Tara Sainath, PhD

Tara Sainath received her S.B., M.Eng and PhD in Electrical Engineering and Computer Science (EECS) from MIT. After her PhD, she spent 5 years at the Speech and Language Algorithms group at IBM T.J. Watson Research Center, before joining Google Research. She has served as a Program Chair for ICLR in 2017 and 2018. Also, she has co-organized numerous special sessions and workshops for many speech and machine learning conferences. In addition, she has served as a member of the IEEE Speech and Language Processing Technical Committee (SLTC) as well as the Associate Editor for IEEE/ACM Transactions on Audio, Speech, and Language Processing. She is an IEEE and ISCA Fellow. In addition, she is the recipient of the 2021 IEEE SPS Industrial Innovation Award as well as the 2022 IEEE SPS Signal Processing Magazine Best Paper Award. She is currently a Principal Research Scientist at Google, working on applications of deep neural networks for automatic speech recognition.

End-to-End Speech Recognition: The Journey from Research to Production(Talk)

Dr. Xiao-Li Meng

Xiao-Li Meng, the Founding Editor-in-Chief of Harvard Data Science Review and the Whipple V. N. Jones Professor of Statistics, is well known for his depth and breadth in research, his innovation and passion in pedagogy, his vision and effectiveness in administration, as well as for his engaging and entertaining style as a speaker and writer. Meng was named the best statistician under the age of 40 by Committee of Presidents of Statistical Societies (COPSS) in 2001, and he is the recipient of numerous awards and honors for his more than 150 publications in at least a dozen theoretical and methodological areas, as well as in areas of pedagogy and professional development. In 2020, he was elected to the American Academy of Arts and Sciences. He has delivered more than 400 research presentations and public speeches on these topics, and he is the author of “The XL-Files,” a thought-provoking and entertaining column in the Institute of Mathematical Statistics (IMS) Bulletin. His interests range from the theoretical foundations of statistical inferences (e.g., the interplay among Bayesian, Fiducial, and frequentist perspectives; frameworks for multi-source, multi-phase and multi- resolution inferences) to statistical methods and computation (e.g., posterior predictive p-value; expectation–maximization (EM) algorithm; Markov chain Monte Carlo; bridge and path sampling) to applications in natural, social, and medical sciences and engineering (e.g., complex statistical modeling in astronomy and astrophysics, assessing disparity in mental health services, and quantifying statistical information in genetic studies). Meng received his BS in mathematics from Fudan University in 1982 and his PhD in statistics from Harvard in 1990. He was on the faculty of the University of Chicago from 1991 to 2001 before returning to Harvard, where he served as the Chair of the Department of Statistics (2004–2012) and the Dean of Graduate School of Arts and Sciences (2012–2017).

Luca Antiga

Luca Antiga is the CTO at Lightning AI. He is an early contributor to PyTorch core and co-authored “Deep Learning with PyTorch” (published by Manning). He started his journey as a researcher in Bioengineering, and later co-founded Orobix, a company focused on building and deploying AI in production settings.

Accelerating the LLM Lifecycle on the Cloud(Talk)

Solution Showcase: Dive into the Lightning AI Open Source Stack and Lightning Studios to Unlock Reproducible AI Development on the Cloud.

Abstract:

PyTorch Lightning is a leading open source framework that was used to train several of the best generative AI models. With over 100 million downloads, it is the framework of choice for researchers and companies worldwide to train and fine-tune AI models. PyTorch Lightning and the rest of the Lightning AI stack, which includes Fabric, TorchMetrics, litgpt, litdata, and Thunder, provides a cutting-edge open-source foundation for practitioners. Just as the Lightning AI open source stack is democratizing access to cutting-edge AI research and engineering, Lightning Studios democratize access to cloud computing resources and solve the challenges of reproducibility and collaboration. Lightning Studios offer a laptop-like cloud experience, enabling seamless accelerated computing for individuals and organizations. Studios provide reproducible environments for development, training, and hosting applications across diverse hardware setups, with support for multi-node training and parallel data processing. Attendees will gain firsthand experience and access to datasets, models, and source code.