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Data Information Technology

Richardson, Texas, United States
February 01, 2018

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Sangeeta Kadambala

Machine Learning Natural Language Processing Big Data SQL Computer Programming 17817 Coit Rd. Dallas, TX +1-469-***-**** F-1 authorization Professional Summary

A proactive, resourceful and ambitious data enthusiast with machine learning, statistical modelling and analysis skills with sound knowledge in computer programming, algorithms and Big Data ecosystem seeking a full time position. Education

Masters in Data Science University of Texas at Dallas GPA 3.63 Expected May’18 Relevant courses: Machine Learning, Big Data Management and analytics, R for Data Science, Statistical methods for data science, Information Retrieval, Design Analysis and Implementation of algorithms, Database Design Bachelors in Computer science VSSUT Burla, Sambalpur GPA 3.5 May’14 Computer Skills

Languages : Python, R, Core Java, SQL, PL/SQL, Scala, C++, C#, JavaScript, Shell scripting, Pig Latin Tools : R studio, Unity, MongoBooster, Toad, Weka, MySQL Workbench, Oracle SQL Developer, WebCenter Forms Recognition, SVN & Git repository, Rally, Jira, Oracle Enterprise Management Cloud Control, Liquibase, Pentaho, DBeaver, and IBM Data Studio

Big Data Ecosystem : Hadoop Map-reduce, Apache Spark, Scala, Spark SQL & Dataframes, GraphX, MLlib, Spark Streaming, Pig, Hive, Kafka connect, Cassandra, Impala, and HBase Work Experience

University of Texas at Dallas, Dallas, TX - Virtual Humans and Synthetic Societies Lab Research & Development Programmer


Research with Emotive Virtual Patient to design an efficient natural interface while dealing with a human subject in Virtual Reality (Oculus, HTC Vive) and Augmented Reality (Microsoft HoloLens) platforms.

Developed a natural language interface capable of responsive and realistic communication by compiling body language and other physiological information using artificial neural network, HMMs, deep learning, and other Natural language processing techniques. All models are developed in-house from scratch. Copart, Dallas, TX

Summer Intern


Demonstrated problem identification and providing solution in Optical Character Recognition (OCR) implemented using Convoluted Neural Net to scan text data from vehicle accessories.

Optimized model by accommodating more useful predictors and eliminating less useful ones using PCA and lasso on semi-structured data, thereby increasing the accuracy of the regression model by 2.7%.

Collaborated with other team members working on data representations by aiding in information extraction. University of Texas at Dallas, Dallas, TX - Office of Information Technology Student Worker


Applied Time-series plot to predict server and memory availability and saturation levels based on past data. Infosys Ltd, Bangalore, India

Systems Engineer – Cisco Contingent

May’14 –June’16

Trained a model using Brainware classifier to read invoice images(OCR) to automate invoice processing

Developed and modified Pl/SQL packages, triggers, custom OAF pages, reports, shell scripts, personalized standard OAF pages, extended controllers and view objects on finance modules. Projects

Developed a Random Forest model trained with data corresponding to readings from CT scan images to predict abnormal tissue that may cause lung cancer for ALCF Concept to Clinic contest.

Built a recommendation engine to recommend a movie to a certain person based of his previous reviews, reviews of other people and the genre of the movie using R.

Predicted Road Traffic for optimized operation of tollgates using Neural Network, and K-NN models with codes written entirely from scratch in python for KDD – 2017 challenge.

Built machine learning models: Decision tree(ID3), Artificial Neural Networks(Perceptron) and Multinomial Naïve Bayes and clustering(k-nn) using java and python completely from scratch.

Implemented a Search Engine by using Lucene crawler and indexer to crawl webpages, index pages, create a web graph and use these with query expansion. The search engine responded to user queries trough UI.

Analyzed the 2016 US election data using R and plotted the results on a map (using map files).

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