School of Engineering

David V. Rosowsky
Professor
Dean of Engineering

Department Affiliation:

School of Engineering
Department of Civil & Environmental Engineering

Education:

Ph.D., Johns Hopkins University
Civil Engineering

M.S., Tufts University
Civil Engineering

B.S., Tufts University
Civil Engineering

Background:

Texas A&M University, Department Head, A.P. and Florence Wiley Chair, Zachry Department of Civil Engineering, 2004-2009

Oregon State University, Professor and Richardson Chair (Inaugrual Holder), Department of Civil Engineering, Department of Wood Science & Engineering, 2000-2004

Clemson University, Associate Professor, Department of Civil Engineering, 1997-2000

Clemson University, Assistant Professor, Department of Civil Engineering, 1994-1997

Johns Hopkins University, Visiting Scholar, Department of Civil Engineering, 1993-1994

Purdue University, Assistant Professor, School of Civil Engineering, 1990-1993

Research Interests:

Since 1990, Dr. Rosowsky has conducted research in the areas of structural reliability, performance of wood structural systems, design for natural hazards, stochastic modeling of structural and environmental loads, and probability-based codified design. He has taught classes in probability and statistics, structural engineering (analysis and design), and mechanics. Dr. Rosowsky serves on a number of technical committees including the ASCE Committee on Wood and the ASCE Committee on Performance of Structures. He also currently serves on the Editorial Board of Structural Safety and is a past editorial board member of the ASCE Journal of Structural Engineering, ASCE Journal of Infrastructure Systems, and Natural Hazards Review. His current research addresses three topics: (1) behavior of the built environment subject to natural hazards, (2) modeling and analysis of load effects on buildings and other structures, with particular emphasis on complex environmental phenomena, and (3) performance-based engineering for design, post-disaster condition assessment, and loss estimation studies.