Our society today faces ever-changing challenges, e.g., climate change, digitization and pandemics, which require novel and sustainable solutions. The focus of my research is on improving the resilience of critical systems such as the healthcare sector by using quantitative and data-driven methods from business analytics. Stochastic programming as well as machine learning and risk measurement are particularly valuable in this regard to strengthen humanitarian and healthcare logistics against disruptive events such as natural disasters and cyber-attacks. My research is dedicated to applying these approaches to mitigate the impact of negative events on critical infrastructures and increase their resilience level.
Grass, E., Ortmann, J., Balcik, B., and W. Rei (2023): A Machine Learning Approach to deal with Ambiguity in the Humanitarian Decision Making. Production and Operations Management 32 (9), 2956-2974.
O'Brien, N; Grass, E; Martin, G; Durkin, M; Darzi, A; Ghafur, S (2021): „Developing a globally applicable cybersecurity framework for healthcare: a Delphi consensus study“, BMJ Innovations.
Grass, E; Fischer, K (2020): „Case Study Design for Short-Term Predictable Disasters“, Journal of Humanitarian Logistics and Supply Chain Management 10 (3), 391–419.
Grass, E; Fischer, K; Rams, A (2020): „An Accelerated L-Shaped Method for Solving Two-Stage Stochastic Programs in Disaster Management“, Annals of Operations Research 284 (2), 557–582.
Ghafur, S; Grass, E; Jennings, N; Darzi, A (2019a): „The challenges of cybersecurity in health care: the UK National Health Service as a case study“, The Lancet Digital Health 1 (1), 10–12.
Grass, E; Fischer, K (2016): „Two-Stage Stochastic Programming in Disaster Management: A Literature Survey“, Surveys in Operations Research and Management Science 21 (2), 85–100.
Grass, E; Pagel, C; Crowe, S; Ghafur, S: „A Stochastic Optimisation Model to Support Cybersecurity within the UK National Health Service“, in preparation.
Grass, E; Ortmann, J; Balcik, B; Rei, W: „A Machine Learning Approach to deal with Information Ambiguity in Humanitarian Decision Making“, in preparation.
Graß, E. and R. Stolletz (2023): The Resilient Newsvendor: Hospital Surge Capacity Planning, GOR 2023, Hamburg, Germany, August 2023.
Graß, E. (2022): A Novel Framework to deal with Ambiguity in the Humanitarian Decision Making. GOR, Karlsruhe, Germany, September 2022.
Graß, E. (2022): Verbesserung der Cybersicherheit im Gesundheitswesen. Health Care Management. Straubing, Germany, February 2022.
Graß, E. (2021): Improving Cybersecurity in Healthcare. GOR. Digital, September 2021.
Graß, E. (2021): Cybergeddon in Healthcare: Preparing for the Worst. WORAN. Digital, July 2021.
Graß, E. (2021): Improving Cybersecurity in Healthcare (Session Chair). EURO. Digital, July 2021.
Graß, E. (2020): Measuring and Minimizing Cyber Risks in the NHS. Digital Trust & Security in Healthcare Follow-Up Conference, Digital, July 2020.
Graß, E. (2018): The Efficient L-Shaped Method for Large-Scale Problems. Scientific Commission on Logistics. Düsseldorf, Germany, July 2018.
Graß, E. (2017): The Efficient L-Shaped Method, ISCRAM2017: International Conference on Information Systems for Crisis Response and Management. Albi, France. May 2017.
Graß, E. (2017): Developing an Efficient Solution Method for Problems in Disaster Management. Humanitarian Logistics Meeting. Dresden, Germany. April 2017.
Graß, E. (2016): Facility Location Planning under Uncertainty in Disaster Management. Nashville, US. November 2016.
Graß, E. (2016): Pre-Positioning of Relief Items in Preparation for Disasters. OR2016: International Conference on Operations Research. Hamburg, Germany. September 2016.
Graß, E. (2015): Pre-Positioning Relief Items under Uncertainty. Disaster Management in Urban Areas. Duisburg, Germany. November 2015.
Graß, E. (2015): Classification of Modelling and Solution Approaches for Disaster Management. Logistics Management Conference. Braunschweig, Germany. September 2015.
Stiftungspreis der Volksbank Hamburg für die beste Dissertation 2018
Outstanding Reviewer Award der Zeitschrift OR Spectrum 2019
Stochastic Programming Society
Gesellschaft für Operations Research e.V (GOR-Arbeitsgruppe: Praxis der Mathematischen Optimierung; Health Care Management)
Institute for Operations Research and Management Science
Information Systems for Crisis Response and Management Association
Doctoral Studies, Institute for Operations Research and Information Systems, Hamburg University of Technology
Postdoctoral Researcher, Chair of Production Management (Prof. Stolletz), University of Mannheim
Research Fellow, Imperial College London, United Kingdom
Research Assistant, Institute for Operations Research and Information Systems, Hamburg University of Technology
Postgraduate Demonstrator, School of Mathematics, University of Birmingham, United Kingdom
Graduate Assistant, Institute for Operations Research, Helmut Schmidt University
Student Assistant, Department of Statistics and Econometrics, University of Hamburg