Ni Lao
(劳逆)
Office:
5507 Gates Hillman Complex
Carnegie Mellon University
5000 Forbes Avenue, Pittsburgh, PA 15213
Mail: nlao[ at ]cs[ .]cmu[ .]edu
I am a PhD candidate in Language Technologies Institute, School of Computer Science at Carnegie Mellon University. My advisor is professor William W. Cohen. My research interests are Machine Learning, Information Retrieval, and Natural Language Processing.
I am generally interested in models with rich structures, and am currently studying relational models for information retrieval and extraction applications. Previously I have studied a wide range of topics such as robotic soccer, computer system diagnosis, product search, and question answering.
My CV, research statement, and thesis proposal
Fun with math and programming.
Publications
Ni Lao, Tom Mitchell, William W. Cohen, Random Walk Inference and Learning in A Large Scale Knowledge Base. EMNLP, 2011 slides poster
Jun Zhu, Ni Lao, Ning Chen, Eric P. Xing Conditional Topical Coding: an Efficient Topic Model Conditioned on Rich Features. KDD, 2011
Ni Lao, William W. Cohen, Relational retrieval using a combination of path-constrained random walks Machine Learning, 2010, Volume 81, Number 1, Pages 53-67 (ECML, 2010 slides poster )
Ni Lao, Jun Zhu, Liu Liu, Yandong Liu, William W. Cohen, Efficient Relational Learning with Hidden Variable Detection. NIPS, 2010 poster
Ni Lao, William W. Cohen, Fast Query Execution for Retrieval Models based on Path Constrained Random Walks. KDD, 2010
Jun Zhu, Ni Lao, E. P. Xing, Grafting-Light: Fast, Incremental Feature Selection and Structure Learning of Markov Random Fields. KDD, 2010
Lao, Ni, Hideki Shima, Teruko Mitamura and Eric Nyberg. 2008. Query Expansion and Machine Translation for Robust Cross-Lingual Information Retrieval , in Proceedings of NTCIR-7 Workshop, Japan.
Shima, Hideki, Ni Lao, Eric Nyberg and Teruko Mitamura. 2008. Complex Cross-lingual Question Answering as Sequential Classification and Multi-Document Summarization Task , in Proceedings of NTCIR-7 Workshop, Japan.
W. Zuo, N. Lao, Y. Geng, and K. Ma. 2008. GeoSVM: an efficient and effective tool to predict species' potential distributions. Journal of Plant Ecology, 1(2): 143-145.
Yiming Yang,Abhimanyu Lad, Ni Lao, Abhay Harpale, Bryan Kisiel, Monica Rogati, Utility-based information distillation over temporally sequenced documents, SIGIR, pp. 31-38, 2007.
Chun Yuan; Ni Lao; Ji-Rong Wen; Jiwei Li; Zheng Zhang; Yi-Min Wang; Wei-Ying Ma, Automated Known Problem Diagnosis with Event Traces, EuroSys, 2006.
Ni Lao, Ji-Rong Wen, Wei-Ying Ma, Yi-Min Wang, Combine High Level Symptom and Low Level State Information for Configuration Fault Diagnosis, LISA, 2004.
Ji-Rong Wen, Ni Lao, Wei-Ying Ma, Probabilistic Model for Contextual Retrieval, SIGIR, 2004.
Archana Ganapathi, Yi-Min Wang, Ni Lao, Ji-Ron g Wen, Why PCs Are Fragile and What We Can Do About It: A Study of Windows Registry Problems, Dependable System and Network (DSN), 2004.
Jinyi Yao, Lao Ni, Fan Yang, Yunpeng Cai, Zengqi Sun, Technical Solutions of TsinghuAeolus for Robotic Soccer. Robocup 2003: 205-213,RoboCup, pp. 205-213, 2003
Unpublished Manuscript and Presentations
Ni Lao, Beyond Shallow Semantics. 2011.
Ni Lao, CCG, Fractal, and Emergence. 2011.
Ni Lao, Split-Emit Process for Natural Language Generation. 2009.
Ni Lao, T. Mitamura, E. Nyberg, Tree Representations for Chinese Semantic Role Labeling. 2009.
Ni Lao, Read The Web (presentation) . 2007.
Ni Lao, Schema Extraction Model . 2007.
Thesis
Master thesis, 2006. Data Mining Problems in Automatic Computer Diagnosis.Tsinghua University
Bachelor thesis, 2003. Mining Spatial-Temporal Data Using Constructive Induction. Tsinghua University
Code
2011, Path Ranking Algorithm A system for relational retrieval on heterogamous graphs
2006, geoSVM A predictive system for modeling species potential distributions based on SVM. See details at Wenyun's page
Data Sets
2010, yeast2 updated yeast data with extra information about Mesh heading, chemicals and affiliations etc. (321K entities and 6.1M links)
2010, fly a biological literature graph with 770K entities and 3.5M links
2010, yeast a biological literature graph with 164K entities and 2.8M links