Andre Martins is an Associate Professor at IST and VP of AI Research at Unbabel in Lisbon, Portugal. His research focuses on natural language processing and machine learning.

Andre’s PhD thesis is titled, "The Geometry of Constrained Structured Prediction: Applications to Inference and Learning of Natural Language Syntax", which he completed in 2012 at Carnegie Mellon University and IST.

We talk about his work in the thesis on structured prediction in NLP, and discuss connections between his thesis work on later work on sparsity, sparse communication, and more.

- Episode notes: https://cs.nyu.edu/~welleck/episode32.html

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[32] Andre Martins - The Geometry of Constrained Structured Prediction

Andrew Lampinen is a research scientist at DeepMind. His research focuses on cognitive flexibility and generalization. 

Andrew’s PhD thesis is titled "A Computational Framework for Learning and Transforming Task Representations", which he completed in 2020 at Stanford University.

We talk about cognitive flexibility in brains and machines, centered around his work in the thesis on meta-mapping. We cover a lot of interesting ground, including complementary learning systems and memory, compositionality and systematicity, and the role of symbols in machine learning.

- Episode notes: https://cs.nyu.edu/~welleck/episode38.html

- Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter

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[38] Andrew Lampinen - A Computational Framework for Learning and Transforming Task Representations

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

- Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter

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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

- Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter

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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

- Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter

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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

- Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter

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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

- Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter

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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

- Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter

- Find out more info about the show at www.wellecks.com/thesisreview

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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

- Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter

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

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.

[32] Andre Martins - The Geometry of Constrained Structured Prediction

The Thesis Review

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