Paul Liang, CMU

Paul Pu Liang

Email: paul.liangpu(at)gmail.com

[CV]

[google scholar]

[linkedin]

[github]

[twitter]

[instagram]

[Papers] [Teaching] [Research Group] [Talks]

I recently received my Ph.D. from the Machine Learning Department at Carnegie Mellon University, advised by Louis-Philippe Morency and Ruslan Salakhutdinov. I was also fortunate to collaborate with Manuel Blum, Lenore Blum, Faisal Mahmood, Jack Hessel, and Yejin Choi at Berkeley, Harvard Medical School, and UW/AI2. My research was generously supported by a Siebel Scholars Award, Waibel Presidential Fellowship, Facebook PhD Fellowship, and Center for Machine Learning and Health Fellowship, and has been recognized by 4 best paper/honorable mention awards at international conferences and workshops. I love teaching and was honored to receive the Alan J. Perlis Graduate Student Teaching Award for co-instructing courses on multimodal machine learning. Previously, I received an M.S. in Machine Learning and a B.S. with University Honors in Computer Science and Neural Computation from CMU.

Research opportunities: I am happy to collaborate and answer questions about my research and CMU academic programs. If you are interested, please send me an email. I especially encourage students from underrepresented groups to reach out.

News

2023: Excited to release some recent work formalizing and quantifying multimodal interactions from statistical (NeurIPS 2023) and human (ICMI 2023) perspectives, with applications in visualizing and interpreting multimodal models (ICLR 2023), contrastive learning of unique information (NeurIPS 2023), and guarantees for multimodal semi-supervised learning (arXiv 2023).
2023: Co-teaching 11-777 Multimodal Machine Learning, Fall 2023, course content will be updated on the website.
2023: Tutorials on multimodal machine learning at ICML 2023, ICMI 2023, CVPR 2022 and NAACL 2022, teaching at CIFAR DLRL summer school and African Masters of Machine Intelligence (day1, day2, day3, day4): check out our survey paper, slides, and videos.
2023: Nothing has excited me more than collaborating with and advising great students. I've learned so much from them and I'm excited to watch them embark on their new research agendas - follow their work for more exciting new ideas! Yun Cheng -> PhD at Princeton, Rulin Shao -> PhD at UW, Xiang Fan -> PhD at UW, Jivat Neet -> PhD at Berkeley, Yiwei Lyu -> PhD at Michigan, Yuxin Xiao -> PhD at MIT, Peter Wu -> PhD at Berkeley, Dong Won Lee -> PhD at MIT, Terrance Liu -> PhD at CMU.
2023: LP and I are teaching 2 new graduate seminar courses: 11-866 Artificial Social Intelligence and 11-877 Advanced Topics in Multimodal Machine Learning.
2022: Check out course content for 11-777 Multimodal Machine Learning, Fall 2022, where LP and I have completely revamped the course content. Also check out fully recorded lecture videos and course content for 11-777 in Fall 2020.
2022: Are you working on multimodal tasks and can't decide on a model? Check out HighMMT (TMLR 2022), our attempt at a single multimodal model that can predict sentiment, emotion, humor, disease, robot pose, and more, as well as MultiBench (NeurIPS 2021) and MultiZoo (JMLR 2022), a large-scale benchmark for multimodal learning spanning 15 datasets, 10 modalities, 20 prediction tasks, and 6 research areas.
2021: Extremely honored to have received a Facebook PhD Fellowship and a Center for Machine Learning and Health Fellowship to support my research in socially intelligent AI! For students applying for graduate fellowships, I uploaded my statement from the 2020 application.
2020: Check out the CMU Machine Learning Blog - new research and educational content every few weeks on ML research going on at CMU!

Selected Publications

(* denotes joint first-authors, see full list of publications here)

Foundations of multimodal machine learning:

Multimodal Learning Without Labeled Multimodal Data: Guarantees and Applications

[arXiv]

[code]

Quantifying & Modeling Multimodal Interactions: An Information Decomposition Framework

[arXiv]

[code]

Foundations and Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questions

[arXiv]

[tutorial website]

[tutorial videos]

Representation learning over multisensory and temporal data:

High-Modality Multimodal Transformer: Quantifying Modality & Interaction Heterogeneity for High-Modality Representation Learning

[arXiv]

[code]

MultiBench: Multiscale Benchmarks for Multimodal Representation Learning

Multimodal Transformer for Unaligned Multimodal Language Sequences

[arXiv]

[code]

Multimodal applications in social AI, health, and wellness:

Modeling Dense Multimodal Interactions Between Biological Pathways and Histology for Survival Prediction

[arXiv]

[code]

Learning Language and Multimodal Privacy-Preserving Markers of Mood from Mobile Data

(oral)

[arXiv]

Computational Modeling of Human Multimodal Language: The MOSEI Dataset and Interpretable Dynamic Fusion

(first runner-up award)

[paper]

[slides]

[poster]

Multimodal Sentiment Analysis with Word-level Fusion and Reinforcement Learning

(oral, honorable mention award)

[arXiv]

[code]

[slides]

Real-world societal concerns:

Towards Understanding and Mitigating Social Biases in Language Models

[arXiv]

[code]

Towards Debiasing Sentence Representations

[arXiv]

[code]

Think Locally, Act Globally: Federated Learning with Local and Global Representations

(oral, distinguished student paper award)

[arXiv]

[code]

Teaching

Co-Instructor: 11-877 Advanced Topics in Multimodal Machine Learning, Spring 2024, CMU with Daniel Fried
Co-Lecturer: 11-777 Multimodal Machine Learning, Fall 2023, CMU with Louis-Philippe Morency
Instructor: Multimodal Artificial Intelligence (day1, day2, day3, day4), African Masters Of Machine Intelligence, Summer 2023
Co-Instructor: Tutorials on Multimodal ML at ICML 2023, ICMI 2023, CVPR 2022 and NAACL 2022 with Louis-Philippe Morency
Co-Instructor: 11-866 Artificial Social Intelligence, Spring 2023, CMU with Louis-Philippe Morency
Co-Instructor: 11-877 Advanced Topics in Multimodal Machine Learning, Spring 2023, CMU with Louis-Philippe Morency
Co-Lecturer: 11-777 Multimodal Machine Learning, Fall 2022, CMU with Louis-Philippe Morency
Co-Instructor: 11-877 Advanced Topics in Multimodal Machine Learning, Spring 2022, CMU with Louis-Philippe Morency and Amir Zadeh
Guest Lecturer: 10-707 Deep Learning, 05-618 Human AI Interaction, 17-728 Machine Learning and Sensing, Peking University, University of Florida.
Lectures on multimodal machine learning [slides] [video]
Head TA & Lecturer: 11-777 Multimodal Machine Learning, Fall 2020, CMU. Instructor: Louis-Philippe Morency
4 lectures on multimodal tasks [slides] [video], deep generative models [slides] [video], reinforcement learning [slides] [video], and multimodal RL [slides] [video].
Public videos on YouTube have amassed more than 10000 views.
Head TA & Lecturer: 11-777 Multimodal Machine Learning, Fall 2019, CMU. Instructor: Louis-Philippe Morency
2 lectures on reinforcement learning [slides] and multimodal RL [slides]
TA: 10-708 Probabilistic Graphical Models, Spring 2019, CMU. Instructor: Eric Xing
TA: 10-715 Advanced Introduction to Machine Learning, Fall 2018, CMU. Instructor: Maria-Florina Balcan
TA: 10-601 Introduction to Machine Learning, Fall 2016, CMU. Instructor: Roni Rosenfeld
TA: 15-213/18-213/15-513 Introduction to Computer Systems, Summer 2016, CMU. Instructor: Brian Railing

Research Group

Some amazing students I've had the pleasure of advising:

Rohan Pandey, now at Reworkd AI (YC S23) (best senior thesis award)
Samuel Yu (CRA finalist)
Yun Cheng, now PhD student at Princeton
Rulin Shao, now PhD student at University of Washington
Xiang Fan, now PhD student at University of Washington (CRA honorable mention)
Jivat Neet, then research fellow at Microsoft Research, now PhD student at UC Berkeley
Yiwei Lyu, now PhD student at University of Michigan (CRA honorable mention)
Yuxin Xiao, now PhD student at MIT
Peter Wu, now PhD student at UC Berkeley
Dong Won Lee, now PhD student at MIT
Xiangru Tang, now PhD student at Yale
Terrance Liu, now PhD student at CMU
Seong Hyeon Park, now PhD student at KAIST
Chengfeng Mao, now PhD student at MIT
Ziyin Liu, then PhD student at University of Tokyo, now PostDoc at MIT
Irene Li, now at SoundHound (CRA honorable mention)

Academic Talks

The Future of Large Language Models: Multimodality and Safety

Foundations of Multimodal Machine Learning: Principles, Challenges, and Open Questions

[slides1]

[slides2]

Brainish: Formalizing A Multimodal Language for Intelligence and Consciousness

[slides]

Towards Real-World Social AI

[slides]

Think Locally, Act Globally: Federated Learning with Local and Global Representations

[slides]

Computational Modeling of Human Multimodal Language

[slides]

I have an Erdős number of 3 (Paul Erdős → Giuseppe Melfi → Erik Cambria → Paul Pu Liang).
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