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Assistant Java

Levittown, NY
November 28, 2018

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*** ****** ******** *****, ****** G-1108, Stony Brook, NY - 11790


LinkedIn: GitHub: EDUCATION

Stony Brook University, New York, USA Aug 2017 – Dec 2018 Master of Science in Computer Engineering GPA: 3.74/4.0 Coursework – Machine Learning, Analysis of Algorithms, Mobile Cloud Computing, Big Data Systems, Data Structures in Java, Mobile Sensing Systems.

Sir M. Visvesvaraya Institute of Technology, Bangalore, India Aug 2013 – Jul 2017 Bachelor of Engineering in Computer Science and Engineering GPA: 8.5/10 TECHNICAL SKILLS

Programming Languages: Java, Python, C++, C

Database Management Systems: MySQL, PHPMyAdmin, MongoDB, JDBC Data Mining Tools: Yarn, Tez, Hadoop, Apache, Spark, Apache Kafka. Operating Systems: Linux, Ubuntu, Android.

Full-Stack development: Angular JS, PHP, HTML5, CSS Other Software: Android Studio, Visual Studio, Eclipse 4.0, Microsoft Office. WORK EXPERIENCE

Graduate Research Assistant, Stony Brook, New York May 2018 – Present Designed and developed a fine grained ID and attribute – based access control for IoT devices. Created a strong and secure backend database using Java and MySql to ensure the administrator can perform activities without hassles. The database of IoT devices is automatically updated through the data received from Arduino and Raspberry Pi. Developed and designed a Web and Android application for user interface using HTML, PHP and Android Studio 3.0.

Graduate Teaching Assistant, Stony Brook, New York Aug 2017 – Dec 2017 Teaching Assistant for Under graduate student in Java and C++. Dedicated office hours to students, where I helped the professor to clear various doubts in coursework and projects. Part of a team which designed and evaluated a Java project for the class on Smart Library Management System. Conducted regular in class quizzes in Java and C++ for a class of 120 students. Student Research Intern, Indian Institute of Science, India Feb 2017 – May 2017 Worked at the Supercomputer Education and Research Center (SCERC) department as a student intern. Designed and developed a model to predict short term electrical load forecasting using the artificial technique of Long Short-Term Memory (LSTM) using the concepts of Neural Networks and Machine Learning.


Stony Brook University, New York Aug 2017 – Dec 2018 Fusion Ensemble for Google cluster Workload Analysis (Neural Networks) Time series prediction of the CPU and memory usage of google cluster data using Linear Regression, SVM, ARIMA, LSTM and ensemble of RNN-RF. IoT Recycle-Hack@CEWIT 2018 MLH (IoT and Machine Learning) Designed and developed a model to detect and classify different recyclable materials using IoT sensor networks, SVM and K nearest neighbor classifiers. It was integrated with both CISCO Meraki scanner API as well as Softheon payment API for user interface and functionality. InteractDoc (Android, MongoDB and Google Firebase) An interactive Android application to diagnose common household diseases, integrated with a Firebase API chat application and google map API for navigation.

IoT Management and Control (Java, Android, MySQL and HTML) Designed and developed an adaptive IoT environment system to automate a faculty center. An administrator backed system was developed using Java, MySQL, PHP and Angular JS for centralized control. An Android application was developed to provide interaction between the devices and the users. Facial Expression recognition using Convolutional Neural Network (Neural Networks) Using a combination of Linear regression and Multiplayer Perceptron constitutional neural network for facial expression recognition. Sir M. Visvesvaraya Institute of Technology, India Aug 2013 – Jul 2017 A Smart Adaptive LSTM technique used for electrical load forecasting at source (Neural Networks) Designed and Developed a Python Application that helps in accurately predicting the electrical load using regression, SVM, ARIMA and Long short-term

..memory (LSTM). IEEE explore: (URL: Comparison of text classifiers (Natural Language Processing): The project was developed using Python and its NLTK toolkit and Naive Bayes Classifier to review the movies. (Ciencia e Tecnica Vitivinicola Journal and IAENG Journal EL conference).

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