About Me
My work spans software engineering, machine learning, hardware integration, and applied computational research, with expertise in developing scalable systems, advancing imaging techniques, and building hardware-software solutions. In software engineering, I have designed and implemented full-stack applications, including cross-platform mobile apps using Flutter and React Native, and backend systems optimized for real-time data acquisition and synchronization in low-connectivity environments. My work has focused on distributed architectures, secure communication protocols, multimedia processing, geolocation, and API integrations across diverse industries, such as emergency response and corporate event management. I have extensive experience in embedded systems, working with platforms like Raspberry Pi, Arduino, and FPGAs to develop IoT-based medical devices and distributed signal processing systems. My projects include building edge computing pipelines for real-time CO₂ waveform analysis and anomaly detection in pediatric respiratory monitoring, as well as designing imaging systems capable of sub-millimeter spatial accuracy. In research, I have contributed to computational frameworks for tagged MRI myocardial strain analysis, focusing on finite element modeling, synthetic phantom design, and advanced metrics like SSIM and Dice Coefficient to optimize imaging-based quantification pipelines. I have also worked on enhancing microwave and metasurface imaging systems through deep learning architectures, including U-Nets, GANs, and transformer-based models, achieving significant improvements in noise resilience and resolution under bandwidth constraints. My research involved dataset synthesis, model validation, and performance evaluation using tools like TensorFlow, PyTorch, and Keras. This combination of software development, hardware integration, and computational research informs my interest in neural engineering, particularly in brain-computer interfaces, neural signal processing, and closed-loop systems. My work in imaging, signal processing, and embedded systems provides a robust foundation for exploring how computational and engineering methods can drive advancements in interfacing technologies and understanding complex biological systems.
Education
University of Maryland - College Park
workspace_premium B.S. Computer Engineering
stars Achievements & Involvement
- arrow_right Anticipated Graduation at Age 18
- arrow_right Co-founder and Tech Lead at the App Dev Club
- arrow_right Chief Executive Officer of AI/ML Club
- arrow_right Secretary of Cloud Computing Club
- arrow_right Bitcamp 2023 and 2024 Workshop Presenter and Mentor
- arrow_right Member of IEEE @ UMD
- arrow_right Member of Google Developer Student Club
- arrow_right Member of NeuroTech @ UMD
- arrow_right Member of XR Club @ UMD
- arrow_right Member of Philosophy Club @ UMD
Prince George's Community College
workspace_premium A.S. General Studies
stars Achievements & Involvement
- arrow_right Co-founder and Vice President of the Gaussian Club of Mathematics
- arrow_right 2nd place Student AMATYC Math League
- arrow_right Full ride Promise Scholarship and Club Leader Award recipient
- arrow_right STEM Week presenter
- arrow_right Member of Phi Theta Kappa Honor Society
- arrow_right Member of Student Research Club
- arrow_right Member of Diverse Male Student Initiative
- arrow_right Promise Scholarship recipient
- arrow_right Club Leader Award recipient recipient
Relevant Coursework
- Elements of Discrete Signal Analysis
- Digital Logic Design
- Digital Circuits and Systems Laboratory
- Electric Circuits
- Analog and Digital Electronics
- Computer Organization
- Advanced Data Structures
- Applications of R for Data Science
- Applied Probability and Statistics I
- Linear Algebra for Scientists and Engineers
- Differential Equations for Scientists and Engineers
- General Physics: Electricity, Magnetism and Thermodynamics
- General Chemistry for Engineering
- Computer Programming for Engineers and Scientists
Experience
Projects
Research & Publications
My research interests lie at the intersection of engineering and neural systems, shaped by my experiences in signal processing, neuromorphic computing, and medical device development. Exploring biological signal processing has revealed how engineering principles can illuminate neural function and drive therapeutic innovation. I aim to design tools that integrate biological signal analysis with adaptable neural interfaces, targeting closed-loop stimulation systems and decoding algorithms to advance therapeutic applications.
Deep Learning Approach for Image Quality Improvement in Computational Microwave Imaging Systems
Manuscript in preparation, targeted for IEEE Transactions on Antennas and Propagation (expected 2025)
Enhancing Microwave Imaging: A Robust and Efficient Approach with Compact Optical Sensors and Advanced Neural Networks
Full-Stack Development with Firebase and Flutter
Introduction to Dart and Flutter
Introduction to App Development
Certificates
- The Complete 2021 Flutter Development Bootcamp with Dart
- Deep Learning Course with Flutter & Python - Build 6 AI Apps
- Profitable App Development Blueprint for Startups
- The Brain and Space
- Foundations of User Experience (UX) Design
- Cloud Computing Basics (Cloud 101)
- Powerful Tools for Teaching and Learning: Digital Storytelling
- Statistics with SAS
- Foundations of Mindfulness
- Reasoning Across the Disciplines
- Problem Solving Using Computational Thinking
- Introduction to Marketing
- Innovation Through Design: Think, Make, Break, Repeat
- Car Transport App in Figma
- Programming Foundations with JavaScript, HTML and CSS
- PHP for Beginners - Become a PHP Master - CMS Project
- MATLAB Onramp Training
Contact
- Phone: (240) 390-7207
- Website: momoniem.com
- Email: [email protected]
- LinkedIn: in/-mae
- GitHub: madamoniem
- Location: Seabrook, Maryland