Increasing recurrence and disruptive forces of extreme climatic events reveal the extent of vulnerability of our built environment and communities. The OASIS lab conducts cutting-edge interdisciplinary research to address the resiliency and sustainability challenges related to our vulnerable communities and socio-technical systems leveraging advanced machine learning algorithms and robust optimization techniques. More specifically, we develop quantitative models to examine systemic/stochastic impacts of various chronic/acute shocks on the interdependent socio-technical systems, develop risk-informed decision models, and investigate cost-effective adaptation measures to advance resilience and sustainability of our communities and critical infrastructure systems (e.g., energy, water, health, etc.).

The mission of OASIS lab is to address the wicked problems and the concurrent issues to investigating the resilience and sustainability of the critical infrastructure systems.

  1. New model could improve energy demand predictions in New York State. To read the news article, click here

  2. Here’s what it will take to adapt the power grid to higher wildfire risks. To read the news article, click here

  3. How will climate change stress the power grid? Hint: Look at dew point temperatures. To read the news article, click here

  • [ESREL 2020] Two papers got accepted at the 30th European Safety and Reliability Conference (ESREL 2020) to be held at Venice Italy, Nov 1-6, 2020 .

  • [SRA 2019] Mukherjee conducted a symposium on “Data-driven risk modeling using predictive analytics approach” and presented a research paper at the Society of Risk Analysis (SRA) General Meeting, Arlington VA, December 8-12, 2019.

  • [SRA 2019 Best Paper Award] Mukherjee & Nateghi’s Risk Analysis paper “A Data-Driven Approach to Assessing Supply Inadequacy Risks Due to Climate-Induced Shifts in Electricity Demand” received Best Paper Award at SRA 2019 General Meeting 

research highlights


In the era of data evolution, when there is an increasing availability of big data along with a growing effort from scientific communities to improve data sharing, the importance of leveraging advanced statistical and machine learning algorithms to uncover trends, patterns and make predictions for different perspectives of the healthcare system considering deep uncertainties of the future cannot be overstated.


The United States energy system is growing vulnerable to the adverse impacts of climatic events that characterized by climate patterns shift such as hot and humid summers, etc.


Extreme events, for example, climatic change, natural disasters or health hazards such as pandemic is affecting the life of people in far-reaching ways across regions, impacting a multitude of infrastructure sectors such as energy, public health, ecosystems, water supply and the communities.