Mounika Yakasiri is a Master's student in Computer Engineering at Stevens Institute of Technology with a 3.78 GPA. She received her Bachelor's degree in Electronics and Communication Engineering from PES University in India, specializing in VLSI and embedded systems. Her professional experience includes working as an electrical engineering intern developing Bluetooth sensors and as an Android testing intern. Her skills include programming in C, C++, Python, and Verilog HDL, as well as protocols like MQTT and Bluetooth Low Energy. She has completed academic projects involving environmental sensing with Arduino and Raspberry Pi, and developing a full stack cryptocurrency website. She has also published work on wind speed forecasting using Markov chains.

1. Mounika Yakasiri
1-201-394-2959, Secaucus, New Jersey || myakasir@stevens.edu || www.linkedin.com/in/mounikayakasiri2197
EDUCATION
Stevens Institute of Technology, Hoboken, NJ, USA Anticipated 2021 May
Master of Science: Computer Engineering (Embedded Systems, Software Engineering) GPA – 3.78/4
Course work: Digital systems and Computer Architecture, Real time and Embedded Systems, VLSI design, Data Structures and
Algorithms, Internet of Things.
PES University, Bengaluru, India August 2015 – September 2019
Bachelor of Technology: Electronics and Communication Engineering
Specialization: VLSI – Digital Switching and Logic Design, Low Power VLSI, Pervasive Computing, Verilog Design, Embedded
Systems and Operating systems, Microcontrollers.
PROFESSIONAL EXPERIENCE
Hacol Inc Jan 2020 - Present
Electrical Engineering Intern, New York, United States of America
Working on Nordic nRF52840 SoC for Bluetooth Low Energy to develop a Horticulture Bluetooth Sensor
Interfacing the BME680 Environmental Sensor with the nRF52840 SoC to transmit data using BLE
United Telelinks Private Limited, Bengaluru, India June 2017 - July 2017
Testing intern, R&D Department
Tested Android devices validating different test cases - software test, qc-test and RF-test on android versions ranging from
KitKat to Nougat
Utilized software- Spreadtrum Flash tool – Android MTK, UPGRADEDOWNLOAD R3.0.0001 and WriteIMEI
Leveraged knowledge about Accelerometer(Gravity sensors), Proximity sensor, Magnetic field sensor and Gyroscopic sensor,
GPS signals transmission and receiving functioning and IMEI
SKILLS
Programming: C, C++, Python, Arduino, MATLAB, SCILAB, Verilog HDL, MIPS, ARM, HTML, CSS, MySQL, SQLite
Protocols: MQTT, HTTP, Bluetooth Low Energy
Software: Xilinx ISE, Keil, FreeRTOS, Mentor Graphics, Arduino, Visual Studio Code, CLion, Segger Embedded Studio
Hardware: Arduino Uno, Arduino Nano 33 BLE Sense, Nordic nRF52840 SoC, BME680, Raspberry Pi 3B+, NEO 6-M GPS
module, HMC5883L(GY-271) 3-axis Compass Magnetometer Sensor Module
ACADEMIC PROJECTS
Real-Time Environmental Sensing: An application to present the Real-Time data using Web Services and MQTT April 2020
Presented real-time temperature and humidity data to a web server hosted on a Raspberry Pi 3B+
Established communication between Arduino Nano 33 BLE Sense and Raspberry Pi using MQTT protocol
Designed a Web Application on Raspberry Pi using Flask microframework for front end and backend using Python and MySQL
Small Scale Full Stack Web Application for Online Cryptocurrency Platform April 2020
Developed a Full Stack Web Application using Flask microframework for frontend and backend modules in Python
Designed the user interface requirements of the website using HTML and CSS and backend using Python and MySQL
Stochastic Modelling for the prediction of Wind Speed and Wind Power January 2019 - May 2019
Forecast of wind speed and power training past data (3 years data) with stochastic modelling – Markov Chains, Regression and
Time – series analysis
Implemented 1st and 2nd
order Markov chains, Linear and Second order Polynomial Regression and ARIMA models in Time
– series analysis for forecasting. Worked with MATLAB, SPSS and Microsoft EXCEL
Obtained forecast values of wind speed and power for 14 hours applying Markov Chains, 24 hours and 48 hours of forecast
using Second and third order Polynomial Regression and up to 10 hours in Time – series analysis (ARIMA)
Arduino Controlled Claw Machine November 2015
Built a claw machine employing the Arduino UNO REV3 to detect any object, move and pick it up within radius of 10-50cms
using ultrasonic sensors working on SONAR to determine distance of an object
PUBLICATIONS
M. Yakasiri, J. Avrel, S. Sharma, M. Anuradha and B. K. Keshavan, "A Stochastic Approach for the State-Wise Forecast of Wind Speed
Using Discrete-Time Markov Chain," TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON), Kochi, India, 2019, pp. 575-
580. https://ieeexplore.ieee.org/abstract/document/8929529