Research Portfolio
My research develops mathematical models and computational algorithms for decision-making under uncertainty. My particular interests are stochastic optimization, chance-constrained programming, fairness-aware methods, and large-scale discrete optimization. Two themes shape much of my work:
- Optimization under uncertainty: how to make high-stakes decisions when future information is imperfect?
- Foundations of fairness: how to characterize fairness axiomatically through a model's optimality conditions?
My research is grounded in mathematical theory but always motivated by real-world challenges. Key application areas include public-health preparedness, infectious disease outbreaks, energy-system planning, sustainability, and resource allocation (details below). Over the past 15 years, I have collaborated with governments, public agencies, and research institutes across the US, Europe, and Asia.
Selected funding agencies and partners:
Optimization Under Uncertainty
My specific focus within stochastic optimization is chance-constrained programming, where one must satisfy guaranteed reliability requirements despite uncertain future outcomes. This framework provides a mathematically rigorous way for balancing risk and performance, but the models are often computationally challenging to solve. My work investigates approximations of joint chance constraints. [Click for more →]
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Sustainability, Energy Systems & Climate Change
Global transition towards sustainable energy systems presents major optimization challenges. Renewable energy sources (e.g., wind and solar) introduce volatility, while infrastructure planning requires highly reliable operation. My research develops large-scale mathematical models and scalable algorithms for planning & operating energy systems under uncertainty. Particularly, I use chance-constrained optimization with Lagrangian-based approaches and machine-learning-inspired iterative algorithms. [Click for more →]
Fairness & Waste Management
In 2020, I began exploring undesirable facility location problems (FLPs) with a unique perspective: ensuring fairness from the point of view of the facilities, rather than the users, which is the typical approach in existing literature. Motivated by applications in waste management, particularly in the context of recycling centers (e.g., UK tips or German Wertstoffhöfe), I lead a diverse team of researchers on this subtopic.
Supported by the Bavarian State Ministry for Science & Arts and the University of Southampton, my team develops discrete optimization models that achieve fairness in facility closures. So far this effort has led to:
- Three student theses with three published articles.
- Grant funding supporting a postdoctoral researcher to quantify subjective opinions on recycling campaigns and incorporate human perceptions into decision models. [Click for more →]
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Pandemic Risk Mitigation
I began collaborating with the Texas Department of State Health Services, US, as an MSc student in 2012, helping prepare for future pandemics long before the emergence of COVID-19. Motivated by Texas's response to the 2009 H1N1 pandemic, my PhD research focused on designing web-based, optimization-backed decision-support tools for government use. These tools, accessible at flu.tacc.utexas.edu, assist the State of Texas in the fair and efficient allocation of critical resources, such as antivirals and vaccines.
The most significant funding here is provided by a three-year grant from the Deutsche Forschungsgemeinschaft (German Research Foundation), with additional funding from the Bavarian Czech Academic Agency and the EU Horizon 2020 Program. [Click for more →]
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