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KARTHIK TANGIRALA
(785) 320-0252
[email protected]
EDUCATION
Kansas State University, Manhattan, KS
 Ph.D. in Computer Science (GPA: 3.8)
 MS in Computing and Information Science (GPA: 3.7)
 Areas of Interest: Machine Learning, Information Retrieval, Big Data Analysis, Bioinformatics, and Data Mining
 Graduate Coursework: Algorithms; Databases; Programming Languages; Information Retrieval
May 2015
August 2011
WORK EXPERIENCE
SDE Intern
Microsoft Corporation
Summer 2014
 Worked with the Application Insights team, specifically focusing on data monitoring and analysis
 Used Elasticsearch to generate statistical reports that can benefit customers and developers
Engineering Intern
Proofpoint, Inc.
Summer 2013
 Worked on developing a system for stopping the spam campaigns.
 Introduced a dynamic clustering framework into the system for improving the efficiency.
Machine Learning Intern
Proofpoint, Inc.
Summer 2012
 Applied machine learning algorithms for spam message classification.
 Added clustering based knowledge to the existing learning process to improve the efficiency of the classifier.
 Worked on statistical analysis of spam data for reducing the learning time of the classifier.
Research Assistant
Kansas State University
September 2009 – Present
 Working on semi-supervised and transductive machine learning algorithms, constraint based information retrieval techniques and large
scale data processing.
 Specialization: Generating low dimensional informative sequential features/patterns from biological sequences
TECHNICAL EXPERIENCE
Projects
 Community Detection-based Feature Extraction (2014): Working on applying community detection algorithms to identify patterns in
biological sequences, which are further used as input features for learning algorithms.
 Burrows Wheeler Transform for Feature Generation (2013): Worked on a novel idea of generating a low dimensional informative feature
set from biological sequences using Burrows Wheeler Transform.
 Co-training to a Multi-view Learning Process (2011 – 2012): Extended a two view semi-supervised learning algorithm (Co-training) to a
multi view process to improve accuracy and efficiency.
 Constraint-based Information Extraction from Biological Literature (2011 – 2012): Proposed a constraint based information retrieval
technique for extracting biological information from large amounts of literature.
 Semi-Supervised Learning of Alternative Splicing Events (2011): Availability of large amounts of unlabeled data motivated us in applying
semi-supervised learning algorithms to predict alternative splicing events
PUBLICATIONS
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“Semi-supervised Learning of Alternative Splicing Exons Using Co-training”, IEEE International Conference - Bioinformatics and
Biomedicine (BIBM), 2011 IEEE International Conference in Atlanta, GA, 2011
“Extraction of Gene Regulatory Networks from Biological Literature”, 3rd IEEE International Conference on Computational Advances in Bio
and Medical Sciences (ICCABS) in New Orleans, LA, 2013
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“Generating Features using Burrows Wheeler Transformation for Biological Sequence Classification”, 5 International Conference on
Bioinformatics Models, Methods and Algorithms (BIOINFORMATICS) in Loire Valley, France, 2014
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“Semi-supervised Classification of Protein Sequences Using Burrows Wheeler Transform-based Features”, 6 International Conference on
Bioinformatics and Computational Biology (BICOB) in Las Vegas, NV, 2014
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“Community Detection-based Features for Sequence Classification”, The 5 ACM Conference on Bioinformatics, Computational Biology,
and Health Informatics (ACM BCB) in Newport Beach, CA, 2014
“Predicting Protein Localization Using a Domain Adaptation Naive Bayes Classifier with Burrows Wheeler Transform Features”, IEEE
International Conference - Bioinformatics and Biomedicine (BIBM) in Belfast, UK, 2014.
LANGUAGES AND TECHNOLOGIES
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Languages: Java, C#, R
Tools: Elasticsearch, weka
Database: MySQL, Hive (Hadoop implementation of Database management)