About Me
I’m Dr. Jordan Kim, a computer science researcher driven by curiosity and a real interest in problems that don’t give up their answers easily. My work sits where theory meets the messiness of real systems—algorithms, distributed computing, machine learning, the kind of stuff that looks clean on paper but behaves wildly in practice. That’s the part I enjoy: tracing the why behind complex behavior and turning that understanding into something others can build on.
When I’m not deep in code or research papers, I usually have a notebook nearby filled with half-formed ideas, odd questions, and the occasional breakthrough scribbled in the margins. I care a lot about clarity—whether I’m writing, teaching, or presenting—and I like helping people see complicated ideas start to make sense. Research can be slow and stubborn work, but the moments when everything clicks make it worth every hour.
Research Areas
Artificial Intelligence
Machine learning, deep learning, and neural networks for complex problem solving.
Distributed Computing
Algorithms and architectures for distributed systems and cloud computing.
Cybersecurity
Security protocols and systems for protecting digital infrastructure.
Real-time Systems
Designing and implementing systems with strict timing constraints.
Featured Projects
2024
Neural Decode Interface
Real-time EEG decoding for paralysis patients
Paper
Project
2023
Neural Decode Interface
Real-time EEG decoding for paralysis patients
Paper
GitHub
2023
Neural Decode Interface
Real-time EEG decoding for paralysis patients
Paper
Code
2023
Neural Decode Interface
Real-time EEG decoding for paralysis patients
Paper
Video
Research Impact
1,240+
Citations
18%
5
Book Chapters
12%
3
Patents
10%
$ 4.2 M
Funding
25%
24
Journal Articles
18%
38
Conference P.
12%
Selected Publications
Real-time AI Infrastructure for Emergency Response Systems
Qadir, S., Johnson, M., & Williams, T. (2024). Journal of Artificial Intelligence Research, 72, 145-178.
This paper presents a novel architecture for AI-powered emergency response systems that can process and analyze data in real-time to improve decision-making during critical situations.
Title
Optimizing Neural Networks for Edge Computing in IoT Devices
Qadir, S., & Rodriguez, A. (2023). IEEE Transactions on Mobile Computing, 22(5), 1892-1905.
We propose a new method for optimizing deep neural networks to run efficiently on resource-constrained IoT devices, enabling advanced AI capabilities at the edge.
Optimizing Neural Networks for Edge Computing in IoT Devices
Qadir, S., & Rodriguez, A. (2023). IEEE Transactions on Mobile Computing, 22(5), 1892-1905.
We propose a new method for optimizing deep neural networks to run efficiently on resource-constrained IoT devices, enabling advanced AI capabilities at the edge.
Secure Communication Protocols for Distributed AI Systems
Chen, L., & Qadir, S. (2023). Computers & Security, 124, 102987.
This work addresses security challenges in distributed AI systems by introducing a new framework for secure communication and data exchange between AI components.
IEEE Journal
[Impact Factor: 8.7]
Secure Communication Protocols for Distributed AI Systems
Chen, L., & Qadir, S. (2023). Computers & Security, 124, 102987.
This work addresses security challenges in distributed AI systems by introducing a new framework for secure communication and data exchange between AI components.
Current Research Projects
Real-time AI Infrastructure for Emergency Response
Funding: National Science Foundation (2023-2026)
Real-time AI Infrastructure for Emergency Response
Funding: National Science Foundation (2023-2026)
Developing a comprehensive AI infrastructure that enables real-time data processing and decision support for emergency response teams during natural disasters and critical incidents.
Ai
Real-time Systems
Secure Distributed Learning Framework
Funding: Department of Defense (2022-2025)
Creating a secure framework for distributed machine learning that protects sensitive data while enabling collaborative model training across multiple organizations.
Security
Machine Learning
Next-Generation Edge AI Systems
Funding: Industry Partnership (2023-2025)
Researching novel architectures and algorithms to enable advanced AI capabilities on edge devices with limited computational resources and power constraints.
Edge Computing
Real-time Systems
Technical Expertise
 Software & Tools
Python, JavaScript, Java, C++
90%
My Skills
95%
Algorithms, Systems Programming, ML
80%
My Skills
95%
Git, Docker, AWS, Kubernetes, Linux
85%
My Skills
95%
Postman, Figma, Jira
90%
My Skills
95%
 Core Competencies
Signal Processing
95%
My Skills
95%
Machine Learning
85%
My Skills
85%
EEG/fMRI Analysis
80%
My Skills
80%
BCI Hardware
80%
My Skills
80%
Research & Academic
 Academic Appointments
Associate Professor
Department of Computer Science, University of Technology (2018 -Â Present)
Teaching graduate and undergraduate courses in AI, distributed systems, and cybersecurity. Leading the AI Systems Research Lab.
Assistant Professor
Department of Computer Science, University of Technology (2014-2018)
Developed curriculum for AI and real-time systems courses. Established research collaborations with industry partners.
Postdoctoral Researcher
AI Research Institute, Stanford University (2012-2014)
Conducted research on distributed AI systems and real-time machine learning algorithms.
 Education
Ph.D. in Computer Science
Massachusetts Institute of Technology (2007-2012)
Dissertation: "Real-time Constraints in Distributed AI Systems"
Ph.D. in Computer Science
M.S. in Computer Science
Dissertation: "Real-time Constraints in Distributed AI Systems"
B.S. in Computer Engineering
Carnegie Mellon University (2001-2005)
Graduated with honors, focus on software systems and algorithms
List of Publications
2024
Real-time neural decoding of speech intent
Nature Neuroscience, IF: 25.0
Rodriguez M, Kim S, Patel J, et al.
DOI
Alt: 892
2023
An open-source platform for neural data analysis
Journal of Neural Engineering, IF: 4.6
Rodriguez M, Kim S, Patel J, et al.
DOI
Code
2024
Real-time neural decoding of speech intent
Journal of Neural Engineering, IF: 4.6
Rodriguez M, Kim S, Patel J, et al.
DOI
Alt: 892
2023
An open-source platform for neural data analysis
Journal of Neural Engineering, IF: 4.6
Rodriguez M, Kim S, Patel J, et al.
DOI
Code
2023
An open-source platform for neural data analysis
Journal of Neural Engineering, IF: 4.6
Rodriguez M, Kim S, Patel J, et al.
DOI
Code
2024
An open-source platform for neural data analysis
Journal of Neural Engineering, IF: 4.6
Rodriguez M, Kim S, Patel J, et al.
DOI
Code
Contact Information
Send a Message
kim@swarthmore.edu
Phone
+1 (555) 123-4567
Office
Computer Science Building, Room 405
University of Technology
Office Hours
Monday & Wednesday: 2:00 PM - 4:00 PM
Friday: 10:00 AM - 12:00 PM
Connect with Me