About
Hi there! My name is Abhijit Chebiyam, and I am a recent graduate from the University of Illinois at Urbana - Champaign with a Bachelors of Science in Computer Engineering. My primary interests lie in the space of Machine Learning, Artificial Intelligence, and API development. If you are a hiring manager looking for entry-level software engineers on your team, please contact me through my email or phone number!
Full-Stack Software Engineer
- Birthday: January 5th, 2001
- Email: abhichebiyam@gmail.com
- City: Livermore, CA
- Phone:+1 (408)-499-6252
I am knowledgeable in several backend languages/frameworks such as Python, Java, C++, and Flask. I am also experienced in certain frontend languages/frameworks such as HTML, CSS, and React. In my free time I enjoy playing sports such as basketball and tennis. I also love to cook food and play the acoustic guitar.
Skills
This section details the languages and technologies I'm most experienced in.
Resume
Education
University of Illinois at Urbana-Champaign
Masters in Computer Science (Coursera)
May 2023 – Present
Coursework: Internetworking, Internet of Things, Modern Software Development, Robust and Probabilistic Design
University of Illinois at Urbana-Champaign
Bachelors of Science in Computer Engineering
Aug. 2019 - May 2023
Coursework: Data Structures & Algorithms, Artificial Intelligence, Internet of Things, Robust and Probabilistic Design, Computer Systems Engineering, Database Systems
KTH Royal Institute of Technology
School of Electrical Engineering and Computer Science (Study Abroad)
Jan. 2022 – June 2022
Coursework: Internetworking, Internet of Things, Modern Software Development, Robust and Probabilistic Design
Projects & Certifications
Pet Food/Water Dispensing System
- Developed a food & water dispensing system for pets that notifies the owner when the dispenser has been activated
- Built a Python-based notification subsystem comprising of Tenserflow’s object detection model API & Twilio’s API
Linux Kernel Operating System
- Designed an Operating System based on Linux through development of x86 assembly & C code which implemented concepts such as an in-memory file system, user programs, and multitasking iteratively
- Contained functionality involving the i8259 PIC interrupt handler, keyboard input buffer, and ACPI/CPUID support
Stanford Machine Learning Certification (Coursera)
- Built and trained machine learning models in Python using libraries such as Numpy and sckit-learn for prediction and binary classification tasks as well as neural networks through Tensorflow for multi-class classification
- Contained functionality involving the i8259 PIC interrupt handler, keyboard input buffer, and ACPI/CPUID support
Professional Experience
Nuro
Software Engineering Intern - Mountain View, CA
Jun. 2022 - Aug. 2022
- Developed a requirements document specifying fault definitions at the system level and built a verification plan to maintain fault coverage for the Onboard and System Monitoring Modules
- Designed a Python script based on gRPC framework to find correlations between module exits and latency kickouts through metrics such as top module and outlier timer latency reporters
- Utilized SQL to extract Log IDs from BigQuery that supported extraneous error thresholds for safety-critical faults
Intel
Software Engineering Intern - Folsom, CA
May 2021 - Aug. 2021
- Instituted implementation of Movidius VPU through Python which enables computer vision and edge AI techniques in areas such as computer manufacturing, edge servers, and industrial automation
- Integrated Infer Request API within OpenVINO Inference Engine to run Image Classification sample in asynchronous mode
- Implemented GoogleTest primer to debug drivers/packages which led to 70% increase in VPU test coverage
Semiconductor Research Corporation
Research Student - Urbana, IL
May 2020 - Apr. 2021
- Trained a Machine Learning model with an R Squared value of 0.92 using Python and Keras API to compare real and predicted stock prices of Fortune 500 companies
- Integrated Synthetic Minority Oversampling Technique to resolve imbalance classification and model validation
- Generated activation functions that create epochs to analyze stock data within a 50-day period
Technologies & Frameworks
- AWS
- React
- Flask
- Kafka