Khyati Parekh **** *th Ave NE Apt. No. ***, Seattle, WA - 98105

https://linkedin.com/in/khyatiparekh j https://github.com/khyatiparekh Email : ac4a0h@r.postjobfree.com j Mobile : +1-973-***-**** EDUCATION

University of Washington Seattle, WA

Master of Science in Data Science Sep. 2016 – March 2018 Relevant Coursework: Data Visualization, Database Management, Machine Learning, Applied Statistics, Scalable Data Systems.

Nirma University Ahmedabad, India

Bachelor of Technology in Computer Science Jul 2012 – May 2016 Relevant Coursework: Data Structures and Algorithms, Database Management Systems, Data Compression, Probability, Computer Architecture, Image Processing

PROGRAMMING SKILLS

Languages: C, Python, R, HTML, JavaScript, D3, CSS.

Cloud: Amazon Web Services, Microsoft Azure.

Tools: Tableau, Microsoft Excel, MySQL Workbench, Unity3D.

Databases: SQLite, MySQL, AsterixDB, SQL server, Hadoop, Redshift, Spark EXPERIENCE

University of Washington Seattle, Washington

Graduate Research Assistant Jun. 2017 – Sept. 2017 Analyzed and visualized results from the data taken from the syntrophic mutualism experiment between Desulfovibrio vulgarisHildenborough (DvH) and the archaeonMethanococcus maripaludis(Mmp) using Python. Data includes the change in the codon of the evolved and original cultures. Used machine learning to find out which genes affected the mutation the most and bokeh plots to visualize the results effectively.

Indian Institute of Management Ahmedabad Ahmedabad, India Research Assistant Jan. 2016 – May 2016

As a part of my internship, I was part of a team that created part of a Mobile Area Network (MANET) formed by users in a campus community. We developed simulations and evaluations of scheduling algorithms using Java to design an effective data sharing application. Data sharing included defining and implementing data model and data transformation for uploaded data. PROJECTS

Bird species classification, Machine Learning April 2017 – June 2017

Achieved 70% prediction accuracy by implementing multiple machine learning algorithms and pre-trained deep learning model inception v3 to predict bird species using the Caltech-UCSD Birds-200-2011 dataset.

Python vs PySpark, Scalable Data systems Sep 2017 – Dec 2017

In this project, I compared scalable data analysis in Python vs in PySpark. To compare the runtimes of a dataset in Python and Pyspark, I stored my files in an S3 bucket and read my csv files from there into Python and Pyspark respectively. To better compare them, I gradually increased the file size from 100MB to 10GB to see the difference in performance of Pyspark and Python.

Capstone Project: Amplero Time Series analysis Sept. 2017 – Ongoing

Our goal is to predict the future behavior of customers from multivariate time series. Using, Machine Learning and/or Deep Learning methods(section Technical Approach) we will find out the churners and non-churners, and the probability of a customer either switching their existing plan or leaving the network altogether(state transitioning). Also, we will conduct research to measure the similarity between two time series depending on magnitude by doing Value comparison, Trend comparison, Distribution comparison, Distance Analysis, and other Statistical Test like t-test. EXTRA CURRICULUR ACTIVITES

GPSS Coordinator at FIUTS Sept. 2017 – Ongoing

Currently hold an executive position on the board of trustees of FIUTS (Foundation for International Understanding Through Students). FIUTS is a non-profit organization at the University of Washington for incoming international students.

UW Percussion Ensemble Sep 2017 – Ongoing

Part of the percussion ensemble at the University of Washington under the guidance of Dr. Bonnie Whiting. The group focuses on contemporary music of many genres composed for percussion ensemble.

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