Dr Steven Mell

Biography

I am an early-career, orthopaedic biomechanics researcher focused on mechanistic, computational approaches that bridge patient motion, tissue/material behavior, and implant/native joint function and performance. My work builds reproducible pipelines that combine subject-specific finite-element analysis, probabilistic experimental design, and machine-learning models to predict joint contact, wear, and failure mechanisms from motion and imaging inputs. I hold faculty appointments at Rush University Medical Center (Department of Orthopedic Surgery) and as adjunct faculty in biomedical engineering at the University of Illinois at Chicago. Research Goals: I design and validate multiscale computational workflows — including parametric FEA, material model calibration, and virtual clinical trials — to assess how surgical decisions, implant geometry, and patient gait interact to produce clinically meaningful outcomes such as contact pressure, polyethylene wear, and joint degeneration. A second thrust of my program is integrating deep learning and generative methods to accelerate simulation-driven inference (e.g., motion → contact predictions), enable realistic synthetic datasets for motion analysis, and make virtual trials tractable for clinical translation. Selected accomplishments & collaborative focus I have led computational studies and collaborative experimental efforts on total knee replacement wear, patellofemoral contact mechanics, the mechanics of modular taper junctions, and cartilage material characterization. My work has been recognized with awards including the Orthopaedic Research Society New Investigator Recognition Award and institutional fellowships, and has resulted in peer-reviewed publications in biomechanics and orthopaedic journals. I actively pursue multidisciplinary collaborations with clinician–scientists, materials scientists, and data scientists to ensure models address clinically relevant questions and are validated against experimental and in-vivo data.