You enter a dark forest
. Standing in front of you is:
A professor named Hal Daumé III.
He wields appointments in
Computer Science and
Language Science at
UMD; he is currently on leave from UMD,
spending time in the machine learning and fairness
groups at Microsoft Research NYC.
He and his wonderful advisees
questions related to how to get machines to becomes more adept at
human language, by developing models and algorithms that allow them
to learn from data. (Keywords: natural language processing and machine
The two major questions that really drive their research these days are:
(1) how can we get computers to learn language
through natural interaction with people/users?
and (2) how can we do this in a way that promotes fairness,
transparency and explainability in the learned models?
He's discussed interactive learning informally recently in a Talking Machines Podcost
and more technically in recent talks;
and has discussed fairness/bias in broad terms in a recent blog post.
Hal is committed to promoting an inclusive
scientific environment; if you are thinking of inviting him for a talk
or to participate in an event, please ensure that the event is
consistent with this goal (see the first question on the FAQ).
Hal is super fortunate to have awesome colleagues in the Computional
Linguistics and Information Processing Lab (which he currently
He maintains the structured
prediction framework in VW.
If you want to contact him, email is your best bet; you can
also find him on @haldaume3
on Twitter. Or, in person, in the CLIP lab
(AVW 3126) or his office
If you're a prospective grad student or grad applicant,
please read his FAQ!
Improving fairness in machine learning systems: What do industry practitioners need?
Kenneth Holstein, Jennifer Wortman Vaughan, Hal Daumé III, Miroslav Dudík and Hanna Wallach
Non-Monotonic Sequential Text Generation
Sean Welleck, Kianté Brantley, Hal Daumé III and Kyunghyun Cho
Meta-Learning for Contextual Bandit Exploration
Amr Sharaf and Hal Daumé III
Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback
Chicheng Zhang, Alekh Agarwal, Hal Daumé III, John Langford and Sahand N Negahban
Hierarchical Imitation and Reinforcement Learning
Hoang M Le, Nan Jiang, Alekh Agarwal, Miroslav Dudík, Yisong Yue and Hal Daumé III
More papers please!
Learning language through interaction
December 2016, Georgetown, Amazon, USC, GATech
Bias in AI
November 2016, UMD MCWIC Diversity Summit
Locally optimal learning to search and distant supervision
December 2015, UMD CS Research Seminar
Imitation learning and recurrent neural networks mashup
December 2015, CIFAR NCAP Workshop
Algorithms that learn to think of their feet
October 2015, UC Santa Cruz
More talks please!
email: me AT hal3 DOT name skype: haldaume3
phone: 301-405-1073 twitter: haldaume3
office: AVW 3227 github: hal3
I can't reply to all prospective students
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 twenty february, two thousand nineteen.