Hi, I'm Isaac. I'm a graduate researcher in computer science with a background in chemical engineering, working on optimization problems across operations research and quantum computing.
My work focuses on mathematical programming models, including semidefinite programming, polynomial optimization, and combinatorial optimization, and hybrid quantum–classical methods, with applications in energy and process systems. I also have experience with data-driven and machine learning models.
Here are some things I've been working on:
- Exploratory Study of Symmetry in Combinatorial Optimization, exploratory study of symmetry in classical combinatorial optimization problems formulated as integer linear programs, including shortest path, graph coloring, max-cut, and TSP.
- Symmetry-Aware CC Unit Commitment, symmetry-aware MILP formulation of the Combined Cycle Min-Up/Min-Down Unit Commitment Problem, incorporating demand-aware lexicographic ordering to handle structural symmetries.
- SDP-Symresack: Symmetry Handling in SDP, a small exploration of symmetry handling in SDP. Takes the idea behind symresacks (ordering symmetric variables to break symmetry in binary integer programs) and applies it to PSD matrices, using MaxCut on a 3-vertex graph as a worked example.
I'm early in my research career, so the list is still short, but growing. Selected publications are listed below. For a complete list, see the publications section of my homepage.
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A Hybrid Quantum–Classical Machine Learning Framework for Black Carbon Forecasting — Journal Article
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A QUBO-Driven Simulated Annealing Methodology for the Shortest Path Problem in Urban Transportation Networks — Journal Article
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Integrating Quantum Computing into Sustainable Carbon-Capture Materials Research: Opportunities and Perspectives — Book Chapter
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Computer-Aided Design of Intensified Separation Sequences for a Complex Mixture of Renewable Hydrocarbons — Conference Proceeding
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