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You enter a dark forest. Standing in front of you is:

A professor named Hal Daumé III (he/him). He wields appointments in Computer Science where he is a Volpi-Cupal Professor, as well as Language Science at UMD where he leads the TRAILS, the Institute for Trustworthy AI in Law & Society (in Spring 2024 he's teaching a gen-ed course You and I, and Generative AI; past: Trustworthy ML (F23), AI (S23), Human-AI Interaction (F22), Just ML (F21)); he is also a Senior Principal Researcher the machine learning and fairness groups at Microsoft Research NYC. He and his wonderful advisees like to study questions related to how to get machines to becomes more adept at human language (and artificial intelligence tasks more broadly), by developing models and algorithms that allow them to learn from data. (Keywords: natural language processing and machine learning.) The two major questions that really drive their research these days are:

    (1) how can we get computers to learn
        through natural interaction with people/users?

and (2) how can we do this in a way that minimize harms
        in the learned models?

He's discussed interactive learning informally in a Talking Machines Podcast and more technically in recent talks; and has discussed fairness/bias in broad terms in a (now somewhat outdated) blog post. He is the author of the online textbook A Course in Machine Learning, which is fully open source.

Hal is super fortunate to be a member of, and have awesome colleagues in the Computional Linguistics and Information Processing Lab (which he formerly directed), the Human-Computer Interaction Lab, and the Center for Machine Learning. If you want to contact him, email is your best bet; you can also find him on @haldaume3 on Twitter. Or, in person, in his office (IRB 4134).

If you're a prospective grad student or grad applicant, please read his FAQ to answer some common questions. If you're thinking of inviting him for a talk or event, please ensure that the event is organized in an inclusive manner (inclusion rider). More generally, if you are organizing a conference, workshop or other event, you may wish to read the NeurIPS D&I survey results (joint with Katherine Heller), Humberto Corona's collection of resources/advice, or two blog posts on this topic.

I acknowledge that I live and work on the ancestral and unceded lands of the Piscataway People, who were among the first in the Western Hemisphere to encounter European colonists, as well as the lands of the Lenape and Nacotchtank people.

Recent Publications:

Large Language Models Help Humans Verify Truthfulness -- Except When They Are Convincingly Wrong
Chenglei Si, Navita Goyal, Sherry Tongshuang Wu, Chen Zhao, Shi Feng, Hal Daumé III and Jordan Boyd-Graber
NAACL, 2024
[Abstract] [BibTeX]

How do Authors' Perceptions of their Papers Compare with Co-authors' Perceptions and Peer-review Decisions?
Charvi Rastogi, Ivan Stelmakh, Alina Beygelzimer, Yann N. Dauphin, Percy Liang, Jennifer Wortman Vaughan, Zhenyu Xue, Hal Daumé III, Emma Pierson and Nihar B. Shah
PLOS One, 2024
[Abstract] [BibTeX]

A Randomized Controlled Trial on Anonymizing Reviewers to Each Other in Peer Review Discussions
Charvi Rastogi, Xiangchen Song, Zhijing Jin, Ivan Stelmakh, Hal Daumé III, Kun Zhang and Nihar B. Shah
PLOS One, 2024
[Abstract] [BibTeX]

Multilingual large language models leak human stereotypes across language boundaries
Yang Trista Cao, Anna Sotnikova, Jieyu Zhao, Linda X. Zou, Rachel Rudinger and Hal Daumé III
Preprint, 2024
[Abstract] [BibTeX]

Premier-TACO is a Few-Shot Policy Learner: Pretraining Multitask Representation via Temporal Action-Driven Contrastive Loss
Ruijie Zheng, Yongyuan Liang, Xiyao Wang, Shuang Ma, Hal Daumé III, Huazhe Xu, John Langford, Praveen Palanisamy, Kalyan Shankar Basu and Furong Huang
International Conference on Machine Learning (ICML), 2024
[Abstract] [BibTeX]

More papers please!

Recent Talks:

AI UK: Doing better in data science – from algorithmic fairness to diversity
Anjali Mazumder, Shakir Mohamed, Danielle Belgrave, Maria De-Arteaga, and Hal Daumé III
The Alan Turing Institute AI UK Roadmap, March 2021

Coded Bias Panel Discussion at the University of Maryland
Margrét Bjarnadóttir, Nicol Turner Lee, Deborah Raji, Adam Wenchel, and Hal Daumé III (moderator)
March, 2021

Responsible AI Systems and Experiences
Abolfazl Asudeh (moderator), Hal Daumé III, Golnoosh Farnadi, Bernease Herman, Bill Howe (moderator), Yuval Moskovitch, Katie Shilton, and Jenn Wortman Vaughan
Panel at VLDB 2021

Tech Ethics in a Changing World
Catherine Bannister, Mary Lacity, Cindy Moehring, and Hal Daumé III
Northwest Arkansas Tech Summit, 2021

Language (Technology) Is Power: Exploring the Inherent Complexity of NLP Systems
Hal Daumé III and Sam Charrington (host)
TWIML AI Podcast, 2020

More talks please!

Contact information:
    email: me AT hal3 DOT name               skype: haldaume3
    phone: 301-405-1073                    twitter: haldaume3
   office: IRB 4150                         github: hal3
I can't reply to all prospective students email; please read this before emailing me.

credits: design and font inspired by Seth Able's LoRD, some images converted to ANSI using ManyTools, original drawing of me by anonymous.
last updated on twelve may, two thousand twenty four.