Faculty & Staff

Goksu Avdan, Ph.D.
Assistant Professor

Office: Wilson Hall 191
Email: gavdan@missouriwestern.edu
Phone: 816-271-4562

Curriculum Vitae

Dr. Goksu Avdan is a Tenure-Track Assistant Professor in the Department of Engineering at MoWest. He earned his Ph.D. in Engineering Science (Industrial and Quality Engineering) from Southern Illinois University – Carbondale, and holds M.S. and B.S. degrees in Industrial Engineering from Southern Illinois University – Edwardsville and Istanbul Technical University. His research focuses on biomechanics, biomedical signal processing, wearable sensors and artificial intelligence to enhance ergonomics, human-robot collaboration, and healthcare systems.

He has published in journals such as Neural Computing and Applications, Biomedical Signal Processing and Control, and the Journal of Medical and Biological Engineering; as well as serving as a reviewer for high-impact outlets including npj Digital Medicine (Nature), Artificial Intelligence Review (Springer), and Scientific Reports (Nature).

Dr. Avdan has taught a wide range of courses with strong student evaluations, including Lean Production Systems, Engineering Materials, Dynamics, Computer Integrated Manufacturing, CNC Machining Processes, Manufacturing Methods, Methods Design & Work Measurements, Technical Report Writing, and Programmable Logic Controllers. He is a member of IEEE and IEEE Engineering in Medicine & Biology Society.

Dr. Goksu Avdan supported the ABET accreditation efforts for the Construction Engineering Technology program, and the ABET visiting team’s initial feedback during the site visit was very positive. He further strengthened the program by enabling closed-loop continuous improvement efforts inspired by the PDCA (Plan-Do-Check-Act) framework. In parallel with these efforts, during Fall 2025, two student-led posters were presented through applied collaborations at the Multidisciplinary Research Day at Missouri Western State University. In an industry collaboration with Altec Industries, his student Haydn Richardson presented “Ergonomic Design and Validation of a Foldable Ladder for AOKI DM45/47 Operator Access,” and this work has also been accepted for presentation at the National Conference of Undergraduate Research (NCUR) 2026. In an interdisciplinary collaboration with the Department of Health Professions (Dr. Justin Kraft and Dr. Maureen Holtz), his student Zachary Catron presented “Effects of a Passive Neck Exoskeleton on NASA TLX, Perceived Fatigue, and Neck Pain During Milling and Lathe Operations.” In addition, he submitted a conference paper titled “Ergonomics Sensing in Industry 5.0 Human-Robot Collaboration: Current Trends and Future Directions” to the 11th North American International Conference on Industrial Engineering and Operations Management. Also, during 2025, he published and disseminated research aligned with our commitment to applied, high-impact scholarship. His journal article, “IMU-Trans: Imputing Missing Motion Capture Data with Unsupervised Transformers,” was accepted in Neural Computing and Applications (Impact Factor: 4.5). He also published “Evaluating Worker Performance and Ergonomic Risks Using EMG and Kinematics: A MIMO Approach” in the Journal of Applied Research on Industrial Engineering in collaboration with Dr. Sohyung Cho (Kennesaw State University) and his graduate student. Additionally, he co-authored a conference proceedings paper with his student Kolby Scott Sweiger, “Bio-Based Neck Orthosis Design: Structural Comparison of Circular, Elliptical, and Honeycomb Patterns Using Finite Element Analysis,” which was presented at the 2025 IEEE 8th International Conference on Electrical, Electronics, and System Engineering (ICEESE). He also pursued external funding aligned with School and University goals by submitting an NSF Engineering Research Initiation (ERI) proposal, “ERI: Real-Time Multimodal Ergonomic Risk Prediction and Robotic Adaptation for Safety-Aware Human-Robot Collaboration,” to the ENG/CMMI M3X program; the proposal is currently pending. This project uses EMG and plantar pressure sensing to predict REBA scores during human-robot collaboration and enable real-time robot adaptation to reduce ergonomic risk, with a requested budget of $199,239.