Paul Liang, CMU

Paul Pu Liang

Email: pliang(at)cs.cmu.edu
Office: Gates and Hillman Center 8011
5000 Forbes Avenue, Pittsburgh, PA 15213
Machine Learning Department and Language Technologies Institute, School of Computer Science, Carnegie Mellon University

[CV]

[google scholar]

[linkedin]

[github]

[twitter]

[instagram]

[Publications] [Honors] [Teaching] [Talks] [Activities]

I am a final-year Ph.D. student in the Machine Learning Department at Carnegie Mellon University, advised by Louis-Philippe Morency and Ruslan Salakhutdinov. I also collaborate with Manuel Blum, Lenore Blum, Jack Hessel, and Yejin Choi at Berkeley and UW/AI2.

I am on the academic job market this year. [CV] [research statement] [teaching statement] [DEI statement]

My research lies in the foundations of multimodal machine learning with applications in socially intelligent AI, natural language processing, healthcare, and education. As steps towards this goal, I work on:

Foundations of multimodal machine learning: modality heterogeneity, connections, and interactions [foundations, interactions, disagreement, FactorCL, MultiViz, factorized, translation].

Representation learning and foundation models for multisensory and temporal data [HighMMT, MultiBench, T2FN, RMFN, MulT].

Multimodal applications in socially intelligent AI, NLP, healthcare, education, and robotics [CMU-MOSEI, Social-IQ, mental health, pathology, education].

Real-world fair, robust, interpretable, and efficient representation learning [fairness in LMs, fairness in BERT, federated learning, efficiency].

My research is 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 3 best paper/honorable mention awards at NeurIPS workshops and ICMI 2017. I love teaching and advising, and was honored to receive the Alan J. Perlis Graduate Student Teaching Award for co-instructing courses (CMU 11-877, CMU 11-866, CMU 11-777), and organizing workshops (ICCV, NAACL, NeurIPS, ACL) and tutorials (ICML, CVPR, NAACL) 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, where I am grateful for the mentorship of Louis-Philippe Morency, Ruslan Salakhutdinov, Tai Sing Lee, Roni Rosenfeld, and Ryan Tibshirani. I have also been fortunate to spend time at DeepMind, Facebook AI Research, Nvidia AI, Google Research, and RIKEN Artificial Intelligence Project.

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!

Education

Ph.D. in Machine Learning, Carnegie Mellon University. 2018 - Present

Louis-Philippe Morency

M.S. in Machine Learning, Carnegie Mellon University. 2017 - 2018

Louis-Philippe Morency

Ruslan Salakhutdinov

B.S. in Computer Science and Neural Computation, Carnegie Mellon University. 2014 - 2017

Roni Rosenfeld

Ryan Tibshirani

Tai Sing Lee

Selected Publications

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

Multimodal Learning Without Labeled Multimodal Data: Guarantees and Applications

[arXiv]

[code]

Quantifying & Modeling Multimodal Interactions: An Information Decomposition Framework

[arXiv]

[code]

Factorized Contrastive Learning: Going Beyond Multi-view Redundancy

[arXiv]

[code]

Multimodal Fusion Interactions: A Study of Human and Automatic Quantification

[arXiv]

[code]

MultiViz: Towards Visualizing and Understanding Multimodal Models

[arXiv]

[code]

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

[arXiv]

[tutorial website]

[tutorial videos]

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

[arXiv]

[code]

MultiZoo & MultiBench: A Standardized Toolkit for Multimodal Deep Learning

[arXiv]

[website]

[code]

Brainish: Formalizing A Multimodal Language for Intelligence and Consciousness

(oral)

[arXiv]

MultiBench: Multiscale Benchmarks for Multimodal Representation Learning

[arXiv]

[website]

[code]

Towards Understanding and Mitigating Social Biases in Language Models

[arXiv]

[code]

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

(oral)

[arXiv]

Cross-Modal Generalization: Learning in Low Resource Modalities via Meta-Alignment

(oral)

[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]

Learning Representations from Imperfect Time Series Data via Tensor Rank Regularization

[arXiv]

[poster]

Learning Factorized Multimodal Representations

[arXiv]

[code]

[poster]

Found in Translation: Learning Robust Joint Representations by Cyclic Translations Between Modalities

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

(first runner-up award)

[paper]

[slides]

[poster]

Multimodal Language Analysis with Recurrent Multistage Fusion

(oral)

[arXiv]

[slides]

[poster]

Multimodal Sentiment Analysis with Word-level Fusion and Reinforcement Learning

(oral, honorable mention award)

[arXiv]

[code]

[slides]

Honors

Siebel Scholars Award: 2023
Waibel Presidential Fellowship: 2023-2024
Alan J. Perlis Graduate Student Teaching Award: 2023
Young Researcher Invitation to the Heidelberg Laureate Forum: 2022
Facebook PhD Fellowship: 2021-2023
Center for Machine Learning and Health Fellowship: 2021-2022
MIT Fintech Innovation Prize Grant and Sandbox Innovation Program Funding: 2021
CVPR 2020 Argoverse Competition Honorable Mention: 2020
NeurIPS 2019 Workshop on Federated Learning Distinguished Student Paper: 2019
CMU Machine Learning Department Teaching Assistant Award: 2019
Princeton University Gordon Wu Fellowship (declined): 2018
NSF Graduate Research Fellowship Honorable Mention: 2018
CMU Machine Learning Department Data Analysis Project First Runner-up: 2018
ICMI 2017 Best Paper Honorable Mention: 2017
CMU School of Computer Science Dean's List and University Honors: 2014-2017

Teaching

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, 2021-2023, CMU.
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

Student Advising

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, now PhD student at University of Tokyo
Irene Li, now at SoundHound (CRA Honorable Mention)

Academic Talks

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

[slides1]

[slides2]

Brainish: Formalizing A Multimodal Language for Intelligence and Consciousness

Introduction to Multimodal Machine Learning

[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]

Professional Activities

Co-organizer: Tutorial on Multimodal Machine Learning at ICML 2023, ICMI 2023, CVPR 2022, NAACL 2022
Co-organizer: Workshop on Machine Learning for Cognitive and Mental Health at AAAI 2024
Co-organizer: Artificial Social Intelligence Workshop and Challenge at ICCV 2023
Co-organizer: Workshop on Foundation Models in Vision and Language at ICDM 2022
Co-organizer: Workshop on Multimodal Artificial Intelligence at NAACL 2022, NAACL 2021
Co-organizer: Workshop on Tensor Networks in Machine Learning at NeurIPS 2020
Co-organizer: Grand Challenge and Workshop on Human Multimodal Language at ACL 2020, ACL 2018
Workflow Chair: ICML 2019
Session Chair: AAAI 2019
Conference Program Committee: NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL, EACL, COLING, IJCNLP, AACL, ACL Rolling Review, CVPR, ICCV, ECCV, WACV, ACCV, AAAI, IJCAI, AISTATS, UAI, CHI, ICMI, FG, ACML, ML4H, CHIL, ACM Multimedia, ACM Multimedia Asia, Interspeech
Workshop Program Committee: NeurIPS workshop on Meta-Learning, NeurIPS workshop on Machine Learning for Health, ICLR workshop on Embodied Multimodal Learning, ICLR workshop on Never-ending RL, ICLR workshop on Enormous Language Models, ACL workshop on Multimodal Language, EMNLP workshop on NLP Open Source Software, EMNLP workshop on NLP Beyond Text, NAACL workshop on Trustworthy NLP, NAACL workshop on Multimodal AI, IJCAI workshop on Federated Learning, WWW workshop on NLP Beyond Text, ICRA workshop on Social Intelligence in Humans and Robots, NeurIPS Workshop on Human Evaluation of Generative Models, NeurIPS Workshop on Self-Supervised Learning, EMNLP Workshop on Generation, Evaluation & Metrics
Journal Reviewer: IEEE Transactions on Affective Computing, IEEE Transactions on Audio, Speech and Language Processing, IEEE Transactions on Multimedia, IEEE Transactions on Cybernetics, IEEE Computational Intelligence Magazine, IEEE Signal Processing Letters, Elsevier Information Fusion, Elsevier Computer Speech and Language, Machine Learning, Transactions on Machine Learning Research, Neural Networks, Journal of Artificial Intelligence Research, Journal of Machine Learning Research, Medical Image Analysis
CMU Machine Learning Blog Editorial Board: 2019, 2020, 2021, 2022 (chief editor)
CMU AI Undergraduate Research Mentor: 2018, 2019, 2020, 2021
CMU Graduate Applicant Support Program Organizer: 2020
CMU Machine Learning Department PhD Admissions Committee: 2018, 2019, 2020, 2021, 2022
CMU Machine Learning Department Masters Admissions Committee: 2017, 2018
CMU Singapore Students Association Co-President: 2015

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