Featured Speakers: Bill Cannan, Nicolette Dunston
Date: Tuesday, August 4, 2026
Time: 10 a.m. – 11 a.m.
Where: In Person: 50A-5132. Virtual: Zoom Available.
Past Behavior is the best predictor of future performance! Behavioral-based interviewing is a competency-based interviewing technique in which employers evaluate a candidate’s past behavior in different situations in order to predict their future performance. This technique is the new norm for academic and industry-based organizations searching for talent. This workshop will provide information and tools to help you prepare for your next interview including an overview of the behavioral-based interview process, sample questions, and techniques on how to prepare..
Workshop presented by Bill Cannan (Senior HR Partner to CS Area) and Nicolette Dunston (HR Partner to CS Area).
Featured Speakers: Stefan Wild
Date: Tuesday, August 4, 2026
Time: 10 a.m. – 11 a.m.
Where: In Person: 50A-5132. Virtual: Zoom Available.
Stefan Wild is the Director of the Applied Mathematics and Computational Research (AMCR) Division in the Computing Sciences Area at Lawrence Berkeley National Laboratory (Berkeley Lab). AMCR conducts research and development in mathematical modeling, simulation and analysis, algorithm design, computer system architecture, and high-performance software implementation.
Bill Cannan is the Sr. HR Division Partner that supports Computing Sciences and IT. Bill has over 20 years of HR related experience as a recruiter and HR Generalist in both industry and National Lab environments. Bill is responsible for providing both strategic and hands-on full cycle Human Resources support and consultation to employees and managers.
Nicolette started working at the Lab in 2019 in the Talent Acquisition Team as a Recruitment Coordinator. She joined the HR Field team in 2021 supporting the Computing Sciences and IT Divisions.
Stefan Wild leads the Applied Mathematics and Computational Research Division at Lawrence Berkeley National Laboratory. He also develops numerical optimization and automated learning algorithms for solving difficult science and engineering problems.
