Edward Kim

Hello, my name is

Edward Kim, Ph.D.

Associate Professor of Computer Science, Drexel University

I build intelligent systems at the intersection of computer vision, neuromorphic computing, and machine learning. I direct the SPARSE Coding Lab at Drexel University.

Explore My Research

About

Ed Kim is an Associate Professor of Computer Science at Drexel University. He received his Ph.D. in Computer Science from Lehigh University in 2013, and his B.S.E. in Computer Science and M.S.E. in Computer Graphics and Game Technology from the University of Pennsylvania.

He is the recipient of an NSF CAREER Award (2019) and the STAR Scholars Outstanding Mentors of the Year Award (2024). His research focuses on developing computational methods inspired by biological neural systems, with applications ranging from medical imaging to adversarial robustness.

He directs the SPARSE Coding Lab at Drexel, where his team investigates sparse representations, neural architectures, and their applications to real-world problems in vision, language, and accessibility.

Alongside his academic work, he brings AI into practice as Chief AI Officer of RespondHealth, a healthcare data science and translational research company. He held the role full-time during a 2025–2026 leave from Drexel and continues as a consultant. He is also an AI advisor to HouseCall VR, a virtual reality patient-education company, and to 12Plus, a Philadelphia college and career readiness nonprofit.

Research Interests

Computer Vision Sparse Coding Neuromorphic Computing Neural Networks Large Language Models Adversarial ML Medical AI Accessibility

Research Highlights

Sparse Coding & Neuromorphic Computing

Developing bio-inspired sparse representations that mimic neural coding strategies, enabling efficient and robust visual processing architectures.

Computer Vision & Adversarial ML

Building robust visual recognition systems resilient to adversarial attacks, with applications in safety-critical domains and autonomous systems.

Medical AI & LLMs

Applying machine learning and large language models to medical imaging, clinical decision support, and healthcare accessibility challenges.

Accessibility & Multimodal Learning

Creating multimodal systems that bridge visual, textual, and auditory modalities to improve accessibility and inclusive technology design.

Industry

2025–present

RespondHealth Chief AI Officer

Leads AI at a data science and translational research company that turns complex healthcare data into real-world evidence for clinical researchers, health systems and life science organizations. Full-time during a 2025–2026 leave from Drexel; now consulting.

2026–present

HouseCall VR AI Advisor

Advises on AI strategy and product design for a patient-education company whose clinician-designed virtual reality modules help patients with diabetes learn and retain key information in short sessions.

2025–present

12Plus AI Advisor

Advises a Philadelphia college and career readiness nonprofit on bringing AI to its work supporting high school students and alumni. The work centers on the 12Plus AI Assistant, which helps staff support more than 6,500 students and alumni across 15 partner high schools in their paths to college and careers.

Recent News

2026

Paper Paper published in Nature Medicine
Paper Papers at ICMLA, AIME, 3DOR
Paper Co-authored textbook Multimodal Communication in Human–Robot Interaction (Springer Nature), forthcoming
Grant New funding from Coulter-Drexel and Anthropic AI for Science
Milestone PhD student graduated: Shrestha

2025

Paper Paper accepted at IEEE BigData 2025
Paper Paper accepted at EMNLP 2025
Paper Paper accepted at AAAI Fall Symposium 2025
Paper Paper accepted at UMAP 2025
Paper Paper accepted at AAAI 2025
Paper Paper published in BMJ Open journal
Milestone Three PhD students graduated: Rego, Barari, Nesbit

2024

Paper Papers at IEEE BigData, ICASSP, WACV, ISBI, SSIAI, IJCAI
Grant Selected for NSF NAIRR Pilot program
Award Received STAR Scholars Outstanding Mentors of the Year Award
Milestone Three PhD students graduated: Hannan, O'Brien, Shakibajahromi

2023

Paper MobilePTX presented at AAAI IAAI
Paper Papers at BMVC, ICASSP, SPIE, AIKE
Paper Journal publications in AI in Medicine and AI and Ethics

Get In Touch

I'm always open to discussing research collaborations, prospective students, or interesting problems in computer vision and machine learning.

Phone: 215.895.0532
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