Melis has completed her B.Sc and M.Sc degrees in the department of Chemical Engineering of Bogazici University, Istanbul, Turkey with honors in 2012, and 2014 respectively. She has worked with Professor Turkan Haliloglu and Professor Ruth Nussinov during her M.Sc studies where she has focused on understanding protein-protein interactions via molecular dynamics simulations, and machine learning techniques. Melis was a visiting researcher in the National Cancer Institute in the summer of 2013 as part of her M.Sc degree. She joined Texas A&M University in January 2015 for her PhD studies. Her PhD research focuses on solving challenges related to energy, environment, and healthcare via big data analytics, machine learning and optimization. Specifically, she works on effective dimensionality reduction/feature selection algorithms based on Support Vector Machines and optimization of multi-scale systems so as to create valuable insights from big data. As part of this effort, she is developing a data-driven framework for fault detection and diagnosis that aims to deliver an online decision support tool for process monitoring, control, and optimization.
- DOMINO: Data-driven Optimization of bi-level Mixed-Integer NOnlinear Problems. Journal of Global Optimization 2020, 78 (1), 1-36.
- Integrated Data-Driven Process Monitoring and Explicit Fault-Tolerant Multiparametric Control. Industrial & Engineering Chemistry Research 2020, 59, 2291-2306.
- A Nonlinear Support Vector Machine based Feature Selection Approach for Fault Detection and Diagnosis: Application to the Tennessee Eastman Process. AIChE Journal 2019, 65, 992-1005.
- 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.
- Big Data Approach to Batch Process Monitoring: Simultaneous Fault Detection and Identification Using Nonlinear Support Vector Machine-based Feature Selection. Computers & Chemical Engineering 2018, 115, 46-63.
- Simultaneous Fault Detection and Diagnosis of Continuous Processes via Nonlinear Support Vector Machine-based Feature Selection. 13th International Symposium on Process Systems Engineering (PSE 2018); Elsevier, 2018; pp 2077-2082.
- 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.
- Computational Tools in the Assistance of Personalized Healthcare. In Quantitative Systems Pharmacology: Models and Model-Based Systems with Applications; Manca, D., Ed.; Computer Aided Chemical Engineering, Vol. 42; Elsevier, 2018.