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Dimitris Papailiopoulos
Jay & Cynthia Ihlenfeld Associate Professor University of Wisconsin-Madison
Electrical and Computer Engineering
I am the Jay & Cynthia Ihlenfeld Associate Professor of Electrical and Computer Engineering (and CS by courtesy) at the University of Wisconsin-Madison, a faculty fellow at the Grainger Institute, and a faculty affiliate with the Optimization group at the Wisconsin Institute for Discovery.
My research lies in the intersection of machine learning, coding theory, and optimization. I am particularly interested in the theory and practice of large-scale machine learning systems and the challenges that arise once we aim to build solutions that come with robustness and scalability guarantees. I am particuarly interested in these topics in the context of large language models and transformer architectures.
Before coming to Madison, I spent two wonderful years as a postdoc at UC Berkeley, where I was a member of the AMPLab and BLISS, and had the pleasure to collaborate with
Ben Recht and Kannan Ramchandran.
I received my Ph.D. in 2014 from UT Austin, where I was fortunate to be advised by
Alex Dimakis. Before UT, I spent 3.5 years as a grad student at USC.
Before all that, I received my M.Sc. (2009) and ECE Diploma (2007) from the Technical University of Crete (TUC), located in the beautiful city of Chania.
In 2018, I co-founded the conference on Machine Learning & Systems (MLSys), a new conference that targets research at the intersection of systems and machine learning. In 2018 and 2020, I was the program co-chair for MLSys. In 2019, I also co-chaired the 3rd Midwest Machine Learning Symposium (MMLS).
Most updated list available at Google Scholar
A selected list of recent papers that I'm particularly fond of:
Dimitris Papailiopoulos
Jay & Cynthia Ihlenfeld Associate Professor University of Wisconsin-Madison
Electrical and Computer Engineering
I am currently on leave at Microsoft Research.
I am currently on leave at Microsoft Research.
I am the Jay & Cynthia Ihlenfeld Associate Professor of Electrical and Computer Engineering (and CS by courtesy) at the University of Wisconsin-Madison, a faculty fellow at the Grainger Institute, and a faculty affiliate with the Optimization group at the Wisconsin Institute for Discovery.
My research lies in the intersection of machine learning, coding theory, and optimization. I am particularly interested in the theory and practice of large-scale machine learning systems and the challenges that arise once we aim to build solutions that come with robustness and scalability guarantees. I am particuarly interested in these topics in the context of large language models and transformer architectures.
Before coming to Madison, I spent two wonderful years as a postdoc at UC Berkeley, where I was a member of the AMPLab and BLISS, and had the pleasure to collaborate with
Ben Recht and Kannan Ramchandran.
I received my Ph.D. in 2014 from UT Austin, where I was fortunate to be advised by
Alex Dimakis. Before UT, I spent 3.5 years as a grad student at USC.
Before all that, I received my M.Sc. (2009) and ECE Diploma (2007) from the Technical University of Crete (TUC), located in the beautiful city of Chania.
In 2018, I co-founded the conference on Machine Learning & Systems (MLSys), a new conference that targets research at the intersection of systems and machine learning. In 2018 and 2020, I was the program co-chair for MLSys. In 2019, I also co-chaired the 3rd Midwest Machine Learning Symposium (MMLS).
gScholar
bio,
resume
twitter
Publications
Most updated list available at Google Scholar
A selected list of recent papers that I'm particularly fond of:
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Teaching Arithmetic to Small TransformersN Lee, K Sreenivasan, JD Lee, K Lee, D PapailiopoulosICLR 2024 [arxiv]
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Looped Transformers are Better at Learning Learning AlgorithmsL Yang, K Lee, RD Nowak, D PapailiopoulosICLR 2024 [arxiv]
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Looped Transformers as Programmable ComputersAngeliki Giannou, Shashank Rajput, Jy-yong Sohn, Kangwook Lee, Jason D Lee, Dimitris PapailiopoulosICML 2023 [arxiv]
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Transformers as Algorithms: Generalization and Stability in In-context LearningYingcong Li, M Emrullah Ildiz, Dimitris Papailiopoulos, Samet OymakICML 2023 [arxiv]
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LIFT: Language-Interfaced Fine-Tuning for Non-Language Machine Learning TasksTuan Dinh, Yuchen Zeng, Ruisu Zhang, Ziqian Lin, Shashank Rajput, Michael Gira, Jy-yong Sohn, Dimitris Papailiopoulos, Kangwook LeeNeurIPS 2022 [arxiv]
Research Group
I am very fortunate to work with and learn from the following amazing colleagues
Members
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CS Ph.D (2020-), co-advised with Rob Nowak and Kangwook Lee
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ECE Ph.D (2020-), co-advised with Kangwook Lee
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CS Ph.D (2021-)
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CS Ph.D (2022-)
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ECE Ph.D (2023-)
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CS MS (2023-)
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ECE MS (2023-)
Alumni
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CS Ph.D (2019-2024), co-advised with Shivaram VenkataramanPostdoc, UT-Austin
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CS Ph.D (2020-2024)Research Scientist, Mosaic AI/Databricks
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CS Ph.D. (2018-2023)Research Scientist, Mosaic AI/Databricks
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ECE M.Sc. (2020-2023)
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Postdoc (2020-2022)Assistant Professor, Yonsei University, Statistics
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CS Ph.D (2016-2021)Assistant Professor, Rutgers, CS
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ECE Postdoc (2017-2019)Research Scientist, Google
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ECE M.Sc. (2019-2022)ECE Ph.D., UT Austin
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CS M.Sc. (2016-2019)CS Ph.D., UMontreal
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ECE M.Sc. (2018)ML Engineer, Facebook
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undegraduate researcher (2018)
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Visiting undegraduate researcher (2016)EECS Ph.D., MIT
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Undegraduate researcher (2014-2016)CS Ph.D, Harvard
Teaching
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Fall 2017, Fall 2018
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Fall 2019, Fall 2020, Fall 2021, Fall 2022
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Spring 2023
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Spring 2021
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Spring 2022
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Fall 2016, Spring 2018
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Spring 2020
Dimitris Papailiopoulos