about me Hal Daumé III
about me cv + bio publications research teaching students software photos calendar contact me links		I am Hal Daume III, an Assistant Professor in Computer Science at the U of Utah (CV). My research is in machine learning (primarily Bayesian methods and structured prediction), natural language processing and computational biology. More thoughts on these fields are on my blog. Recent publications: Unsupervised POS without Lexicons [AT,HD] (NIPS09 Wksp) Fast Search for IBP [PR,HD] (NIPS09 Wksp) Multi-Label Prediction via Infinite CCA [PR,HD] (NIPS09) Markov Random Topic Fields [HD] (ACL 2009) Semi-supervised or Semi-unsupervised? [HD] (SSLNLP 2009) Bayesian Hierarchical Multitask Learning [HD] (UAI 2009) Unsupervised Search-based Structured Pred [HD] (ICML 2009) Exponential Family Hybrid Learning [AA,HD] (IJCAI09) One-pass SVMs [PR,HD,SV] (IJCAI09) Bayesian Areal Linguistics [HD] (NAACL09) Streaming NLP: Language Modeling [AG,HD,SV] (NAACL09) Search-based Structured Prediction [HD,JL,DM] (MLJ09) Bayesian Coarse Graining [PL,QS,HD,GV] (J Chem Phys,09) Perceptron-based Coherence Prediction [DG,HD,JC] (ICSA08) Infinite Hierarchical Factor Regression [PR,HD] (NIPS08) Cross-Task Self Training [HD] (EMNLP08) Trading Structure for Features [PL,HD,DK] (ICML08) Name Transliteration in SMT [UH,KK,HD] (ACL08) Office: MEB 3126 Phone: 801-585-3586 Recent software: HBC to simulate and compile Bayesian models (updated 03 Apr 2008) WhatToSee to find research papers relevant to your interests (updated 09 May 2007) MegaM for training maximum entropy and perceptron models (updated 08 Oct 07) DPSearch for fast DP inference (updated 20 Dec 06) TagChunk for POS tagging, shallow parsing and NER (updated 11 Oct 08) FastDT for training boosted and bagged decision trees (updated 05 Sep 06) Recent teaching: Topics in ML: TBD     CS 7941     (Fall   2009) Machine Learning     CS 5350/6350     (Fall   2009) Topics in ML: Recent results     CS 7941     (Summer  2009) Artificial Intelligence     CS 5300/6300     (Spring  2009) Topics in ML: Learning Theory     CS 7941     (Spring  2009) Machine Learning     CS 5350/6350     (Fall   2008) Topics in ML: Graphical Models     CS 7941     (Fall   2008) Topics in ML: Multitask Learning     CS 7941     (Summer  2008) Machine Learning     CS 5350/6350     (Spring  2008) Topics in ML: Kernel Methods     CS 7941     (Spring  2008) Applications of Natural Language Processing     CS 5964/6964     (Fall   2007) Topics in ML: Manifold Learning     CS 7941     (Summer  2008) Machine Learning     CS 5350/6350     (Spring  2007) Students: Prospective students: Read this and email me after taking machine learning and/or NLP about potential research. Current students: Arvind Agarwal (PhD), Scott Alfeld (BS), Amit Goyal (PhD), Piyush Rai (PhD). Upcoming conferences: (bold = plan to attend): Location Due Date Notification Conference Dates NIPS 09 Vancouver Past Past 08-13 Dec 08 PSB 10 Hawaii Past Past 04-08 Jan 10 SODA 10 Austin Past ?? Sep 09 17-19 Jan 10 AISTATS 10 Sardinia 06 Nov 09 03 Feb 10 13-15 May 10 RECOMB 10 Lisbon 05 Oct 09 08 Dec 09 25-28 Apr 10 NAACL-HLT 10 Los Angeles 01 Dec 09? TBA 01-06 Jun 10 CVPR 10 San Francisco 19 Nov 09 24 Feb 10 13-18 Jun 10 ICML 10 Haifa 01 Feb 10? 13 Apr 10? 21-24 Jun 10 COLT 10 Haifa TBA TBA 25-29 Jun 10 ISMB 10 Boston 08 Jan 10 02 Mar 10 11-13 Jul 10 AAAI 10 Atlanta 15 Jan 10 26 Mar 10 11-15 Jul 10 ACL 10 Uppsala 15 Feb 10? TBA 11-16 Jul 10 CONLL 10 TBA TBA TBA 17-18 Jul 10 COLING 10 Beijing 19 Apr 10 28 May 10 23-27 Aug 10 SIGIR 10 Geneva TBA TBA TBA KDD 10 TBA TBA TBA TBA EMNLP 10 TBA TBA TBA TBA UAI 10 TBA TBA TBA TBA
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last updated on eight november, two thousand nine; contact