lie at the intersection of data science and industrial statistics; his publications span topics including experimental design and A/B testing, social network modeling and monitoring, survival and reliability analysis, measurement system analysis, and the design and analysis of estimation-based alternatives to traditional hypothesis testing.

We look forward to seeing you!

#DataScience #Statistics #AIBTesting #Experimentation #USFCA #USFMSDSAI #DataInstitute #MachineLearning #NetworkEffects #BayesianStatistics #TechTalk #DataScienceSpeakerSeries

We are excited to welcome Suman Debnath, Technical Lead in Machine Learning at Anyscale, for a practical and intuitive introduction to distributed training.

Talk Description:
As modern AI models continue to grow, single-GPU training is no longer enough. Distributed training has become essential, but scaling models introduces challenges that require understanding communication patterns, system bottlenecks, and key trade-offs.
In this session, we will break down distributed training from first principles. We will explore why single-GPU training hits limits, how transformer models manage memory, and what techniques like gradient accumulation, checkpointing, and data parallelism actually do.
We will also demystify communication primitives, walk through ZeRO-1, ZeRO-2, ZeRO-3 and FSDP, and show how compute and communication can be overlapped for better efficiency. Finally, we will connect these concepts to real-world tooling used in frameworks like Ray and PyTorch. Attendees will gain a clear, grounded understanding of how distributed training works and when to apply different strategies.

Bio:
Suman Debnath is a Technical Lead in Machine Learning at Anyscale, where he works on large-scale distributed training, fine-tuning, and inference optimization in the cloud. His expertise spans Natural Language Processing, Large Language Models, and Retrieval-Augmented Generation.
He has also spoken at more than one hundred global conferences and events, including PyCon, PyData, and ODSC, and has previously built performance benchmarking tools for distributed storage systems.

We look forward to seeing you!

#DataScience #MachineLearning #DistributedTraining #Ray #PyTorch #LLM #RAG #DeepLearning #USFCA #USFMSDSAI #DataInstitute #AIEngineering #TechTalk