Software Engineer
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I am a versatile software engineer with a strong foundation in computational mathematics and extensive experience across diverse technical domains, including AI-driven solutions, game development, and scalable backend systems.
I hold a Bachelor of Science in Computational Mathematics from Michigan State University and have earned certifications in machine learning and cloud-based ETL processes. Additionally, my research contributions in AI and synthetic datasets have been recognized through publications in prestigious conferences such as IEEE ICCV and SmartCloud.
As a Software Development Consultant at Feel Good AI (2023–2024), I contributed to AI-driven products by integrating large language models, building backend systems on Google Cloud Platform, and creating JavaScript APIs. Additionally, I developed a chatbot avatar using Unity3D and a progressive web application for both mobile and desktop platforms with React and CSS.
As an Independent Unity Game Developer (2020–2021), I designed real-time multiplayer networking solutions, built functional prototypes, and documented detailed technical designs. My work included testing and optimizing performance for device compatibility.
Earlier in my career at EPAM Systems (2019–2020), I focused on automating testing and data processing tasks using Python and Bash, streamlining data collection processes, and executing SQL queries to support analytics.
I hold a Bachelor of Science in Computational Mathematics (3.6 GPA) from Michigan State University (2019), where I developed strong analytical and problem-solving skills applicable to software development and machine learning.
I also completed the following certifications:
Machine Learning on GCP: Expertise in ETL processes, statistical models, and ML pipelines.
Machine Learning by Stanford University: Advanced understanding of neural networks, logistic regression, and clustering methods.
I was also a contributing author to the following research papers:
"MUVA: A New Large-Scale Benchmark for Multi-view Amodal Instance Segmentation in the Shopping Scenario." IEEE/CVF ICCV 2023.
"Making Autonomous Stores Smarter (MASS): A Practical Solution to Improve Product Detection Performance Using Synthetic Dataset at Scale." IEEE SmartCloud 2023.
