Sameer Singh

Sameer Singh
4204 Donald Bren Hall
University of California
Irvine, CA 92697-3435

Sameer Singh is an Assistant Professor of Computer Science at the University of California, Irvine since Summer 2016. He is working on large-scale and interactive machine learning applied to information extraction and natural language processing. Before UCI, Sameer was a Postdoctoral Research Associate at the University of Washington, working with Carlos Guestrin, Luke Zettlemoyer, Dan Weld, and worked briefly with the late Ben Taskar. He received his PhD from the University of Massachusetts, Amherst in 2014 under the supervision of Andrew McCallum, during which he also interned at Microsoft Research, Google Research, and Yahoo! Labs. He was selected as a DARPA Riser, won the grand prize in the Yelp dataset challenge, has been awarded the Yahoo! Key Scientific Challenge (umass story, yahoo story) and the UMass Graduate School fellowships, and was a finalist for the Facebook PhD fellowship.

(obsolete) Resume/CV

External Links


Univ of California
Irvine CA
University of California, Irvine
Assistant Professor
2016 - current

Univ of Washington
Seattle WA
Postdoctoral Researcher
2013 - 2016


PhD (CS)
Univ of Massachusetts
Amherst MA

Vanderbilt University
Nashville TN

BEng (EE)
University of Delhi
New Delhi

High School
Sardar Patel Vidyalaya
New Delhi


Microsoft Research
Cambridge UK


Research Intern
Summer 2012

Google Research
Mountain View CA
Research Intern
Summer 2010

Yahoo! Labs
Sunnyvale CA
Research Intern
Summer 2009

Piitsburgh PA
Research Intern
Summer, Fall 2007

Selected Recent Publications see all...

  • M. Tulio Ribeiro, S. Singh, C. Guestrin. "Why Should I Trust You?": Explaining the Predictions of Any Classifier. Knowledge Discovery and Data Mining (KDD). 2016Conference
    Also presented at the CHI 2016 Workshop on Human-Centred Machine Learning (HCML).
    Coming Soon!
     author = {Marco Tulio Ribeiro and Sameer Singh and Carlos Guestrin},
     title = { "Why Should I Trust You?": Explaining the Predictions of Any Classifier },
     booktitle = {Knowledge Discovery and Data Mining (KDD)},
     year = {2016}
  • T. Rocktaschel, S. Singh, S. Riedel. Injecting Logical Background Knowledge into Embeddings for Relation Extraction. Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL). 2015Conference
    Matrix factorization approaches to relation extraction provide several attractive features: they support distant supervision, handle open schemas, and leverage unlabeled data. Unfortunately, these methods share a shortcoming with all other distantly supervised approaches: they cannot learn to extract target relations without existing data in the knowledge base, and likewise, these models are inaccurate for relations with sparse data. Rule-based extractors, on the other hand, can be easily extended to novel relations and improved for existing but inaccurate relations, through first-order formulae that capture auxiliary domain knowledge. However, usually a large set of such formulae is necessary to achieve generalization.
    In this paper, we introduce a paradigm for learning low-dimensional embeddings of entity-pairs and relations that combine the advantages of matrix factorization with first-order logic domain knowledge. We introduce simple approaches for estimating such embeddings, as well as a novel training algorithm to jointly optimize over factual and first-order logic information. Our results show that this method is able to learn accurate extractors with little or no distant supervision alignments, while at the same time generalizing to textual patterns that do not appear in the formulae.
     author = {Tim Rocktaschel and Sameer Singh and Sebastian Riedel},
     title = {Injecting Logical Background Knowledge into Embeddings for Relation Extraction},
     booktitle = {Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)},
     year = {2015}
  • T. Chen, S. Singh, B. Taskar, C. Guestrin. Efficient Second-Order Gradient Boosting for Conditional Random Fields. International Conference on Artificial Intelligence and Statistics (AISTATS). 2015Conference
    Coming Soon!
     author = {Tianqi Chen and Sameer Singh and Ben Taskar and Carlos Guestrin},
     title = {Efficient Second-Order Gradient Boosting for Conditional Random Fields},
     booktitle = {International Conference on Artificial Intelligence and Statistics (AISTATS)},
     year = {2015}
  • X. Ling, S. Singh, D. Weld. Design Challenges for Entity Linking. Transactions of the Association for Computational Linguistics (TACL). 2015Journal
    To be presented at ACL, Beijing, July 26-31, 2015.
    Recent research on entity linking (EL) has introduced a plethora of promising techniques, ranging from deep neural networks to joint inference. But despite numerous papers there is surprisingly little understanding of the state of the art in EL. We attack this confusion by analyzing differences between several versions of the EL problem and presenting a simple yet effective, modular, unsupervised system, called Vinculum, for entity linking. We conduct an extensive evaluation on nine data sets, comparing Vinculum with two state-of-the-art systems, and elucidate key aspects of the system that include mention extraction, candidate generation, entity type prediction, entity coreference, and coherence.
     author = {Xiao Ling and Sameer Singh and Dan Weld},
     title = {Design Challenges for Entity Linking},
     series = {Transactions of the Association for Computational Linguistics (TACL)},
     year = {2015}