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Graduate Teaching Assistant Resume Example

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GRADUATE TEACHING ASSISTANT
Professional Summary
  • Ph.D. student seeking a summer internship to apply my experience of over 2 years in the field of Deep Learning, Big Data Analytics and Recommendation Systems and work on new products that capitalize on emerging technologies to solve customer problems by leveraging my unique skill set.
  • Results-driven researcher from the University of Alabama in Huntsville passionate about developing user-friendly software applications and models. Excellent problem-solving skills and ability to perform well in a team.
Work History
The University Of Alabama In Huntsville - Graduate Teaching AssistantHuntsville, Alabama, USA07/2018 - Current

Gained experience of assisting faculty by performing teaching or teaching-related duties, developing teaching material, preparing, proctoring and grading examinations to both undergraduate and graduate level courses such as

  • Special Topics : Big Data Analytics (CS 696)
  • Introduction to Big Data (CS 488 and CS 588)
  • Introduction to Computer Architecture (CS 413)
  • Introduction to Programming in C++ (CS 121)

EdX - Community Teaching AssistantHuntsville, Alabama, USA10/2019 - 01/2020
  • Managed and monitored an Introduction to Deep Learning with PyTorch course and a Capstone Project on Deep Learning.
  • Reinforced tutorials presented on portal and reviewed material with students one-on-one or in small groups.
Accenture Solutions Pvt. Ltd. - Software Development AssociateBengaluru, Karnataka, India09/2017 - 12/2017
  • Worked as part of an online Festival Reservation System development team as part of a software development project.
  • Bagged ample experience working on Spring Core and Spring MVC frameworks.
Projects
  • Spatial Spectral feature extraction and fusion of Hyperspectral data andclassification using Convolutional Neural Network - Built a classification model which extracted spatial features of Hyperspectral data using Gabor filtering and spectral features with Sparse Random Projections. These features were fed into a CNN for classification. Oversampling of data points from weaker classes was also incorporated using SMOTE. This model was tested on three state of the art data-sets, Salinas-A, Pavia University and Indian Pines which produced 100%, 97% and 91% accuracy respectively with just 10% of training data.
  • Context Adaptive Expert Selection System In Medical Diagnosis - I developed an application to suggest the best suitable medical expert available to treat the aliment one's going through by employing the theory of context adaptability. Here, the symptoms are given as an input by the diseased and those symptoms are used as features by NGram algorithm for decision making. The core area of work on this project was data-mining.
  • Principal Component Analysis v/s Linear Autoencoder for Dimensionality Reduction on Hyperspectral Data - The superiority between PCA and Linear Autoencoder for dimensionality reduction is analysed in this project. This work starts off with PCA with the combination of Gaussian Maximum Likelihood Classifier (GMLC) on a hyperspectral dataset which produced an accuracy of around 99.7%. Later efficacy of combination of a Linear Autoencoder with a GMLC is analysed. Exhaustive results and observations were documented along with analysis on the models computational complexity.
  • Brief Analysis of Computational Complexity of TSNE for Feature Extraction on Remote Sensing Data - Feature Extraction and Feature Map construction is a prominent task in the interest of classification oriented applications. This project mainly analyzes the effect of using TSNE for spectral feature extraction and DR in Remote Sensing Applications.
  • A Brief Analysis Of The Effects Of Dimensionlity Reduction Techniques On Colon Cancer Data and Wisconsin Breast Cancer Data - Experimented the potency of various dimensionality reduction techniques such as PCA, LDA and ICA on Colon Cancer and Wisconcin Breast cancer data set and combined these methods with various classifiers. It was observed that LDA with SVC classifier produced most efficient results of around 98% for just 20% of dimensionally reduced training samples for Breast Cancer data.
Skills
  • Proficient in Python, Keras and Tensorflow
  • Programming Languages : C, C++, Java, C#, JavaScript, HTML5, CSS, Bootstrap
  • DBMS : MySQL, SQLite
  • Operating Systems : Windows, Linux
  • Version Control System : Git
  • Typesetting Softwares : LaTex, Microsoft Office
Publications
  • Bishwas Praveen and Vineetha Menon, "Novel Deep-Learning-Based Spatial-Spectral Feature Extraction For Hyperspectral Remote Sensing Applications," 2019 IEEE International Conference on Big Data, December 2019.
  • Bishwas Praveen and Vineetha Menon, "Journal on Feature Extraction, Fusion and Classification with 3D-CNN for Remote Sensing Applications," IEEE Transactions on Geoscience and Remote Sensing. (Under Review)
Education
Expected in 05/2022University Of Alabama In HuntsvilleHuntsville, Alabama, USAPh.D.: Computer Science
12/2019University Of Alabama In HuntsvilleHuntsville, Alabama, USAMaster of Science: Computer Science

Thesis : Deep Learning-Based Spatial-Spectral Feature Extraction for Hyperspectral Data Analysis Applications.

05/2017B.N.M Institute Of TechnologyBengaluru, Karnataka, IndiaBachelor of Science: Computer Science
Accomplishments

Academic Excellence - UAH Graduate Dean's List 2019

Extracurriculars
  • Was an active member of IEEE student council from 2016 to 2018.
  • Was an active member of a non profit organisation 'MELANGE' from 2015 to 2017.
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Resume Overview

Companies Worked For:

  • The University Of Alabama In Huntsville
  • EdX
  • Accenture Solutions Pvt. Ltd.

School Attended

  • University Of Alabama In Huntsville
  • B.N.M Institute Of Technology

Job Titles Held:

  • Graduate Teaching Assistant
  • Community Teaching Assistant
  • Software Development Associate

Degrees

  • Ph.D. : Computer Science
    Master of Science : Computer Science
    Bachelor of Science : Computer Science

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