Sumanth Vissamsetty

ac8tkk@r.postjobfree.com 225-***-**** www.linkedin.com/in/sumanth-vissamsetty EDUCATION

Graduate Degree: Graduation Date - May 2019

Louisiana State University 2016-Present

Master of Science (Major: Computer Science Minor: Applied Statistics) GPA: 3.7 TECHNICAL SKILL SET

Programming Languages Python, R, SAS, C, C++, C#, VB.NET, ASP.NET, Java Database Skills SQL Server, MySQL, MS Access, Oracle DB, AWS Web Technologies HTML, CSS, PHP, JSP, XML, JavaScript WORK EXPERIENCE

Data Engineering Graduate Assistant, LSU Jan 2018 – Present

● Responsible for data wrangling and led the team to better data cleaning and data etiquette.

● Proposed and built a better solution to the existing crash location validation algorithm which resulted in increased efficiency, decreased processing time and retrieval of relevant information.

● Automated several manually processes including geo-processing tasks which save a lot of man- hours every month.

● Technologies used - Python (arcpy), ArcMap, ArcGIS, SQL. Graduate Assistant, LSU Oct 2016 – Dec 2017

● Built an image processing tool to identify, classify and count pedestrians from DOTD video data.

● Developed a software tool to perform engineering analysis to update a pile-design by Cone Penetration Test (CPT) based on newly developed Pile-CPT Methods and pile design features.

● Technologies used - Python (OpenCV), VB.NET, SQL. PROJECTS

Mosquito-image classification using Deep Learning (Masters Thesis), May 2018 – Present

● Built an end-to-end deep learning solution using Convolutional Neural Network for the St. Tammany Parish Mosquito Abatement District (STPMAD).

● Accurately translated the business problem into a machine learning (classification) task.

● Collaborated with members of the team who don’t share the same background.

● Directed and guided the team towards an effective data collection procedure which increased the quality of the data, decreasing the computational effort for data pre-processing.

● Pre-processed the raw images and built a data pipeline for the machine learning process.

● The learning algorithm yielded a whopping 98% average accuracy for the classification task.

● Technologies used - Python(Keras, numpy, pandas, tkinter) Whose voice is that?, Jan 2017 – May 2017

● Collaborated with the team to build an ML solution to determine gender based on the acoustics.

● Applied various machine learning algorithms like SVM, Random Forest, Regression, etc., to achieve >97% accuracy in most algorithms.

● Technologies used - Python(sci-kit learn)

Movies Recommendation System, Jan 2017 – May 2017

● Built a machine learning solution to recommend movies based on the users’ taste.

● Used item-based collaborative filtering technique and Apache Mahout libraries on ‘MovieLens 10M’ dataset to recommend movies in a customized Apache Yarn environment.

● Technologies used - Java.

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