Dr. Daehan Kwak, Ph.D.
Associate Professor of Computer Science
Graduate Program Coordinator
Department of Computer Science and Technology, Kean University
Biography
I am an Associate Professor in the Department of Computer Science and Technology at Kean University, Union, NJ, USA. I received the Ph.D. degree in Computer Science from Rutgers, The State University of New Jersey, New Brunswick, NJ, USA, in 2017, and the M.S. degree from the Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea, in 2008.
I was a member of the Laboratory for Networked Systems and Security (Disco Lab) and my research advisers are the late Dr. Liviu Iftode and Dr. Badri Nath. I was with the Telematics and USN Research Division, Electronics and Telecommunications Research Institute (ETRI), as a research intern during my master's program. I worked as a research staff at the UWB Wireless Research Center, Inha University, and Network Media Management and Optimization Lab, Yonsei University.
My research is focused on applied AI/ML and intelligent system design for data-driven Smart Systems, integrating advances in computing, networking, and data analytics to enable innovative, interdisciplinary applications such as smart transportation, smart health, and smart urban environments.
I currently serve as Graduate Program Coordinator for the MS and PhD in Computer Science, and Chair of the Department Curriculum Committee at Kean University. I am also a Fellow at the Faculty Futures AI Studio of the New Jersey AI Hub (Princeton, NJ).
Research Interests
My research spans applied AI/ML, computing, and networking, developing intelligent, data-driven systems for real-world applications in transportation, healthcare, and urban environments.
Multimodal AI and data fusion; deep reinforcement learning; federated and privacy-preserving learning; LLMs and knowledge graphs; affective computing; security/anomaly detection.
IoT for healthcare; EHR summarization and risk assessment; LLM-augmented knowledge graphs; mental health analytics via NLP; conversational AI for care management.
Social vehicle navigation; traffic sentiment analysis; connected vehicular systems; balanced routing; counterfactual travel time estimation; vehicular cloud computing.
Vehicular, mobile, and pervasive computing; urban computing; vehicular cloud computing; distributed computing; edge computing and task offloading.
MAC/Transport protocols; vehicular networks; social networks; sensor networks; body area networks; cooperative relay networks; handoff; routing.
Privacy-preserving federated learning; IoMT security; cybersecurity binary code analysis; deepfake detection; smart device vulnerability assessment.
Research Grants
Over $8.2 million in external and internal research funding secured as PI or Co-PI.
Selected Publications
A selection of peer-reviewed journal articles and conference papers. View all on Google Scholar โ
Honors & Awards
Recent News
Teaching Experience
Courses taught at Kean University's Department of Computer Science and Technology since Fall 2017.
Core introductory course covering programming fundamentals, data structures, and algorithmic thinking.
Foundational course exploring computing systems, hardware components, and basic system architecture.
Hands-on course in mobile app design and development for modern platforms and user experiences.
Advanced course covering network protocols, architectures, security principles, and distributed systems.
Capstone research course guiding students through independent research projects in computer science.
Individualized research and study on specialized topics in CS, tailored to student interests.
Student Mentoring & Awards
Dedicated to mentoring undergraduate and graduate students in research, with many students earning national recognition and awards.
May 2025
May 2025
January 2025
Graduate category, November 2024
Graduate category, November 2024
April 2024
Undergraduate, October 2023
Undergraduate, October 2022
Professional Service
Department of Computer Science and Technology
1000 Morris Ave, Union, NJ 07083