HYEONG SUK NA
Assistant Professor
Department of Industrial and Systems Engineering
University of Missouri
HYEONG SUK NA, PH.D.
("Hyeong Suk" is my first name)
Assistant Professor of Industrial and Systems Engineering
Director of the Smart, Sustainable, and Resilient Systems (SSRS) Laboratory
University of Missouri, Columbia, MO 65211
Office: E3437B Thomas & Nell Lafferre Hall | Phone: (573) 882-9185
Mizzou | Email | LinkdeIn | Google Scholar | ORCiD
RECENT NEWS
01/2025: Muhammad Zaheer Sajid and Zahra Sobhani (Ph.D. students) have joined our research group. Welcome!
12/2024: Dr. Na has received funding from the Jungseok Logistics Foundation (JLF) for a research grant focusing on Integrated Deep Reinforcement Learning and Large-Scale Optimization for Enhancing Last-Mile Logistics with Future Mobility Technologies.
11/2024: Michael Geisecke (Ph.D. student) has joined our research group. Welcome!
10/2024: Shivam's research presentation "Post-Disaster Damage Assessment from High-Resolution Satellite Imagery using D-LinkNet with Hierarchical Transformer Difference Blocks " has been accepted by the 2025 NHERI Computational Symposium. Congratulations Shivam Shrivastava on receiving the travel fund from DesignSafe at UT Austin and the NHERI SimCenter at UC Berkeley through support from the National Science Foundation (NSF)!
09/2024: Dr. Na has received the Bloss Faculty Enhancement Grant from the Department of Industrial and Systems Engineering at the University of Missouri. The funds for this grant come from an endowment that an alumnus of the ISE Department – Bob Bloss – explicitly created to support early-career ISE Faculty in establishing their research careers.
09/2024: Dr. Na has received funding from the Department of Homeland Security for a research grant focusing on Enhancing AIT-based Passenger Screening Processes and Security Through Integrated Stochastic Optimization and Artificial Intelligence Strategies.
OPEN POSITIONS [Deadline: January 15, 2025]
We are currently looking for two highly motivated Ph.D. students to join our SSRS Lab in the Department of Industrial and Systems Engineering at the University of Missouri, starting as early as Fall 2025. The research focus will be on:
Large-Scale Stochastic Optimization
Artificial Intelligence/Machine Learning (with a particular emphasis on computer vision and image processing)
The positions are fully funded through a combination of research and teaching assistantships, which include full tuition support, a stipend, and benefits. The assistantship is for 12 months with the possibility of renewal based on satisfactory performance.
Qualifications:
M.S. in Industrial and Systems Engineering, Operations Research, Computer Science/Engineering, Transportation Engineering, Statistics, Mathematics, or related fields.
Experience in writing and presenting academic research, such as peer-reviewed journal/conference papers or an M.S. thesis.
Strong programming skills (e.g., Python, CPLEX, GAMS, AMPL, R, MATLAB).
Proficiency with deep learning frameworks like TensorFlow and PyTorch.
Solid understanding of image processing, computer vision, deep learning, and optimization concepts.
Prior research experience in computer vision and image processing using deep learning is highly desirable.
Candidates must also apply to the University of Missouri to be considered prospective members of the SSRS Lab. If you are interested, please email (1) a one-page cover letter highlighting your research interests and experience and (2) a CV with three reference contacts to Dr. Hyeong Suk Na (hyeongsuk.na@missouri.edu). Review of applicants will begin immediately and continue until the positions are filled.
Graduate Education at Mizzou
Graduate Admissions | Ph.D. in Industrial Engineering | M.S. in Industrial Engineering
Those from traditionally underrepresented groups in STEM are particularly encouraged to get in touch with Dr. Hyeong Suk Na. The University of Missouri is an affirmative action/equal opportunity employer and does not discriminate on the bases of age, color, disability, gender, gender identity, genetic information, marital status, national or ethnic origin, race, religion, sexual orientation, or veteran status.