Microsoft tools (Excel, Word, etc.)

Siddarth Prasad adyfhh@r.postjobfree.com

*** * ***** *** ******** 85249: 480-***-****

EDUCATION Ira A. Fulton Schools of Engineering, Arizona State University, Tempe, AZ

Bachelor of Science, Computer Science Cumulative GPA: 4.0, Term GPA: 4.16

Fall 2022, Spring 2023 Dean’s List, Ira Fulton’s Leaders Academy

Ira A. Fulton Schools of Engineering

Skills: Proficient in Python, and Java, Proficient in ESP32 Microprocessor design (Micropython), Circuit design using Digital, Proficient in 3D printed CAD, Proficient in MATLAB, EV3 MicroPython, iVerilog, Arduino, Supervised Machine learning algorithms.

Courses Taken: CSE 230, CSE 120, MAT 242, MAT 266, CSE 205

Learn to Program: The Fundamentals - University of Toronto [Coursera]

: Data Structures and Algorithms (Part 2 of the first course)

Deep Learning Specialization – Andrew Ng – [Coursera]

PROJECTS

Fully Automated Maze Navigating Robot (Fall 2022) - Nov 19th - Dec 23rd

FSE 100

• Worked as a leader in team of 3 and oversaw coding the automated navigation using Micro python Embedded C++ in MATLAB and designing efficient 3D printed models to attach the EV3 battery to the hull of the robot using CAD.

• Implemented self-navigating design using Dijkstra’s pathfinding solution

• The robot of our group placed 1st out of the other classes with a perfect score (fastest time, least collisions)

ESP32 Dorm Camera / Motion Sensor app (Spring 2023) Jan. - Present

• Mounted ESP32 to my dorm door and made makeshift ring doorbell

• The sensor accurately detects people in a ~ 3ft radius from the peephole. I programmed using a Micro python IDE, and the live output is stored in an SSD which is displayed on a monitor in my dorm via USB.

• I am currently developing an app that can allow me to broadcast live footage of my dorm camera to my phone.

House Market Price evaluation using Machine Learning (Spring 2023) Feb. - Present

• Fully programmed a housing market simulator using sample size data of Chandler homes from public databases to predict new housing prices outside of the trained database.

• The machine learning algorithm was a supervised mode of training, and I used linear regression with multiple features such as [Age of house (years), size of house(sqft^2), Number of Bedrooms, Number of Bathrooms)

• Used gradient descent algorithms (with multiple features) in Python to minimize cost function of the linear regression graph.

• Used matplotlib to make visual representation of my predicted value of houses based on real market data.

LAB WORK

ASU NASA II Lab (Interplanetary Initiative Volunteer) (Spring 2023) Mar - Present

•Worked on the hardware and software development of CubeSat project COCONUT.

•Worked with lab tools such as oscilloscope, power supplies, ADC converters to test chipset functionality.

•Coded in C/C++ and Micropython of Raspberry Pico boards to transmit radio waves from satellites in CubeSat.

Contact this candidate