Jay Pujara's WebpageJay Pujara Ph.D. Candidate Computer Science Department University of Maryland, College Park Contact Information: E-Mail:
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About MeI'm Jay and I'm a Postdoctoral Researcher in the Jack Baskin School of Engineering at the University of California, Santa Cruz. Currently, I'm doing research in the field of machine learning with Lise Getoor as part of the D3 Center and the LINQS group. I received my PhD at the University of Maryland, College Park, where my dissertation focused on Scalable Knowledge Graph Construction. Prior to my doctorate, I worked on spam detection at Yahoo! in Sunnyvale, CA. I completed my undergraduate education as well as a research Masters program at Carnegie Mellon University, where my thesis explored machine learning for fMRI analysis. | |||||||||||||||||||||||||||||||||||||||
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Research InterestsMy research focuses on scalable machine learning to address scenarios where billions of predictions are necessary in a limited amount of time and large, noisy corpora of training data are available. One application that embodies these challenges and I find compelling is constructing a knowledge graph from the unstructured text, images, video and audio found on the World Wide Web. | |||||||||||||||||||||||||||||||||||||||
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EducationUniversity of California, Santa Cruz, 2016-
University of Maryland, College Park, 2010-2016
University of California, Santa Cruz, 2014-2015
Carnegie Mellon University, 2014
Carnegie Mellon University, 2004-2005
Carnegie Mellon University, 2000-2004
Carnegie Mellon University, 2001-2004
Carnegie Mellon University, 2001-2004
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PublicationsSee also: LINQS Publications and Google ScholarJournals and MagazinesUsing Semantics & Statistics to Turn Data into Knowledge. Jay Pujara, Hui Miao, Lise Getoor, William W. Cohen. AI Magazine 36.1 (2015). (pdf, bibtex) Refereed ConferencesBudgeted Online Collective Inference. Jay Pujara, Ben London, and Lise Getoor. 2015 Conference on Uncertainty in Artificial Intelligence (UAI). (pdf, bibtex, github) RELLY: Inferring Hypernym Relationships Between Relational Phrases. Adam Grycner, Gerhard Weikum, Jay Pujara, James Foulds, and Lise Getoor. 2015 Conference on Emperical Methods in Natural Language Processing. (pdf, bibtex) Knowledge Graph Identification. Jay Pujara, Hui Miao, Lise Getoor, William Cohen. 2013 International Semantic Web Conference (ISWC). [winner of Best Student Paper award] (pdf, bibtex, github, video, slides) Using Classifier Cascades for Scalable E-Mail Classification. Jay Pujara, Hal Daume III, and Lise Getoor. CEAS 2011. [winner of Best Paper award] (pdf, bibtex, slides) Refereed Workshops and SymposiaOnline Inference for Knowledge Graph Construction. Jay Pujara, Ben London, Lise Getoor, and William W. Cohen. UAI 2015 Workshop on Statistical Relational AI (StaRAI). (pdf) Building Dynamic Knowledge Graphs. Jay Pujara and Lise Getoor. NIPS 2014 Workshop on Automated Knowledge Base Construction (AKBC). (pdf, bibtex) A Unified Probabilistic Approach for Semantic Clustering of Relational Phrases. Adam Grycner, Gerhard Weikum, Jay Pujara, James Foulds, Lise Getoor. NIPS 2014 Workshop on Automated Knowledge Base Construction (AKBC). (pdf) Probabilistic Models for Collective Entity Resolution Between Knowledge Graphs. Jay Pujara, Kevin Murphy, Xin Luna Dong, Curtis Janssen. Bay Area Machine Learning Symposium (BayLearn). (pdf, bibtex) Large-Scale Knowledge Graph Identification using PSL (extended abstract). Jay Pujara, Hui Miao, Lise Getoor, William Cohen. AAAI Fall 2013 Symposium on Semantics for Big Data. (pdf, bibtex) Ontology-Aware Partitioning for Knowledge Graph Identification. Jay Pujara, Hui Miao, Lise Getoor, William Cohen. 2013 CIKM Workshop on Automated Knowledge Base Construction (AKBC). [selected for spotlight talk] (pdf, bibtex, slides) Joint Judgments with a Budget: Strategies for Reducing the Cost of Inference. Jay Pujara, Hui Miao, Lise Getoor. 2013 ICML Workshop on Machine Learning with Test-Time Budgets. (pdf, bibtex) Large-Scale Knowledge Graph Identification using PSL. Jay Pujara, Hui Miao, Lise Getoor, William Cohen. 2013 ICML Workshop on Structured Learning (SLG). (pdf, bibtex) Large-Scale Hierarchical Topic Models. Jay Pujara, Peter Skomoroch. NIPS 2012 workshop on Big Learning. (pdf, bibtex) Social Group Modeling with Probabilistic Soft Logic. Bert Huang, Stephen H. Bach, Eric Norris, Jay Pujara, Lise Getoor. NIPS 2012 workshop on Social Network and Social Media Analysis. (pdf, bibtex) Reducing Label Cost by Combining Feature Labels and Crowdsourcing. Jay Pujara, Ben London, and Lise Getoor. ICML 2011 workshop on Combining Learning Strategies to Reduce Label Cost. [selected for contributed talk] (pdf, bibtex, slides) Facilitating Medication Reconciliation with Animation and Spatial layout. Leo Claudino, Sameh Khamis, Ran Liu, Ben London, Jay Pujara, Catherine Plaisant, Ben Shneiderman. Workshop on Interactive Systems in Healthcare. Coarse-to-Fine, Cost-Sensitive Classification of E-Mail. Jay Pujara and Lise Getoor. NIPS 2010 Workshop on Coarse-to-Fine Processing. [selected for spotlight talk] (pdf, bibtex, slides) PatentsReal-time Ad-Hoc Spam Filtering of E-Mail. Jay Pujara, Patent 8,069,128; awarded 2011. Employing pixel density to detect a spam image. Ke Wei, Hao Zheng, Jay Pujara, Patent 7,882,177; awarded 2011. Identifying IP addresses for spammers. Jaesik Choi, Jay Pujara, Vishwanath Ramarao, Ke Wei, Patent 7,849,146; awarded 2010. | |||||||||||||||||||||||||||||||||||||||
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Work Experience
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Teaching Experience
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Invited Talks, Presentations and Tutorials
Knowledge Graph Construction, talk for D5 group at Max Planck Institut Informatik, Summer 2015. (slides) Knowledge Graph Construction, talk for Web Science and Knowledge Management group at Karlsruhe Institute of Technology, Summer 2015. (slides) Efficient Online Collective Inference for Graphical Models, talk at the New Perspectives for Relational Learning Workshop at the Banff International Research Station, Spring 2015. (video, slides) Knowledge Graph Identification, talk for the ReadTheWeb group at Carnegie Mellon, Fall 2014. (slides) Knowledge Graph Construction, tutorial given in the Advanced Machine Learning course at University of California, Santa Cruz, Spring 2014. (lecture video, demo video, slides) Large-Scale Knowledge Graph Identification using PSL, talk at AAAI Symposium on Semantics for Big Data, Fall 2013. (slides) Ontology-Aware Partitioning for Knowledge Graph Identification, talk at CIKM workshop on Automated Knowledge Base Construction, Fall 2013. (slides) Knowledge Graph Identification, talk at International Semantic Web Conference, Fall 2013. (video, slides) Using Classifier Cascades for Scalable E-mail Classification, talk at the University of Maryland Computer Vision Student Seminar, Winter 2012. (slides) Using Classifier Cascades for Scalable E-Mail Classification, talk at Conference on Collaboration, Electronic Messaging, Anti-Abuse, and Spam, Summer 2011. (slides) Reducing Label Cost by Combining Feature Labels and Crowdsourcing., talk at ICML workshop on Combining Learning Strategies to Reduce Label Cost, Summer 2011. (slides) Coarse-to-Fine, Cost-Sensitive Classification of E-Mail, talk at NIPS workshop on Coarse-to-Fine Processing, Fall 2010. (slides) Using Hadoop to Fight Spam, interview by the Yahoo! Developer network, Spring 2009. (part 1, part 2) | |||||||||||||||||||||||||||||||||||||||
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Graduate-level Course WorkSpring 2012 at University of Maryland
Spring 2011 at University of Maryland
Fall 2010 at University of Maryland
Spring 2005 at Carnegie Mellon University
Fall 2004 at Carnegie Mellon University
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