|Postdoctoral Research Associatefirstname.lastname@example.org|
Dr. Burcu Beykal is a Postdoctoral Research Associate at the Texas A&M Energy Institute. She holds a B.S. degree in Chemical and Biological Engineering from Koc University, an M.S. degree in Chemical Engineering from Carnegie Mellon University, and a Ph.D. degree in Chemical Engineering from Texas A&M University. Her research focuses on data analytics and data-driven optimization for chemical, environmental, and biological systems.
- "Mixed-integer Linear Multi-objective Optimization through Multiparametric Programming".
- Bi-level Mixed-Integer Data-Driven Optimization of Integrated Planning and Scheduling Problems. 31st European Symposium on Computer Aided Process Engineering (ESCAPE-31); 2021.
- Predicting the Estrogen Receptor Activity of Environmental Chemicals by Single-Cell Image Analysis and Data-driven Modeling. 31st European Symposium on Computer Aided Process Engineering (ESCAPE-31); 2021.
- "A Combination of Experimental Isotherms, Minimalistic Simulations and Models for Understanding and Predicting Chemical Adsorption onto Montmorillonite Clays".
- DOMINO: Data-driven Optimization of bi-level Mixed-Integer NOnlinear Problems. Journal of Global Optimization 2020, 78 (1), 1-36.
- A Data-Driven Optimization Algorithm for Differential Algebraic Equations with Numerical Infeasibilities. AIChE Journal 2020, 66 (10), e16657.
- Classification of estrogenic compounds by coupling high content analysis and machine learning algorithms. PLOS Computational Biology 2020, 16 (9), e1008191.
- Integrated Modeling of Transfer Learning and Intelligent Heuristic Optimization for Steam Cracking Process. Industrial & Engineering Chemistry Research 2020, 59, 16357-16367.
- Grouping of Complex Substances Using Analytical Chemistry Data: A Framework for Quantitative Evaluation and Visualization. PLoS ONE 2019, 14, e0223517.
- Development of the Texas A&M Superfund Research Program Computational Platform for Data Integration, Visualization, and Analysis. 29th European Symposium on Computer-Aided Process Engineering (ESCAPE-29); Elsevier, 2019; pp 967-972.
- Optimal Design of Energy Systems Using Constrained Grey-Box Multi-Objective Optimization. Computers & Chemical Engineering 2018, 116, 488-502.
- Optimal Chemical Grouping and Sorbent Material Design by Data Analysis, Modeling and Dimensionality Reduction Techniques. 28th European Symposium on Computer-Aided Process Engineering (ESCAPE-28); Elsevier, 2018; pp 421-426.
- A hierarchical Food-Energy-Water Nexus (FEW-N) Decision-making Approach for Land Use Optimization. 13th International Symposium on Process Systems Engineering (PSE 2018); Elsevier, 2018; pp 1885-1890.