Faculty Directory

Bensi, Michelle (Shelby)

Bensi, Michelle (Shelby)

The Deborah J. Goodings Professor in Engineering for Global Sustainability
Associate Professor
Civil and Environmental Engineering
Center for Risk and Reliability
1170 Glenn L. Martin Hall

OFFICE HOURS (Fall 2021)

  • Wednesday, 9-10am (in-person)
  • Wednesday, 3-4pm (in-person)
  • By appointment


  • Ph.D., University of California, Berkeley
  • M.A.Sc., University of Delaware
  • B.A., University of Toledo  


  • Probabilistic assessment of natural and human-induced hazards
  • Probabilistic risk assessment and risk-informed applications
  • Risk assessment of infrastructure systems
  • Structural reliability and systems modeling
  • Application of machine learning to civil engineering problems
  • Bayesian networks and other applications of Bayesian methods
  • Probabilistic risk assessment for nuclear power facilities and other infrastructure

ENCE 302: Probability and Statistics for Civil and Environmental Engineers

Statistics is the science of data. Civil Engineers must often make decisions based on incomplete, variable or uncertain information. In addition, modern methods of design and analysis need to account for variability in natural, engineered and human systems. After successful completion of this class, a student should have facility and familiarity with established basic techniques for managing data, modeling variability and uncertainty, communicating about data and decisions, and supporting or defending a decision or judgment based on uncertain or incomplete data.

ENCE 633/433: Assessment of Natural Hazards for Engineering Applications

Ensuring the resilience of infrastructure and other engineered systems requires an assessment of the natural hazards to which the systems are exposed. Probabilistic natural hazard assessment evaluates how likely a location is to experience hazard events (e.g., hurricanes or earthquakes) and how likely those events are to cause various impacts (e.g., large surges, intense rainfall, high winds, or ground shaking). This course will review the basic science of natural hazards and provide the foundational concepts of probability and statistics required for developing models to assess the frequency and severity of natural hazards. This course will present methodologies for assessment of multiple types of natural hazards (e.g., seismic, precipitation, riverine, coastal, and wind hazards).

Center for Risk and Reliability Fosters Collaborative Environment

The Center for Risk and Reliability renovated their space to enhance multidisciplinary collaboration.

Assessing Nuclear Risk: CEE, ME Faculty Collaborate on New Tools

Research aims to explore updates to risk assessment tools and methods used by the nuclear industry.

Four Clark School Faculty Receive CAREER Awards

Shelby Bensi, Gregg Duncan, Katrina Groth and Katharina Maisel are recipients of NSF CAREER grants.

NSF CAREER Award Winner Bensi Studies Multi-Hazard Events

CEE faculty member aims to close risk assessment gap.

Search Initiated for New Clark School Dean

A national search has been initiated for a new Dean of the A. James Clark School of Engineering.

Assessing Natural Hazard Risks to Nuclear Facilities

The DOE has awarded nearly $800,000 to a team led by assistant professor Michelle (Shelby) Bensi.

ENCE Course on Probabilistic Hazard Assessment will be Taught this Fall

Prof. Michelle Bensi will be teaching ENCE 433/633 this Fall.

Student Spotlight: Azin Al Kajbaf

Meet Azin, who performs assessments that help identify and quantify the risk of disasters.

Student Spotlight: Somayeh Mohammadi

Meet Somayeh, who considers diaster resilience as the most critical discipline in civil engineering. 

Azin Al Kajbaf Nominated for Outstanding Graduate Assistant Award

Azin is among the top 2% of campus GAs for 2019-2020.

Student Spotlight: Jonathan DeJesus Segarra

Meet Jonathan, who believes engineering is a way to apply life-long learning. 

Bensi, Groth Selected for Nuclear Facility Risk Assessment Faculty Development Program

The inter-department program will support junior faculty in developing solutions to advance probabilistic risk assessments.