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

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[39] Burr Settles - Curious Machines: Active Learning with Structured Instances

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

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[40] Lisa Lee - Learning Embodied Agents with Scalably-Supervised RL

Talia Ringer is an Assistant Professor with the Programming Languages, Formal Methods, and Software Engineering group at University of Illinois Urbana-Champaign. Her research focuses on formal verification and proof engineering technologies.

Talia's PhD thesis is titled "Proof Repair", which she completed in 2021 at the University of Washington. 

We discuss software verification and her PhD work on proof repair for maintaining verified systems, and discuss the intersection of machine learning with her work.

- Episode notes: https://cs.nyu.edu/~welleck/episode41.html

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[41] Talia Ringer - Proof Repair

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

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[42] Charles Sutton - Efficient Training Methods for Conditional Random Fields

Swarat Chaudhuri is an Associate Professor at the University of Texas. His lab studies problems at the interface of programming languages, logic and formal methods, and machine learning.

Swarat's PhD thesis is titled "Logics and Algorithms for Software Model Checking", which he completed in 2007 at the University of Pennsylvania.

We discuss reasoning about programs, formal methods & safer machine learning systems, and the future of program synthesis & neurosymbolic programming.

- Episode notes: www.wellecks.com/thesisreview/episode43.html

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[43] Swarat Chaudhuri - Logics and Algorithms for Software Model Checking

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

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[44] Hady Elsahar - NLG from Structured Knowledge Bases (& Controlling LMs)

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

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[45] Luke Zettlemoyer - Learning to Map Sentences to Logical Form

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

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[46] Yulia Tsvetkov - Linguistic Knowledge in Data-Driven NLP

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

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[47] Niloofar Mireshghallah - Auditing and Mitigating Safety Risks in Large Language Models

Tianqi Chen is an Assistant Professor in the Machine Learning Department and Computer Science Department at Carnegie Mellon University and the Chief Technologist of OctoML. His research focuses on the intersection of machine learning and systems.

Tianqi's PhD thesis is titled "Scalable and Intelligent Learning Systems," which he completed in 2019 at the University of Washington. We discuss his influential work on machine learning systems, starting with the development of XGBoost,an optimized distributed gradient boosting library that has had an enormous impact in the field. We also cover his contributions to deep learning frameworks like MXNet and machine learning compilation with TVM, and connect these to modern generative AI.

- Episode notes: www.wellecks.com/thesisreview/episode48.html

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[48] Tianqi Chen - Scalable and Intelligent Learning Systems

John Schulman is a Research Scientist and co-founder of Open AI. John co-leads the reinforcement learning team, researching algorithms that safely and efficiently learn by trial and error and by imitating humans.

His PhD thesis is titled "Optimizing Expectations: From Deep Reinforcement Learning to Stochastic Computation Graphs", which he completed in 2016 at Berkeley. We talk about his work on stochastic computation graphs and TRPO, how it evolved to PPO and how it's used in large-scale applications like Open AI Five, as well as his recent work on generalization in RL. 

Episode notes: https://cs.nyu.edu/~welleck/episode7.html

Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter, and find out more info about the show at https://cs.nyu.edu/~welleck/podcast.html

Support The Thesis Review at www.buymeacoffee.com/thesisreview

[07] John Schulman - Optimizing Expectations: From Deep RL to Stochastic Computation Graphs

Each episode of The Thesis Review is a conversation centered around a researcher's PhD thesis, giving insight into their history, revisiting older ideas, and providing a valuable perspective on how their research has evolved (or stayed the same) since.

[07] John Schulman - Optimizing Expectations: From Deep RL to Stochastic Computation Graphs

The Thesis Review

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