Michela Paganini is a Research Scientist at DeepMind. Her research focuses on investigating ways to compress and scale up neural networks.

Michela's PhD thesis is titled "Machine Learning Solutions for High Energy Physics", which she completed in 2019 at Yale University. We discuss her PhD work on deep learning for high energy physics, including jet tagging and fast simulation for the ATLAS experiment at the Large Hadron Collider, and the intersection of machine learning and physics.

Episode notes: https://cs.nyu.edu/~welleck/episode21.html

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[21] Michela Paganini - Machine Learning Solutions for High Energy Physics

Simon Shaolei Du is an Assistant Professor at the University of Washington. His research focuses on theoretical foundations of deep learning, representation learning, and reinforcement learning.

Simon's PhD thesis is titled "Gradient Descent for Non-convex Problems in Modern Machine Learning", which he completed in 2019 at Carnegie Mellon University. We discuss his work related to the theory of
gradient descent for challenging non-convex problems that we encounter in deep learning. We cover various topics including connections with the Neural Tangent Kernel, theory vs. practice, and future research directions.

Episode notes: https://cs.nyu.edu/~welleck/episode23.html

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[23] Simon Du - Gradient Descent for Non-convex Problems in Modern Machine Learning

Martin Arjovsky is a postdoctoral researcher at INRIA. His research focuses on generative modeling, generalization, and exploration in RL. 

Martin's PhD thesis is titled "Out of Distribution Generalization in Machine Learning", which he completed in 2019 at New York University. We discuss his work on the influential Wasserstein GAN early in his PhD, then discuss his thesis work on out-of-distribution generalization which focused on causal invariance and invariant risk minimization.

Episode notes: https://cs.nyu.edu/~welleck/episode24.html

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[24] Martin Arjovsky - Out of Distribution Generalization in Machine Learning

Tomas Mikolov is a Senior Researcher at the Czech Institute of Informatics, Robotics, and Cybernetics. His research has covered topics in natural language understanding and representation learning, including Word2Vec, and complexity.

Tomas's PhD thesis is titles "Statistical Language Models Based on Neural Networks", which he completed in 2012 at the Brno University of Technology. We discuss compression and recurrent language models, the backstory behind Word2Vec, and his recent work on complexity & automata.

Episode notes: https://cs.nyu.edu/~welleck/episode25.html

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[25] Tomas Mikolov - Statistical Language Models Based on Neural Networks

Danqi Chen is an assistant professor at Princeton University, co-leading the Princeton NLP Group. Her research focuses on fundamental methods for learning representations of language and knowledge, and practical systems including question answering, information extraction and conversational agents.

Danqi’s PhD thesis is titled "Neural Reading Comprehension and Beyond", which she completed in 2018 at Stanford University. We discuss her work on parsing, reading comprehension and question answering. Throughout we discuss progress in NLP, fundamental challenges, and what the future holds.

Episode notes: https://cs.nyu.edu/~welleck/episode27.html

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[27] Danqi Chen - Neural Reading Comprehension and Beyond

Tengyu Ma is an Assistant Professor at Stanford University. His research focuses on deep learning and its theory, as well as various topics in machine learning.


Tengyu's PhD thesis is titled "Non-convex Optimization for Machine Learning: Design, Analysis, and Understanding", which he completed in 2017 at Princeton University.


We discuss theory in machine learning and deep learning, including the 'all local minima are global minima' property, overparameterization, as well as perspectives that theory takes on understanding deep learning.


- Episode notes: https://cs.nyu.edu/~welleck/episode29.html

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[29] Tengyu Ma - Non-convex Optimization for Machine Learning

Dustin Tran is a research scientist at Google Brain. His research focuses on advancing science and intelligence, including areas involving probability, programs, and neural networks.

Dustin’s PhD thesis is titled "Probabilistic Programming for Deep Learning", which he completed in 2020 at Columbia University.

We discuss the intersection of probabilistic modeling and deep learning, including the Edward library and the novel inference algorithms and models that he developed in the thesis.

- Episode notes: https://cs.nyu.edu/~welleck/episode30.html

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[30] Dustin Tran - Probabilistic Programming for Deep Learning

Jay McClelland is a Professor in the Psychology Department and Director of the Center for Mind, Brain, Computation and Technology at Stanford. His research addresses a broad range of topics in cognitive science and cognitive neuroscience, including Parallel Distributed Processing (PDP).

Jay's PhD thesis is titled "Preliminary Letter Identification in the Perception of Words and Nonwords", which he completed in 1975 at University of Pennsylvania.

We discuss his work in the thesis on the word superiority effect, how it led to the Integrated Activation model, the path to Parallel Distributed Processing and the connectionist revolution, and distributed vs rule-based and symbolic approaches to modeling human cognition and artificial intelligence.

- Episode notes: https://cs.nyu.edu/~welleck/episode31.html

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[31] Jay McClelland - Preliminary Letter Identification in the Perception of Words and Nonwords

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

Michael R. Douglas is a theoretical physicist and Professor at Stony Brook University, and Visiting Scholar at Harvard University. His research focuses on string theory, theoretical physics and its relations to mathematics.

Michael's PhD thesis is titled, "G/H Conformal Field Theory", which he completed in 1988 at Caltech.

We talk about working with Feynman, Sussman, and Hopfield during his PhD days, the superstring revolutions and string theory, and machine learning's role in the future of science and mathematics.

- Episode notes: https://cs.nyu.edu/~welleck/episode33.html

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[33] Michael R. Douglas - G/H Conformal Field Theory

Sasha Rush is an Associate Professor at Cornell Tech and researcher at Hugging Face. His research focuses on building NLP systems that are safe, fast, and controllable.

Sasha's PhD thesis is titled, "Lagrangian Relaxation for Natural Language Decoding", which he completed in 2014 at MIT.

We talk about his work in the thesis on decoding in NLP, how it connects with today, and many interesting topics along the way such as the role of engineering in machine learning, breadth vs. depth, and more.

- Episode notes: https://cs.nyu.edu/~welleck/episode34.html

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[34] Sasha Rush - Lagrangian Relaxation for Natural Language Decoding

Armando Solar-Lezama is a Professor at MIT, and the Associate Director & COO of CSAIL. He leads the Computer Assisted Programming Group, focused on program synthesis.

Armando’s PhD thesis is titled, "Program Synthesis by Sketching", which he completed in 2008 at UC Berkeley.

We talk about program synthesis & his work on Sketch, how machine learning's role in program synthesis has evolved over time, and more.

- Episode notes: https://cs.nyu.edu/~welleck/episode35.html

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[35] Armando Solar-Lezama - Program Synthesis by Sketching

Dieuwke Hupkes is a Research Scientist at Facebook AI Research and the scientific manager of the Amsterdam unit of ELLIS. 

Dieuwke's PhD thesis is titled, "Hierarchy and Interpretability in Neural Models of Language Processing", which she completed in 2020 at the University of Amsterdam.

We discuss her work on which aspects of hierarchical compositionality and syntactic structure can be learned by recurrent neural networks, how these models can serve as explanatory models of human language processing, what compositionality actually means, and a lot more.

- Episode notes: https://cs.nyu.edu/~welleck/episode36.html

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[36] Dieuwke Hupkes - Hierarchy and Interpretability in Neural Models of Language Processing

Joonkoo Park is an Associate Professor and Honors Faculty in the Department of Psychological and Brain Sciences at UMass Amherst. 

He leads the Cognitive and Developmental Neuroscience Lab, focusing on understanding the developmental mechanisms and neurocognitive underpinnings of our knowledge about number and mathematics.

Joonkoo’s PhD thesis is titled "Experiential Effects on the Neural Substrates of Visual Word and Number Processing", which he completed in 2011 at the University of Michigan.

We talk about numerical processing in the brain, starting with nature vs. nurture, including the learned versus built-in aspects of neural architectures. We talk about the difference between word and number processing, types of numerical thinking, and symbolic vs. non-symbolic numerical processing.

- Episode notes: https://cs.nyu.edu/~welleck/episode37.html

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[37] Joonkoo Park - Neural Substrates of Visual Word and Number Processing

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

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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

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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

Gus Xia is an assistant professor at New York University Shanghai. His research explores machine learning for music, with a goal of building intelligent systems that understand and extend musical creativity and expression.

His PhD thesis is titled Expressive Collaborative Music Performance via Machine Learning, which we discuss in depth along with his ongoing research at the NYU Shanghai Music X Lab.

- Gus Xia's homepage: https://www.cs.cmu.edu/~gxia/
- Thesis: http://reports-archive.adm.cs.cmu.edu/anon/ml2016/CMU-ML-16-103.pdf

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

[01] Gus Xia - Expressive Collaborative Music Performance via Machine Learning

Colin Raffel is currently a Senior Research Scientist at Google Brain, and soon to be an assistant professor at the University of North Carolina. His recent work focuses on transfer learning and learning from limited labels. 

His thesis is titled "Learning-Based Methods for Comparing Sequences, with Applications to Audio-to-MIDI Alignment and Matching", which we discuss along with the connections to his later work, and plans for the future.

Episode notes: https://cs.nyu.edu/~welleck/episode2.html

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[02] Colin Raffel - Learning-Based Methods for Comparing Sequences

Yoon Kim is currently a Research Scientist at the MIT-IBM AI Watson Lab, and will be joining MIT as an assistant professor in 2021.
Yoon’s research focuses on machine learning and natural language processing. 

His PhD thesis is titled "Deep Latent Variable Models of Natural Language", which he completed in 2020 at Harvard University. We discuss his work on uncovering latent structure in natural language, including continuous vector representations, tree structures, and grammars. We cover learning and variational inference methods that he developed during his PhD, and he offers a look at where latent variable models will be heading in the future.

Episode notes: https://cs.nyu.edu/~welleck/episode6.html

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[06] Yoon Kim - Deep Latent Variable Models of Natural Language

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

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

Chelsea Finn is an Assistant Professor at Stanford University, where she leads the IRIS lab that studies intelligence through robotic interaction at scale.

Her PhD thesis is titled "Learning to Learn with Gradients", which she completed in 2018 at UC Berkeley. Chelsea received the prestigious ACM Doctoral Dissertation Award for her work in the thesis. We discuss machine learning for robotics, focusing on learning-to-learn - also known as meta-learning - and her work on the MAML algorithm during her PhD, as well as the future of robotics research.

Episode notes: https://cs.nyu.edu/~welleck/episode10.html

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[10] Chelsea Finn - Learning to Learn with Gradients

Paul Middlebrooks is a neuroscientist and host of the Brain Inspired podcast, which explores the intersection of neuroscience and artificial intelligence.

Paul's PhD thesis is titled "Neuronal Correlates of Meta-Cognition in Primate Frontal Cortex", which he completed at the University of Pittsburgh in 2011. We discuss Paul's work on meta-cognition - informally, thinking about thinking - then discuss neuroscience for A.I. and A.I. for neuroscience. 

Episode notes: https://cs.nyu.edu/~welleck/episode17.html

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[17] Paul Middlebrooks - Neuronal Correlates of Meta-Cognition in Primate Frontal Cortex

Eero Simoncelli is a Professor of Neural Science, Mathematics, Data Science, and Psychology at New York University. His research focuses on representation and analysis of visual information.

Eero's PhD thesis is titled "Distributed Representation & Analysis of Visual Motion", which he completed in 1993 at MIT. We discuss his PhD work which focused on optical flow, which ideas and methods have stayed with him throughout his career, making biological connections with machine learning models, and how Eero's perspective of vision has evolved.

Episode notes: https://cs.nyu.edu/~welleck/episode18.html

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[18] Eero Simoncelli - Distributed Representation and Analysis of Visual Motion

Josef Urban is a Principal Researcher at the Czech Institute of Informatics, Robotics, and Cybernetics. His research focuses on artificial intelligence for large-scale computer-assisted reasoning.

Josef's PhD thesis is titled "Exploring and Combining Deductive and Inductive Reasoning in Large Libraries of Formalized Mathematics", which he completed in 2004 at Charles University in Prague. We discuss his PhD work on the Mizar Problems for Theorem Proving, machine learning for premise selection, and how it evolved into his recent research.

Episode notes: https://cs.nyu.edu/~welleck/episode20.html

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[20] Josef Urban - Deductive and Inductive Reasoning in Large Libraries of Formalized Mathematics

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.

The Thesis Review

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