KARTIKEYA S. BAGALORE ***** Launch Circle, Apt ***, Manassas VA -20109

ac2tv2@r.postjobfree.com; ac2tv2@r.postjobfree.com Cell: 864-***-**** https://www.linkedin.com/in/kartikeyabagalore

OBJECTIVE:

Interested in a career as a Data Scientist/ Machine Learning Engineer (Full-time)

• Data Scientist Intern at Micron Technology, focused on Deep Learning, and Neural Networks using Tensorflow, Python, Scala, Apache Spark, Apache HBase and Hadoop.

• A hard-working and self-motivated professional with a Master’s Thesis in the study of implementing a HMM-based classifier for diagnosis of epilepsy in patients.

TECHNICAL SKILLS:

Programming: Python, MATLAB, Scala, R, C, C++, Collaboration: Git, Confluence, JIRA. Data Science & ML: Spark, TensorFlow, Anaconda, Conda, Scikit-learn, Numpy, Pandas, Hadoop, HBase, MapReduce, Spark Streaming, SparkSQL, Scala.

PROFESSIONAL BACKGROUND:

Data Scientist Intern at Micron Technology, Inc., Washington D.C Metro Area (March 2017 - Present) Project: Operations Central Team – Big Data

• Create packages to access HBase tables using Python, Spark and Scala, and prepare tabular datasets for ML analysis.

• Implement Machine Learning algorithms and visualizations on the curated data, using Scikit-Learn, matplotlib, and Spark MlLib libraries.

• Built and trained a deep learning network using Tensorflow on the data, and reduced wafer scrap by 15%, by predicting likelihood of wafer damage. A combination of the z-plot features, image features (pigmentation) and probe features are being used.

• Pioneered migration of all the packages from Python 2 to Python 3 and Spark 1.6 to Spark 2, including all dependencies.

• Sharing and documenting results between global teams using Confluence, Jupyter Notebooks, Git, etc.

Graduate Teaching Assistant at Clemson University, Clemson (August 2015 - Dec 2016) Courses Taught: Basic Electronics (ECE3110), Electrical Circuits (ECE3120)

• Conduct and supervise labs for multiple sections of 15-20 students each for the semester long laboratory course.

• Teach basics of Electronic devices and circuits like transistors, amplifiers, BJTs, FETs, transformers, filters etc.

Program Analyst at Cognizant Technology Solutions, Pune, India (July 2013 - July 2014)

• Solving ad-hoc errors that may occur during storage of confidential customer data using ETL tools like Informatica, SQL and UNIX. PROJECTS AND RESEARCH:

Master’s Thesis

Detection & Classification of epileptic spikes in Patient EEG using HMMs and LPC features:

• Modelled a set of HMMs to automate the process of detecting the presence of an epileptic spike in patient EEG, to help diagnose vulnerability to epilepsy at early stages. HMM: Hidden Markov Models

• Used MATLAB and Python to train a ML-based HMM classifier to classify epileptic transients as healthy or epileptic.

• Built Fuzzy HMMs to better incorporate real world data and confidence factors into the model, with 86% accuracy of detection.

Data Science Projects and Certifications:

Movie Recommendation for Similar and Popular Movies using MovieLens Dataset:

• Implemented a movie recommender system, to recommend 10 similar and popular movies for every movie watched, based on 1M movie reviews from the MovieLens dataset.

• Implemented using Spark-Scala, SparkSQL and Pyspark, and can be run on a distributed Hadoop cluster. Character Recognition (0-9) on the MNIST Dataset

• Built a fully connected deep (3/5layers) neural network to classify images from the MNIST data with 94% accuracy on the test set.

• Project implemented on TensorFlow, and exercises hyperparameter tuning, regularization and optimization techniques. Deep Learning Specialization by Andrew Ng at deeplearning.ai Big Data Certification from John Hopkins University on Coursera

Graduate Studies

Pattern Recognition, Machine Learning & Artificial Neural Networks coursework (MATLAB & Python):

• Proficient in implementing and using algorithms like Bayes classifier, Gradient descent, NNR clustering techniques, GDR, data balancing, cleaning, normalization, feature selection, PCA, k-fold cross validation, etc.

• Implemented Feed Forward networks, recurrent networks, TSP, CSPs, Hopfield nets.

• Experience in building and modelling regression and classification trees (CARTs), SVMs and Random Forest Classifiers.

• Well versed in using Python packages like scikit-learn, numpy, pandas, matplotlib, seaborn and Tensorflow for efficient ML classifiers. EDUCATIONAL BACKGROUND:

Clemson University, Clemson, South Carolina Dec 2016 Master of Science in Electrical Engineering.

Dr. Babasaheb Ambedkar Technological University, Maharashtra, India Nov 2012 Bachelor of Technology in Electronics & Telecommunication Engineering

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