Publications (Google Scholar)

[By type] [By year]

2020

1. Uplift Modeling with Importance Weighting
   C. Ozcaglar, R. Ranjan, V. Parimi
   Patent Issued, June 2020.
   

2019

1. Entity Personalized Talent Search Models with Tree Interaction Features
   C. Ozcaglar, S. Geyik, B. Schmitz, P. Sharma, A. Shelkovnykov, Y. Ma, E. Buchanan
   WWW, 2019.
   

2018

1. Towards Deep and Representation Learning for Talent Search at LinkedIn
   R. Ramanath, H. Inan, G. Polatkan, B. Hu, Q. Guo, C. Ozcaglar, R. Wu, K. Kenthapadi, S. Geyik
   CIKM, 2018.
   

2. Talent Search and Recommendation Systems at LinkedIn: Practical Challenges and Lessons Learned
   S. Geyik, Q. Guo, B. Hu, C. Ozcaglar, R. Wu, K. Kenthapadi
   SIGIR, 2018.
   

3. Entity-Level Search Models with Tree Interaction Features
   C. Ozcaglar, S. Geyik, P. Sharma, B. Schmitz, E. Buchanan
   Patent Filed, August 2018.
   

4. Utilizing Search Facets Based on Project Context
   P. Cheung, E. Buchanan, C. Liao, D. Boyd, G. Gulati, F. Or, C. Ozcaglar
   Patent Filed, July 2018.
   

5. Unsupervised Learning of Entity Representations Using Graphs
   R. Ramanath, G. Polatkan, Q. Guo, C. Ozcaglar, K. Kenthapadi, S. Geyik
   Patent Filed, July 2018.
   

6. Generating Supervised Embedding Representations for Search
   R. Ramanath, G. Polatkan, Q. Guo, C. Ozcaglar, K. Kenthapadi, S. Geyik
   Patent Filed, July 2018.
   

7. Generating Supervised Embeddings Using Unsupervised Embeddings
   R. Ramanath, G. Polatkan, Q. Guo, C. Ozcaglar, K. Kenthapadi, S. Geyik
   Patent Filed, July 2018.
   

8. Generating Candidates for Search Using Scoring / Retrieval Architecture
   R. Ramanath, G. Polatkan, Q. Guo, C. Ozcaglar, K. Kenthapadi, S. Geyik
   Patent Filed, July 2018.
   

9. Applying Learning-to-Rank For Search
   R. Ramanath, G. Polatkan, Q. Guo, C. Ozcaglar, K. Kenthapadi, S. Geyik
   Patent Filed, July 2018.
   

2017

1. Feature Selection Impact Analysis for Statistical Models
   C. Ozcaglar, V. Dialani, S. Smoot, S. Geyik, A. Nair
   Patent Filed, December 2017.
   

2. Generalized Linear Mixed Models (GLMix) for Improving Search
   C. Ozcaglar, R. Wu, J. Yang, A. Gupta, A. Nair
   Patent Filed, December 2017.
   

2014

1. Hybrid Coexpression Link Similarity Graph Clustering for Mining Biological Modules from Multiple Gene Expression Datasets
   S. Salem, C. Ozcaglar
   BioData Mining, 2014.
   

2013

1. MFMS: Maximal Frequent Module Set Mining from Multiple Human Gene Expression Datasets
   S. Salem, C. Ozcaglar
   ACM SIGKDD International Workshop on Data Mining in Bioinformatics (BIOKDD), Chicago, August 2013.
   

2. TB-vis: Visualizing TB Patient-pathogen Relationships
   K. P. Bennett, C. Ozcaglar, J. Ranganathan, S. Raghavan, J. Katz, D. Croft, B. Yener, A. Shabbeer
   Tuberculosis, 2013.
   

2012

1. Crossing Minimization within Graph Embeddings
   A. Shabbeer, C. Ozcaglar, K. P. Bennett
   arXiv, 2012.

2. Inferred Spoligoforest Topology Unravels Spatially Bimodal Distribution of Mutations in the DR Region
   C. Ozcaglar, A. Shabbeer, N. Kurepina, N. Rastogi, B. Yener, K. P. Bennett
   IEEE Transactions on NanoBioscience, 2012.

3. Algorithmic Data Fusion Methods for Tuberculosis
   Ph.D. thesis, 2012.
   

4. Host-pathogen Association Analysis of Tuberculosis Patients via Unified Biclustering Framework
   C. Ozcaglar, B. Yener, K. P. Bennett
   Rensselaer Polytechnic Institute, TR-12-05, 2012.
   

5. Epidemiological Models of Mycobacterium tuberculosis Complex Infections
   C. Ozcaglar, A. Shabbeer, S. Vandenberg, B. Yener, K. P. Bennett
   Mathematical Biosciences, 2012.
   Most accessed paper of Mathematical Biosciences journal between March 2012 and December 2012.

6. TB-Lineage: An Online Tool for Classification and Analysis of Strains of Mycobacterium tuberculosis complex
   A. Shabbeer, L. S. Cowan, C. Ozcaglar, N. Rastogi, S. L. Vandenberg, B. Yener, K. P. Bennett
   Infection, Genetics and Evolution, 2012.
   

7. Web Tools for Molecular Epidemiology of Tuberculosis
   A. Shabbeer, C. Ozcaglar, B. Yener, K. P. Bennett
   Infection, Genetics and Evolution, 2012.
   Most accessed paper of Infection, Genetics and Evolution journal between December 2011 and June 2012.

2011

1. Data-driven Insights into Deletions of Mycobacterium tuberculosis complex Chromosomal DR Region Using Spoligoforests
   C. Ozcaglar, A. Shabbeer, N. Kurepina, B. Yener, K. P. Bennett
   IEEE BIBM, Atlanta, November 2011.

2. Visualization of Tuberculosis Patient and Mycobacterium tuberculosis complex Genotype Data via Host-pathogen Maps
   K. P. Bennett, C. Ozcaglar, J. Ranganathan, S. Raghavan, J. Katz, D. Croft, B. Yener, A. Shabbeer
   IEEE BIBM Workshop on Computational Advances in Molecular Epidemiology, Atlanta, November 2011.
   

3. Knowledge-based Bayesian network for the Classification of Mycobacterium tuberculosis complex Sublineages
   M. Aminian, A. Shabbeer, K. Hadley, C. Ozcaglar, S. Vandenberg, K. P. Bennett
   ACM BCB, Chicago, August 2011.
   

4. Sublineage Structure Analysis of Mycobacterium tuberculosis complex Strains with Multiple-biomarker Tensors
   C. Ozcaglar, A. Shabbeer, S. Vandenberg, B. Yener, K. P. Bennett
   BMC Genomics, 2011.
   

2010

1. Examining the Sublineage Structure of Mycobacterium tuberculosis complex Strains with Multiple-biomarker Tensors
   C. Ozcaglar, A. Shabbeer, S. Vandenberg, B. Yener, K. P. Bennett
   IEEE BIBM, Hong Kong, December 2010.

2. Optimal Embedding of Heterogeneous Graph Data with Edge Crossing Constraints
   A. Shabbeer, C. Ozcaglar, M. Gonzalez, K. P. Bennett
   NIPS Workshop on Challenges of Data Visualization, Whistler, BC, Canada, 2010.
   

3. Multiple-biomarker Tensor Analysis for Tuberculosis Lineage Identification
   C. Ozcaglar, A. Shabbeer, S. Vandenberg, B. Yener, K. P. Bennett
   NIPS Workshop on Tensors, Kernels and Machine Learning, Whistler, BC, Canada. 2010.
   

4. A Clustering Framework for Mycobacterium tuberculosis complex Strains Using Multiple-biomarker Tensors
   C. Ozcaglar, A. Shabbeer, S. Vandenberg, B. Yener, K. P. Bennett
   Rensselaer Polytechnic Institute, TR-10-08, 2010.
   

2008

1. Classification of Email Messages into Topics Using Latent Dirichlet Allocation
   M.S. thesis, 2008.
   

2. MetPetDB: A Database for Metamorphic Geochemistry
   F. S. Spear, J. M. Pyle, S. Adali, B. Szymanski, A. Waters, Z. Linder, C. Ozcaglar, S. Pearce
   Rensselaer Polytechnic Institute, TR-08-14, 2008.

2007

1. MetPetDB: The Unique Aspects of Metamorphic Geochemical Data and Their Influence on Data Model, User Interface and Collaborations
   J. M. Pyle, F. S. Spear, S. Adali, B. Szymanski, S. Pearce, A. Waters, Z. Linder, C. Ozcaglar
   Geological Society of America Abstracts with Programs, Denver, 2007