Tianqi Chen, an Assistant Professor at Carnegie Mellon University and Chief Technologist at OctoML, shares insights from his impactful career in machine learning. He discusses his groundbreaking work on XGBoost, an influential optimization library that transformed data science competitions. The conversation delves into deep learning frameworks like MXNet and TVM, highlighting their role in modern generative AI. Chen also reflects on the synergy between machine learning and systems research, emphasizing the importance of scalability and efficiency in today's complex models.

Niloofar Mireshghallah is a postdoctoral scholar at the University of Washington. Her research focuses on privacy, natural language processing, and the societal implications of machine learning. Niloofar completed her PhD in 2023 at UC San Diego, where she was advised by Taylor Berg-Kirkpatrick. Her PhD thesis is titled "Auditing and Mitigating Safety Risks in Large Language Models." We discuss her journey into research and her work on privacy and LLMs, including how privacy is defined, common attacks and mitigations, differential privacy, and the balance between memorization and generalization. - Episode notes: www.wellecks.com/thesisreview/episode47.html - Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter - Support The Thesis Review at www.patreon.com/thesisreview or www.buymeacoffee.com/thesisreview

Yulia Tsvetkov is a Professor in the Allen School of Computer Science & Engineering at the University of Washington. Her research focuses on multilingual NLP, NLP for social good, and language generation. Yulia's PhD thesis is titled "Linguistic Knowledge in Data-Driven Natural Language Processing", which she completed in 2016 at CMU. We discuss getting started in research, then move to Yulia's work in the thesis that combines ideas from linguistics and natural language processing. We discuss low-resource and multilingual NLP, large language models, and great advice about research and beyond. - Episode notes: www.wellecks.com/thesisreview/episode46.html - Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter - Find out more info about the show at www.wellecks.com/thesisreview - Support The Thesis Review at www.patreon.com/thesisreview or www.buymeacoffee.com/thesisreview

Luke Zettlemoyer is a Professor at the University of Washington and Research Scientist at Meta. His work spans machine learning and NLP, including foundational work in large-scale self-supervised pretraining of language models. Luke's PhD thesis is titled "Learning to Map Sentences to Logical Form", which he completed in 2009 at MIT. We talk about his PhD work, the path to the foundational Elmo paper, and various topics related to large language models. - Episode notes: www.wellecks.com/thesisreview/episode45.html - Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter - Find out more info about the show at www.wellecks.com/thesisreview - Support The Thesis Review at www.patreon.com/thesisreview or www.buymeacoffee.com/thesisreview

Hady Elsahar is a Research Scientist at Naver Labs Europe. His research focuses on Neural Language Generation under constrained and controlled conditions. Hady's PhD was on interactions between Natural Language and Structured Knowledge bases for Data2Text Generation and Relation Extraction & Discovery, which he completed in 2019 at the Université de Lyon. We talk about his phd work and how it led to interests in multilingual and low-resource in NLP, as well as controlled generation. We dive deeper in controlling language models, including his interesting work on distributional control and energy-based models. - Episode notes: www.wellecks.com/thesisreview/episode44.html - Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter - Find out more info about the show at www.wellecks.com/thesisreview - Support The Thesis Review at www.patreon.com/thesisreview or www.buymeacoffee.com/thesisreview

Swarat Chaudhuri, an Associate Professor at the University of Texas, delves into the fascinating intersection of programming languages and machine learning. He discusses the evolution of formal verification and the integration of model checking within AI systems. The conversation highlights advancements in neurosymbolic programming, enhancing reliability in software. Swarat also provides insights on developing reusable modules and emphasizes the importance of practical contributions in research, especially in AI safety and real-world applications.

Charles Sutton is a Research Scientist at Google Brain and an Associate Professor at the University of Edinburgh. His research focuses on deep learning for generating code and helping people write better programs. Charles' PhD thesis is titled "Efficient Training Methods for Conditional Random Fields", which he completed in 2008 at UMass Amherst. We start with his work in the thesis on structured models for text, and compare/contrast with today's large language models. From there, we discuss machine learning for code & the future of language models in program synthesis. - Episode notes: https://cs.nyu.edu/~welleck/episode42.html - Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter - Find out more info about the show at https://cs.nyu.edu/~welleck/podcast.html - Support The Thesis Review at www.patreon.com/thesisreview or www.buymeacoffee.com/thesisreview

In this discussion, Talia Ringer, an Assistant Professor at the University of Illinois Urbana-Champaign, shares her insights on formal verification and proof repair. She explains how proof repair automates the maintenance of formal proofs as software evolves. Talia also discusses the intersection of machine learning with proof engineering and highlights her academic journey from mathematics to software engineering. The conversation dives into the complexities of program verification and the role of theory in problem-solving within software development.

Lisa Lee is a Research Scientist at Google Brain. Her research focuses on building AI agents that can learn and adapt like humans and animals do. Lisa's PhD thesis is titled "Learning Embodied Agents with Scalably-Supervised Reinforcement Learning", which she completed in 2021 at Carnegie Mellon University. We talk about her work in the thesis on reinforcement learning, including exploration, learning with weak supervision, and embodied agents, and cover various topics related to trends in reinforcement learning. - Episode notes: https://cs.nyu.edu/~welleck/episode40.html - Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter - Find out more info about the show at https://cs.nyu.edu/~welleck/podcast.html - Support The Thesis Review at www.patreon.com/thesisreview or www.buymeacoffee.com/thesisreview

Burr Settles leads the research group at Duolingo, a language-learning website and mobile app whose mission is to make language education free and accessible to everyone. Burr’s PhD thesis is titled "Curious Machines: Active Learning with Structured Instances", which he completed in 2008 at the University of Wisconsin-Madison. We talk about his work in the thesis on active learning, then chart the path to Burr’s role at DuoLingo. We discuss machine learning for education and language learning, including content, assessment, and the exciting possibilities opened by recent advancements. - Episode notes: https://cs.nyu.edu/~welleck/episode39.html - Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter - Find out more info about the show at https://cs.nyu.edu/~welleck/podcast.html - Support The Thesis Review at www.patreon.com/thesisreview or www.buymeacoffee.com/thesisreview