Team Members

Ina Yang

Terry Song

Sanjida Saima

Byron McBorrough

Thien Hoang

Raid Islam


Former Research Team Members (Alumni) ▼

Alivia DiPrimeo

I'm a rising third-year Computer Science student at Drexel University with hands-on experience in Java, Python, C, and Racket. Through my coursework and a Software Engineering internship at Amazon, I've developed a strong foundation in programming and software development, especially in Java. What really drives me is finding ways to bring creativity into the tech world. I love taking an idea or using my creativity to bring something to life with my technical skills.


Nathan Thomas

I'm a Computer Science and Math undergraduate at Drexel University with a passion for creative problem-solving. I've led community-driven projects and events, from debugging code to organizing impactful student activities. Whether it's algorithms, digital systems, or event coordination, I'm all about building things that matter.


Joseph Lewis

I'm a third-year Computer Science major with a focus in game design. I've worked with fellow students to create games at Drexel, and now I'll be serving as the YouTube Manager for AI Atelier. My role is to help showcase all the work the team is doing and bring our projects to a wider audience.


Miraj Yafi

I'm currently a rising third year majoring in Computer Science at Drexel University. My coursework has provided me with a solid foundation in programming with languages such as Python, Java, and C and object-oriented programming principles. I've applied this knowledge to build personal projects and web applications using HTML, CSS, and JavaScript for the frontend, along with Flask and Python for the backend. In the future, I hope to pursue areas related to fullstack development.


Amaro Truong

I'm a third-year Software Engineering student at Drexel University. I've been part of a research effort with Drexel University and the University of Bremen in Germany, focusing on chip security using dummy transistors and adaptive models. I've worked as a Teaching Assistant, helping students with software projects using agile methodologies, and I'm currently interning at Microsoft, contributing to ongoing work with the interaction AI and Networking with protocols like MCP and A2A. As Research Lead for AI Atelier, I'll help guide new explorations at the intersection of AI and ethics.


Lyutfiya Yussupova

I'm a second-year Computer Science student at Drexel University, concentrating in Computer Graphics, Vision & Interaction, and Software Engineering. I recently accepted a position as an Event Coordinator for Drexel's Women in Computing Society, where I help plan events that promote diversity and engagement in tech. I also work as a Support Systems Engineer for Drexel's Office of Sponsored Programs, assisting with system optimization and digital workflow improvements. Previously, I interned as a Web Developer at Chocofood in Kazakhstan, developing user-centered web applications. I'm passionate about building innovative, ethical technologies and love connecting technical skills with creative storytelling.


Vedika Agnihotri

I'm a second-year Computer Science student at Drexel University. I've worked as a Security Analyst for Drexel University and led the Third Party Risk Management project during my time there. I take a strong interest in cybersecurity and ethics in tech, which led me to join AI Atelier to research AI ethics and ideate curriculum in this field. I'm excited to be a part of this venture!

Project Overview

This research project explores the use of generative AI tools for restoring and enhancing degraded video and audio content. The work focuses on two key use cases: (1) improving the quality of dash cam footage by addressing common real-world issues such as glare, shadows, and obstructions like dirty windshields; and (2) restoring old or damaged films and television recordings, including enhancing resolution, removing visual artifacts, and cleaning up audio. The project begins with a survey of current state-of-the-art models and tools, followed by hands-on experimentation and the development of novel approaches tailored to these restoration scenarios. The ultimate goal is to create accessible, AI-powered solutions that preserve visual media and improve its clarity and impact for both archival and contemporary use.

Research Goals

  • Apply AI models for super-resolution and deblurring
  • Evaluate restoration fidelity using quantitative metrics
  • Create reusable restoration pipelines

Methods & Tools

  • GAN-based restoration (e.g.
  • ESRGAN)
  • Frame interpolation (e.g.
  • RIFE)
  • Python
  • FFmpeg
  • and OpenCV

Updates

Biweekly updates and demo videos will be posted here as the project progresses.